Author

Manas Saha

Published

8 October 2026

Estimated Read time

10Mins

Manas Saha, Head of Financial Services UK at Publicis Sapient, on why firms that build client context, adviser judgement, past decisions and compliance outcomes – held in a form that both people and AI agents can use – will compound their advantage. Firms that don’t will be renting the same intelligence as everyone else

For decades, wealth management has operated under a simple equation in which growth required proportionally more advisers, more operational capacity and more cost, so as assets under management increased, firms scaled through people, processes and organisational complexity. Artificial intelligence is now breaking that equation, and the speed at which it is happening has caught much of the industry off guard.

Earlier this year, the launch of Hazel, an AI-driven tax-planning tool from Altruist, triggered billions in stock selloffs across listed wealth managers as markets began to question whether the traditional fee model can survive in a world of near-zero-cost analytics. Whatever one makes of the scale of that reaction, it revealed something important about how investors now view the sector, because they are already repricing wealth firms for AI risk before most of those firms have worked out what AI will change inside their own operations.

The model will not be the moat

The harder truth behind that repricing is that the models themselves are heading towards commodity status. Within a couple of years every wealth manager will have access to a powerful foundation model, bought from the same small group of providers on broadly similar terms, which means the lasting advantage will sit in the layer each firm builds on top of it, made up of client context, adviser judgement, past decisions and compliance outcomes held in a form that both people and AI agents can use. Firms that build that layer will compound their advantage with every client interaction, while those that neglect it will find themselves renting the same intelligence as everyone else.

Scarcity still shapes the business

Traditional wealth firms have long operated under scarcity, with scarce expertise, scarce adviser capacity and scarce client touchpoints shaping how the business runs. Clients now expect 24/7 service and hyper-personalisation, while advisers are already overloaded with back-office work that leaves them less time for the client conversations that create value. Most firms continue to rely on workflows built around manual reviews, disconnected systems and information that sits across CRM platforms, portfolio management tools, planning software, emails and adviser notes, so important client context is often fragmented across the organisation rather than available where decisions are made. Advisers spend valuable time gathering information instead of interpreting it, reviews happen according to calendars rather than real-world conditions, and work moves between planning, portfolio management, compliance and operations through error-prone manual handoffs.

Adviser time is the lever

With demand growing and adviser supply dwindling, productivity has become a strategic lever, and if AI can free up even 30 to 50 per cent of adviser time, a firm can channel that capacity into new clients, deeper relationships or significant cost savings. Capturing that value requires AI to become more than another productivity tool bolted onto existing processes, because the first generation of AI, which focused on summarising meetings, drafting communications and automating routine tasks, left the underlying operating model largely untouched.

Three stages of autonomy

The next stage embeds AI much more deeply into business processes through AI agents that coordinate data sources, prepare recommendations and manage multi-step workflows under human supervision and guidance. For a regulated firm the question that matters most is where the boundaries sit, and in practice it helps to think in three stages.

In assistive automation, the adviser initiates the task, checks the output and remains fully responsible for the decision, with AI supporting discrete work such as summarising, extracting data or drafting content under basic usage policies and human review. With guided orchestration, AI coordinates across systems, proposes actions and prepares fuller outputs such as follow-ups or scenario analysis, while a person supervises the workflow, approves recommendations and manages exceptions within defined permissions, approval points and audit trails. A fully AI-orchestrated workflows, agents execute multi-step processes within agreed rules, monitoring portfolios, triggering actions and routing exceptions, while people set the boundaries, monitor outcomes and intervene wherever judgement is required.

Governance from the first day

That third stage is where governance becomes most demanding, and UK firms already have a framework that should shape their design from the very first day. Under the Senior Managers and Certification Regime, accountability for any activity that AI supports, recommends or executes remains with the firm and its senior managers, so every agentic workflow needs a named owner, clear audit trails, escalation routes, outcome testing, explainability, bias monitoring and board-level oversight capable of showing that AI is supporting better decisions and better client outcomes rather than simply faster processes. Governance designed this way becomes part of the operating model itself, and the ability to preserve context, explain recommendations, trace decisions and coordinate work within clearly defined boundaries is precisely what allows AI to operate safely at scale across regulated workflows.

The Intelligent Wealth Firm

In our recent white paper with PIMFA, we describe the destination as the Intelligent Wealth Firm, a continuously learning organisation in which human expertise and AI capabilities operate as an integrated system. A single client interaction may be informed by hundreds of AI-driven activities spanning research, planning, compliance, servicing and portfolio management, and every recommendation, decision and outcome feeds back into the firm’s memory, making the organisation progressively smarter over time. That memory captures historical recommendations, adviser actions, client interactions, compliance outcomes and business results, and over the years this experience becomes institutional knowledge that improves future decisions and creates an advantage that competitors, technology vendors and foundation model providers cannot easily replicate.

Who is already moving

The largest institutions are not waiting. More than 98 per cent of Morgan Stanley’s adviser teams use its internal AI assistant, and its Debrief tool, used with client consent, summarises client meetings, drafts follow-ups and logs notes into the CRM. UBS is using agents to send advisers thousands of alerts a day and to execute trades or transfer funds once an adviser has approved them, according to Reuters. That is guided orchestration in practice. On the trading side, Goldman Sachs has teamed up with Anthropic to develop agents for trading, transaction accounting and client onboarding, and Citi is preparing an AI-enabled virtual wealth management team member.

Context is what makes the technology work

Context informs technology, so the data comes first. Most of what a wealth firm knows about a client sits in unstructured form: adviser notes, emails, meeting transcripts, provider documents, suitability reports, call recordings. Conventional systems cannot read it. AI can. Agents ingest that material, connect it to the structured records in the CRM and portfolio systems, and hold the result where the next adviser or the next agent can use it.

The gains show up in metrics that operations teams already track. First time right, the share of cases that clear onboarding, suitability or review without rework, should improve significantly when AI tools, ours included, work from information that is complete from the start. Every reworked case that disappears returns capacity to the back office. Some of that capacity lowers the cost of running the operation. The rest goes back to advisers and service teams, who get more time with clients.

Screening shows the effect clearly. A politically exposed person check can return 5,000 potential hits. An agent working with the right context, including the client’s history, source of wealth and earlier decisions, can narrow that to the ten that need a person’s judgement and record its reasoning on the rest. The time saved is direct, and it lands in the back office first.

Growth, retention and the client

Efficiency is half the case. The other half is revenue. Advisers with more time and better information can serve more clients, respond faster and see needs before a client raises them. Clients who feel known stay. A firm built around the client, not around its own systems, keeps more of the relationships it wins.

The obstacle is practical. Client data lives in many places, so the questions are how to bring it to one place, how to mine it quickly and how to consolidate the platforms underneath. We have invested significantly in all of our products to answer them. Sapient Bodhi builds and orchestrates agentic workflows with industry context-built in. Sapient Slingshot reads legacy code, documents the current-state by extracting business rules and dependencies and then migrates to a more modern codebase in an accelerated time, while taking much of the risk out by folding several platforms into fewer.

The enterprise context graph

Bringing the data together is the start. An enterprise context graph maps how clients, accounts, advisers, products, policies, decisions and outcomes relate to one another, and keeps that map current. It is the shared context layer behind our platforms, and it grows more accurate the more it is used. An agent preparing a client review can then see the holdings, and also the meeting where the client’s appetite for risk changed, the recommendation that followed and the compliance sign-off attached to it. The graph belongs to the firm. Models can be swapped as better ones arrive while what the firm has learned stays where it is.

Governing the context layer

A layer that holds client history, advice and compliance outcomes needs governing as carefully as the agents that draw on it. Access should follow permissions, so an agent sees only what the person it serves is entitled to see. Every piece of context needs a traceable source, so any recommendation can be followed back to the information behind it. Outputs need testing against client outcomes, and each workflow needs a named senior owner under the same regime. UK GDPR applies to the graph as it does to any other client record.

Where to begin

For leaders wondering where to begin, none of this requires redesigning everything at once, although it does mean starting now to build the capabilities that allow AI to operate safely and effectively across the enterprise, and three priorities stand out.

The first is to define decision rights for humans and AI

Establishing clear responsibilities for where AI can recommend, where it can execute and where human judgement remains essential, with governance, accountability and transparency designed into every workflow from the start. A useful exercise for any leadership team is to ask which activities in the business genuinely require human judgement and which persist simply because that is how they have always been done.

The second priority is to build the context layer

Connecting client information, adviser expertise, investment policies and historical decisions into a shared context that both advisers and AI agents can draw on when a decision needs to be made. This is where the durable advantage lies, and it is worth every firm asking what proprietary context, experience and decision-making knowledge it holds that a competitor, technology vendor or foundation model could not easily reproduce, since that knowledge is the raw material from which institutional memory is built. When we work with wealth firms on this through Sapient Bodhi, our enterprise agentic platform, most of the early effort goes into connecting that knowledge and building the governance around it, because the model a firm uses will keep evolving while its institutional memory keeps growing in value with every client interaction.

The third is to measure client outcomes in place of AI adoption

This sounds obvious but runs against how most AI programmes currently report their progress. It is tempting to track the number of use cases launched or the share of staff using a new tool, yet those figures say very little about whether the firm is getting better at what it does, whereas client results, decision quality, operational efficiency and improved governance are the measures that will matter to boards, to regulators assessing Consumer Duty and ultimately to clients themselves. They are also the measures that show which workflows deserve to be scaled across the organisation, so that every deployment strengthens the firm’s institutional memory and makes the whole system more capable with each client interaction.

Any wealth leader can start that work tomorrow by asking which decisions in their firm are still waiting for a meeting, a spreadsheet or a manual review, because each one of those is a place where the firm’s own memory could already be doing the work.

Learn more at publicissapient.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Fintech & Insurtech
  • InsurTech

Author

Errol Rodericks

Published

6 October 2026

Estimated Read time

8Mins

Errol Rodericks, Product & Solutions Marketing Director at Denodo, on why infrastructure, security, governance, resilience and accountable business authority remain essential

For years, people have discussed sovereignty in financial services primarily in terms of geography. Where is the data stored? Which jurisdiction applies? Who operates the infrastructure? Those questions still matter. But they are no longer enough.

Banks and insurers now operate through an increasingly complex web of cloud providers, SaaS platforms, data ecosystems, third parties, AI models and legacy systems. Modern financial services could not function without these dependencies. The more important question is whether the institution remains in control.

Can it determine where sensitive information is used? Will it understand which critical services depend on which providers? Can it change a platform without rebuilding every data integration? Can it preserve consistent policies and business definitions as information crosses systems? And if a provider, region or legal basis suddenly becomes unavailable, can it continue serving customers within acceptable tolerances?

That leads to a different definition of sovereignty. Sovereignty is not technological self-sufficiency. It is controlled choice.cThe objective is not to remove dependency, but to ensure that critical dependency remains deliberate, governed and, where necessary, reversible.

The sovereignty problem is becoming an architecture problem

One of the biggest threats to institutional control is not simply fragmented data. It is fragmented control.

A typical financial institution has authoritative information distributed across core systems, SaaS applications, multiple clouds, data warehouses, lakehouses, APIs and external providers. Over time, integrations multiply, data is copied, pipelines proliferate, policies are implemented differently across platforms, and business definitions diverge. Each individual decision may appear reasonable. Collectively, they can make the institution increasingly difficult to govern.

The same customer may be represented differently across systems. A masking policy may exist on one copy but not another. An application can become tightly coupled to a provider-specific schema. During an incident, identifying which services, data and customers are affected may require manual reconciliation. The institution may still own its data and hold contracts with its providers, yet its practical ability to exercise control has diminished.

Risk accumulates at every hand-off through aggregation risk, policy drift, conflicting meaning and exit friction. The sovereignty problem therefore becomes architectural. How do you maintain consistent control across a distributed environment without forcing every workload into the same place? Geography remains one control. Architecture determines whether that control survives across the wider estate.

From data sovereignty to decision sovereignty

AI makes this question more consequential. When technology primarily stored and reported information, sovereignty could be discussed largely in terms of data, infrastructure and access. AI consumes information to interpret, recommend and increasingly act.

That introduces another question: who controls the context through which the institution is understood? Consider a fraud system assessing an unusual international payment. The transaction may genuinely look anomalous, and the model may be functioning perfectly. But perhaps the payment is associated with a corporate Treasury event that has already been authorised.

The data may be correct. The model may be correct. Yet without the relevant business context, the resulting action may still be wrong. The same issue appears elsewhere in financial services.

A lending decision requires more than a credit score. It may also require current exposure, affordability, product policy and delegated authority. An insurance claim cannot always be understood from the claim record alone: policy conditions, catastrophe events, customer circumstances, fraud indicators, documents and third-party information can all change the appropriate response. An AI customer-service agent may know the customer and product but still need to understand consent, entitlement, jurisdiction and what actions it is authorised to perform.

Sovereignty therefore has to extend beyond controlling where information resides to controlling how it is understood and how that understanding influences decisions. That is the emerging challenge of decision sovereignty: can the institution reconstruct what information was used, what it meant at that moment, which policies applied, which technology participated, what authority was delegated and why the resulting action occurred?

Institutional understanding should not belong to the model

This creates an important architectural question for enterprise AI. Financial institutions are unlikely to depend on one AI model forever. They will use combinations of commercial models, specialist models, internally developed models, agents and orchestration technologies. Those technologies will change rapidly.

Institutional understanding should not have to change with them. If the meaning of “customer exposure”, “vulnerable customer”, “suspicious transaction” or “eligible claim” is embedded independently inside every application, vector store or AI model, changing technology becomes much harder. Part of the institution’s business understanding has effectively become coupled to the technology consuming it.

A more sovereign architecture separates the two. Authoritative enterprise sources remain authoritative. A governed information and semantic layer can provide approved views, business definitions, relationships, lineage, access policies and current operational context. Applications, analytics and AI can then consume that governed context rather than reconstructing institutional meaning independently.

This leads to an important principle for AI-era sovereignty: Models should be replaceable. Institutional understanding should not be. The architecture in the underlying briefing makes this separation precise: enterprise sources remain authoritative and policy-bound, governed semantic context sits between those sources and AI, and model selection remains a separate policy decision.

Sovereignty does not mean zero-copy

The same nuance applies to data movement. It is tempting to equate sovereignty with never moving data. But that is neither practical nor always desirable. Some workloads require persistent history. Others need caching for performance or continuity. Some analytical workloads suit physical data platforms better.

The better question is whether movement is a governed decision rather than an integration default. Can a query run against the authoritative source when appropriate? If information must be cached, can the institution control where that cache resides, how long it persists and how it is protected? If a physical copy is required, is its purpose explicit and are retention, deletion and access obligations preserved?

Live federated access, controlled caching and physical pipelines can all be legitimate patterns. The appropriate choice depends on sensitivity, lawful location, freshness and latency requirements, source capacity, continuity requirements and economics. The principle is not “copy nothing.” Do not make another copy the automatic price of using information.

Policy must survive consumption

Control can also disappear between policy definition and actual use. An institution may have strong privacy, security and conduct policies, but if those controls are implemented differently across copies, applications and channels, policy is no longer consistently authoritative. The risk grows when information is dynamically assembled for analytics or AI.

A stronger architectural approach brings together identity context, approved semantic views, fine-grained controls and source-native permissions at the point information is accessed. As the briefing argues, policy is strongest when business meaning, access context and source controls agree.

That does not mean technology determines policy. Legal, compliance, risk and business functions still decide what is permissible. Technology’s role is to make those decisions enforceable and evidenced when information is actually used.

Exit clauses are not the same as exit capability

Another uncomfortable aspect of sovereignty is something financial institutions increasingly need to confront. A contract may contain excellent portability and termination provisions. That does not mean the institution can actually leave.

Applications may depend on proprietary schemas. Pipelines may embed provider-specific logic. Business definitions may exist only inside a platform. Identities, caches, networks and operational processes may all be intertwined with the provider.

True reversibility therefore requires more than contractual rights. It requires stable interfaces, reusable semantics, visibility of dependencies, replacement patterns and, critically, rehearsal.

As the briefing puts it, exit is a capability only after rehearsal. Contracts are necessary, but measured execution determines whether portability is real. That gives boards a more useful question. Do not ask only, “Do we have an exit clause?” Ask, “When did we last prove that we could exercise it?”

Controlled interoperability is the alternative to isolation

None of this means financial institutions should retreat from cloud, SaaS, external data, ecosystem partnerships or AI. Quite the opposite. Greater control can enable greater openness.

A bank that knows where its information comes from, understands how it is being used, can enforce access policies consistently, preserves its own business semantics and can change consuming technology is in a stronger position to collaborate.

This is why the future of sovereignty is not technological isolation. It is controlled interoperability. Institutions should be able to connect what they need without surrendering control of what they own.

That requires infrastructure controls, identity and cryptographic controls, cybersecurity, model governance, contractual protections, operational resilience, human authority and accountable business ownership. No data platform replaces those responsibilities.

A logical data and context layer can, however, address an important part of the problem: keeping distributed information connected, governed and understandable without requiring every consumer to create another proprietary representation of the institution.

The board question is changing

Financial-services sovereignty should ultimately be judged not by where a server is located or by a provider’s nationality, but by the institution’s ability to exercise control when conditions change.

Can it identify critical dependencies? Can it govern sensitive information wherever it is legitimately used? Can it preserve its business definitions and institutional context? Can it change providers, platforms or AI models without reconstructing its information environment? Can it continue operating when a dependency fails? And when an automated decision has consequences for a customer, can it explain what happened and remain accountable for the outcome? Those questions turn sovereignty from a political or technological abstraction into an operational capability.

Financial institutions will remain interdependent. They will continue to rely on global cloud providers, SaaS platforms, data ecosystems, and rapidly changing AI technologies. The objective is not independence from them, but ensuring dependency never quietly becomes a loss of control. That requires an architecture in which authoritative data can remain governed, institutional meaning can remain consistent, and applications, platforms and AI models can change without forcing the organisation to reconstruct its trusted information environment each time.

In conclusion

This is where a governed logical data and context layer plays an increasingly important role. By connecting distributed information without making another central copy the default, preserving semantics and lineage, enforcing governance at the point of access, and making trusted business context reusable across applications and AI, financial institutions can preserve control while continuing to innovate.

They do not make an institution sovereign. Infrastructure, security, governance, resilience and accountable business authority remain essential. But by helping separate institutional data and understanding from the technologies that happen to consume them today, they can help preserve something increasingly valuable as those technologies change: control of the institution’s data and understanding remains with the institution.

Learn more at denodo.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Payments
  • Fintech & Insurtech

Author

Chris Wilmot

Published

24 September 2026

Estimated Read time

5Mins

Chris Wilmot, Chief Financial Officer at Medius, on how CFOs who lead automation with a people-first approach can simplify operations, reduce errors, and unlock strategic value to bring the full benefits of digital transformation to their finance teams and the broader organisation

Digital transformation in finance is often framed as a technology challenge. But the real challenge lies in managing complexity.

The Association for Financial Professionals reports that more than half of FP&A professionals rely on eight or more planning tools and nearly ten reporting systems each quarter. This highlights just how fragmented the financial technology landscape has become. And in 2026, this complexity is now a top concern at the leadership level. According to Gartner, 42% of CFOs say unifying finance function, technology, and AI strategies is one of their biggest challenges. These inefficiencies slow performance and impact the day-to-day workflows of the teams managing these processes. This contributes to frustration, disengagement, and lost productivity.

As the leaders responsible for overseeing the financial performance and technology investments of their organisations, CFOs play a critical role in helping to reduce workflow complexity. Addressing this challenge requires more than just implementing new tools. It means eliminating fragmented systems, adopting automated workflows, and ensuring that teams are supported in every step of the way.

CFOs who take a people-first approach to automation not only streamline processes and reduce friction but also unlock strategic value and set their teams up for long-term success.

Automating the right processes in a Digital Transformation

Medius’ 2025 global survey found that finance professionals spend more than three and a half hours each day on repetitive tasks that could be automated. Over a year, that adds up to more than twenty-three working weeks lost to manual work. Which is time that could be spent on higher-value activities such as forecasting, analysis, and strategic planning. With fragmented systems consuming so much time, CFOs’ most immediate opportunity is to identify the processes that automation can help to simplify.

Much of this inefficiency comes from disconnected workflows. These require teams to manually transfer, reconcile, and validate data across multiple systems. This friction slows operations, increases the risk of errors, and contributes to frustration among finance teams.

CFOs should begin by identifying which processes create the most friction and where automation can have the greatest impact. Tasks that are repetitive and span multiple systems – like invoice processing, matching, and reporting – are often where automation can deliver the fastest, measurable results.Targeting these bottlenecks head-on can help eliminate unnecessary steps and make the finance function more agile.

Realising the Power of AI

Adopting an AI-powered accounts payable solution, for example, enables finance teams to centralise invoice and payment workflows that previously spanned multiple platforms into a unified software suite. This approach reduces manual data transfers, improves accuracy, accelerates approvals, and enhances cash flow visibility. It also allows finance teams to detect discrepancies earlier, before they escalate into larger issues.

Beyond improving efficiency, this shift also enables finance teams to operate more collaboratively across the business. With a unified software suite, finance teams can more easily collaborate with suppliers, procurement, and other internal stakeholders, improving visibility and coordination across workflows. This more connected approach helps streamline processes, reduce miscommunication, and strengthen relationships across the broader financial ecosystem.

Even as technology drives efficiency, human expertise must remain central. Automation should support judgment and strategic insights rather than replace them. By guiding teams through adoption, CFOs can simplify workflows, minimise errors, and ensure strategic impact.

Putting People First in Automation

Identifying the right processes to automate to reduce complexity is only half the challenge. What really sets CFOs apart is how they support their teams through adoption. Automation and AI are effective only if finance professionals at all levels of the organisation trust the technology, understand its purpose, and feel confident using it.

Introducing new technology can create uncertainty. Especially when past automation initiatives have been associated with layoffs or restructuring. Transparency is crucial in all communications surrounding AI. CFOs must communicate clearly why new systems are being implemented, how workflows will change, and what this means for individual roles.

Successful AI adoption also requires meaningful investment in training. Teams need hands-on support to see how new tools reduce friction in their daily work, not just how they support long-term transformation goals. Experiencing the benefits firsthand makes employees far more likely to embrace change, boosting engagement, productivity, and retention.

This approach also reinforces a key point: automation is meant to complement human expertise, not replace it. Finance professionals bring judgment, context, and strategic insight that technology cannot replicate. Keeping that expertise at the center ensures automation frees teams from repetitive, low-value tasks while empowering them to focus on critical decision-making.

By leading with this people-first mindset, CFOs can streamline operations to reduce errors and unlock greater value across the finance function.

Turning Automation into Strategic Impact

Automation and AI can transform finance operations, but only if CFOs lead with intention. By focusing on the right processes and keeping people at the centre, leaders can reduce complexity, free teams from repetitive work, and create real strategic value.

How CFOs can tackle complexity and drive value through automation:

  • Communicate purpose and impact: Explain how each initiative simplifies workflows, reduces friction, and benefits both the business and the team.
  • Invest in training and support: Give teams hands-on guidance so they can use new tools confidently and efficiently.
  • Reframe workflows around expertise: Let automation handle repetitive tasks while keeping human judgment at the center, reducing unnecessary complexity.
  • Monitor adoption and adjust: Track usage, gather feedback, and tweak processes to ensure workflows remain streamlined and effective.

It’s also essential to make sure that employees see the benefits of automation firsthand. When finance professionals see how technology simplifies workflows and creates space for strategic contributions, they’re far more likely to embrace change, and engagement, productivity, and retention improve along the way.

CFOs who lead automation with a people-first approach can simplify operations, reduce errors, and unlock strategic value to bring the full benefits of digital transformation to their finance teams and the broader organisation.

Learn more at medius.com

  • Digital Payments
  • Digital Strategy
  • Embedded Finance
  • Fintech & Insurtech

Author

Tamsin Crossland

Published

17 September 2026

Estimated Read time

4Mins

Tamsin Crossland, Principal AI Architect at Icon Solutions, on why ethical thinking, empathy and broader perspectives, leave organisations better equipped to build AI systems that are not only intelligent, but fair, resilient and trusted

Artificial Intelligence (AI) has been making headlines as it moves from a future-facing concept to a modern reality. From fraud detection and credit scoring to customer service automation and risk modelling, AI systems are increasingly shaping real-world outcomes, particularly within the financial services industry. But as AI adoption accelerates across payments and technology, one question has become unavoidable: who is training and developing AI models, and whose perspectives are shaping their decisions?

AI is not neutral; it reflects the data it is trained on, as well as the assumptions and biases of the teams that design, test and deploy it. When those teams lack diversity, the risk of reinforcing existing biases increases. As a result, building ethical AI is as much a human challenge as it is a technical one.

Gender representation

Women remain significantly underrepresented within the technology and payments industries, a reality that often begins in school, before career choices are made. Early perceptions that computing is ‘a boy’s hobby’ continue to influence subject choices at school and university level, narrowing the pipeline of female talent entering technical and computing roles. While slow progress has been made, recruitment processes can still carry subtle gender bias, with many IT and technology placements being given directly to men over women. This is shaping who feels encouraged or excluded from pursuing careers in AI and advanced technologies.

Real change must begin earlier in the classroom. Inclusive education initiatives, visible role models and outreach programmes can actively encourage girls to pursue careers in STEM. At the industry level, companies should critically evaluate recruitment strategies and consider more ambitious gender diverse targets, such as aiming for a 50% female graduate intake. This is not about optics, but about building a workforce that better reflects the society AI systems ultimately serve.

Why diversity is critical in AI development

The case for gender diversity has become even more compelling when examining AI training and deployment. AI models are built on historical data, data that may already contain embedded biases. Without diverse perspectives in the initial phase, challenging assumptions and the biases used to create AI models, these biases can be amplified once the AI system is deployed.

Gender-diverse teams bring a range of different perspectives and backgrounds. For instance, women bring valuable and often underappreciated perspectives to the process. Emotional intelligence and empathy, frequently described as ‘soft skills’, are increasingly critical as AI systems move beyond efficiency tools and into decision-making roles that affect people’s lives. The ability to anticipate unintended consequences, question whether an outcome feels equitable, and consider how different groups may experience an automated decision is essential to responsible deployment.

Diverse teams are better positioned to interrogate training data, stress-test algorithms and identify blind spots before products are launched. Research consistently shows that gender diverse teams outperform male-dominated ones in problem-solving and innovation, generating 45% of total revenue from innovation, compared to 26% by less diverse teams. In the context of AI, diversity becomes a form of governance, a mechanism for reducing risk and strengthening accountability.

Encouragingly, women are already leading important conversations within AI ethics forums and industry working groups, such as Women4Ethical AI, AI4ALL and Women in AI (WAI). They are contributing to debates around transparency, fairness, explainability and responsible use. However, discussion alone is not enough, and now it is time for action. The next step is embedding these principles into product development cycles, procurement standards and executive decision-making processes.

Ethical AI cannot be treated as a retrospective compliance exercise, but as an integral part of the initial development phase.

Shaping the future of ethical AI

As AI continues to shape the future of payments and technology, responsible representation must be viewed as a strategic imperative. Without gender diversity, the risk of reinforcing systemic bias grows. With diverse teams guided by ethical thinking, empathy and broader perspectives, organisations are better equipped to build AI systems that are not only intelligent, but fair, resilient and trusted.

Learn more at iconsolutions.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Payments

Author

Serge Bejjani

Published

4 September 2026

Estimated Read time

3Mins

Serge Bejjani, Co-Founder & CEO of Shootday, on the importance for FinTechs of treating brand indentity as intellectual property

It’s a pervasive idea that reproducing the visual language of credible, established institutions lets new FinTech entrants borrow trust by association. It’s also wrong, and the consequences are real.

Familiarity isn’t reassuring anyone

FinTech customers have, over the last decade, run into all manner of low-trust players dressed in the same polished aesthetic. The reassuring visual cues that once signalled FinTech’s credibility have been hijacked by unscrupulous outfits as a cheap disguise.

Where generic branding might once have read as safely conventional, it now reads as deliberately evasive. Sameness has become the hallmark of a company with nothing much to say for itself, a shrug that leaves the customer with nothing to go on when deciding whether it’s worthy of their trust.

Trust in financial services isn’t built through aesthetic conformity but through demonstrable reliability and a coherent identity. That takes time, and for a weak FinTech, time only increases the odds that its shortcomings get exposed.

At a moment when customers have every reason to approach fintech with caution, messaging that whispers “this could be any FinTech” is a red flag. Customers who can’t tell one FinTech from another aren’t reassured by the homogeneity, it just strips them of any basis to form an opinion, good or bad, which is a poor foundation for a relationship built on money.

Similar by accident

No firm decides, one day, to become generic as a strategy. Product, growth and marketing teams independently optimised toward frictionless onboarding, familiar messaging and low-risk visuals. Because those choices tested well, cleared compliance faster and reassured investors used to recognisable patterns. Individuality got lost across a decade of small, rational decisions. What comes next will happen a great deal faster.

Copy and paste

Generative tools now produce marketing copy and design at a pace no internal FinTech team can match, and they’re doing it by training on a body of content that’s already remarkably homogenous.

Ask any AI model to design a “trustworthy landing page” for the sector and, in an afternoon, it will hand back the same blues, the same reassuring sans-serif type and the same promises of ease and control that already define the sector’s digital presence, because that’s what the training data mostly contains.

Every firm using these tools without creative oversight is baking the same clichés into its marketing at scale. The firms moving fastest to fold AI into their marketing stack may, in practice, be moving fastest toward irrelevance. In 18 months, those who let AI construct their identity may wake up to find they don’t really have one.

Trust your people

This isn’t a problem a better font or a wider colour palette will fix. As AI increasingly shapes what FinTech content looks like, the advantage shifts to firms with a genuinely distinctive identity, one AI can’t replicate.

Executive headshots and stock imagery still have their place. They fill a LinkedIn profile or an investor deck. But a templated one, the same backdrop and the same pose every other firm is using, does nothing to separate one FinTech from the next, and an AI-generated stand-in does even less. What actually differentiates is real footage from real moments: a founder in an actual client meeting, a team at an actual product launch, a conference stand that looks like the people running it rather than a template.

That means treating brand identity as intellectual property worth protecting, and giving marketing and product teams room to choose to look different, rather than defaulting to the safest option every time.

Serge Bejjani is co-founder and CEO of Shootday, a global photo and video production partner providing corporate event photography, event videography, and headshot services for businesses in 150+ cities worldwide.

  • Artificial Intelligence in FinTech
  • Embedded Finance
  • Fintech & Insurtech

Author

James Lawrence & Paul Bermingham

Published

28 August 2026

Estimated Read time

6Mins

James Lawrence, Insurance Specialist at Netcall and Paul Bermingham, CEO at Ecliptic, on why in today’s market, the holy trinity of service, efficiency and compliance isn’t a luxury. It’s a competitive advantage

For years, the insurance industry has operated under a persistent myth: that you can deliver excellent customer service, achieve operational efficiency, or maintain rock-solid compliance – but not all three at once. This idea of an inevitable trade-off between competing priorities has shaped countless transformation strategies, often limiting progress and frustrating innovation.

It’s a myth rooted in the industry’s history. Insurance has long been a world of complexity – fragmented systems, legacy platforms, and regulatory demands that make even minor changes feel high-risk. As a result, when businesses talk about improving the customer experience, it often comes at the cost of back-office control. When they talk about driving efficiency, compliance is treated as a barrier. And when compliance leads the agenda, service and speed are typically sacrificed.

But that mindset no longer holds. Technology has evolved. The tools now exist not just to manage this tension, but to eliminate it. And for insurers willing to rethink how they approach transformation, the so-called “holy trinity” of service, efficiency, and governance is no longer out of reach – it’s entirely achievable.

The low-code shift

What’s changed is the rise of low-code platforms. These technologies allow insurers to build and evolve business-critical applications using visual development tools rather than traditional coding. Crucially, low-code doesn’t require ripping out existing systems. Instead, it enables insurers to layer new capabilities on top of their core infrastructure – adding intelligence, automation, and user-friendly interfaces without destabilising the foundations.

In practice, this means change can happen faster, at lower cost, and with less risk. Processes like first notification of loss (FNOL), for example, can be rebuilt around modern expectations: triaged instantly, validated in real time, and – for low-value, low-risk claims – automated to settlement. At the same time, audit trails are maintained, compliance rules are enforced by design, and customers are kept informed with timely, personalised updates. Service, efficiency, and governance – working together, not competing for attention.

Automation and AI: Turning governance into a competitive edge

The evolution of intelligent automation and agentic AI has opened up new possibilities for insurers – not just in streamlining operations, but in embedding trust and control into every digital decision. These systems are no longer limited to simple rule-based tasks. They can interpret data, make context-aware decisions, learn from interactions, and adapt over time. In complex environments like claims processing, delegated authority, and regulatory reporting, this level of intelligence has real operational value.

But the success of automation doesn’t hinge on speed alone. It depends on trust. In regulated sectors like insurance, automation must be auditable, transparent, and capable of upholding internal policies and external obligations. This is where low-code plays a crucial role. Rather than retrofitting governance onto a system after it’s built, these tools allow insurers to embed governance by design – integrating rules, audit trails, escalation logic, and data safeguards directly into the workflow.

This approach changes the dynamic. Governance is no longer a limiting factor or a compliance hurdle. It becomes a source of confidence. Teams know that AI-driven decisions are accountable. Regulators can trace every step. And customers receive outcomes that are fast, fair, and well-documented.

Low-code also makes it easier to align AI with real business needs. Instead of chasing innovation for its own sake, insurers can focus on solving specific challenges – like improving FNOL response times or reducing the risk of customer complaints – and use automation to enhance outcomes without compromising oversight. Because platforms are configurable, not hard-coded, insurers maintain flexibility as policies, risks, and regulatory expectations evolve.

In this context, governance isn’t the price of innovation – it’s the foundation. It ensures that transformation is not only fast and scalable, but sustainable and defensible. The result is a smarter, safer path forward – one where automation improves service, boosts efficiency, and strengthens compliance in equal measure.

Meeting customers where they are

That experience is more important than ever. Today’s policyholders aren’t comparing insurers only to each other – they’re comparing them to tech giants like Amazon, which set new standards for simplicity, speed and personalisation. Insurance doesn’t get a pass. Yet too many customers still face long response times, unclear communication channels and paper-heavy processes that feel stuck in another decade.

The right approach changes that. European insurance company, Baloise, for example, used a low-code platform to quadruple straight-through processing (STP) from 15% to 65%. This meant faster responses, reduced error rates and a better overall experience for both customers and teams. By processing over 11,000 documents a day with greater accuracy and speed, Baloise could resolve enquiries and claims faster, without adding operational burden.

The impact went beyond efficiency. Customers benefited from a smoother, more consistent service that felt faster and more seamless at every touchpoint. Internally, employees were freed from repetitive, manual tasks and empowered to develop broader skill sets and handle more diverse work. That flexibility made it easier to respond to demand and reduced reliance on siloed roles.

These kinds of results don’t come from massive system overhauls. They come from smart, focused improvements – starting with the moments that matter most to customers. Low-code allows insurers to build around those touchpoints, while keeping core systems intact and compliance fully in check.

From compromise to convergence

The key is integration – not just of systems, but of skills. Transformation efforts succeed when business leaders, consultants, and technology teams work together from the outset. Today’s automated process mapping tools allow organisations to automatically capture, visualise, and analyse real-time workflows – highlighting friction points, inefficiencies, and opportunities for simplification before taking action. This upfront clarity ensures insurers aren’t simply digitising legacy inefficiencies but reimagining how work gets done.

It’s this combination of smart technology and domain expertise that moves the needle from compromise into convergence. When transformation is designed with strategy, service, and compliance in mind – from day one – insurers can build experiences that work for everyone: customers who want speed, regulators who want rigour, and teams who want tools that actually help them do their jobs.

The truth is, the trade-off is a choice – and it’s no longer a necessary one. With the right approach, insurers can achieve all three pillars of the holy trinity. Service doesn’t have to come at the cost of efficiency. Governance doesn’t have to slow things down. And efficiency doesn’t have to mean cutting corners.

Setting a new standard

Insurers that understand this – and act on it – will lead the next chapter of the market. Not because they’ve chosen a side, but because they’ve built systems where no one has to lose. In a market that’s only getting more competitive, that’s not just good strategy – it’s business-critical.

It’s time to retire the idea that you can’t have it all. You can. And you must. Because in today’s market, the holy trinity of service, efficiency and compliance isn’t a luxury. It’s a competitive advantage.

Learn more at netcall.com and ecliptic.tech

  • Fintech & Insurtech
  • InsurTech

Author

Peter Pugh-Jones

Published

24 August 2026

Estimated Read time

5Mins

Peter Pugh-Jones, EMEA Field CDO at Confluent, explains why as more AI projects move into production, that gap will become harder to ignore. The conversation will shift away from what AI could do and towards what organisations are actually able to support in practice

AI is no longer an experiment in financial services. It’s already embedded in day-to-day operations.

Banks are rolling out AI-driven customer service, investing in automation, and exploring how generative and agentic systems can improve decision-making. Around 92% of global banks are already using AI in at least one core function, which suggests the industry is well on its way.

As these initiatives scale, however, a different reality starts to emerge.

What works in a pilot often becomes more complex, not to mention expensive, when rolled out across the business. In many cases, the issue is not the AI itself, but the environment it is being deployed into.

There is no ‘magic bullet’ for AI in financial services. Its impact is shaped by how well organisations can access, connect, and act on their data — increasingly through real-time approaches such as data streaming.

When AI meets reality

This is particularly visible in customer service.

AI-powered chatbots and virtual assistants are now widely used to handle routine queries, categorise transactions, and guide users through basic processes, and in the right conditions they can deliver real efficiency gains.

Scaling those systems, however, is a different challenge altogether. Despite the level of investment, many of these systems continue to rely on fragmented or outdated information, which sees the experience quickly starts to break down.

For example, customer context can be missing or inconsistent. Conversations don’t carry across channels. Employees are forced to double-check or correct outputs, removing much of the efficiency they were meant to deliver.

In a sector where trust matters, that’s difficult to ignore.

Legacy systems are still doing most of the damage

Frameworks like the EU AI Act are shaping how organisations deploy AI, particularly in high-impact use cases such as credit scoring, fraud detection, and customer risk assessment. Requirements around transparency, auditability, and data governance are raising the bar for how AI systems are built and monitored. And rightly so, given the importance of trust in financial services.

In practice, though, legacy infrastructure is often the bigger issue. When data is fragmented across systems or difficult to access in real time, it becomes far harder to evidence how decisions are made or ensure models are operating on complete, up-to-date information.

In many cases, it is these underlying data limitations — rather than regulation itself — that slow progress. With many banks reliant on systems built up over decades through mergers, upgrades, and workarounds, they struggle to create a single, reliable view of a customer or transaction — something AI depends on.

This also helps explain how FinTech startups can outmaneuver industry giants. Free of technical or technological baggage, they can build around modern data architectures from the start and bring new ideas to market more quickly.

AI can’t reach its potential, or in some cases even run, in a legacy environment. Sticking with the customer service example, one such example is personalisation.

Personalisation only works if the data is current

Personalisation has been a goal in financial services for years, but in many cases it has been closer to segmentation than true individualisation. If AI is going to change that, it needs a flow of data that can tell it exactly what’s happening in the moment.

Hyper-personalised experiences depend on real-time context — understanding a customer’s behaviour and situation as it evolves, often enabled by data streaming to ensure systems are working from the most up-to-date information. Without that, interactions quickly become disconnected.

A customer might start a query in an app, follow up by email, and then call support, only to repeat the same information each time. This is not a limitation of AI, but of how information moves between systems.

When that flow is in place, interactions become far more seamless, with context carrying across channels and experiences feeling more consistent.

AI should support people, not replace them

There is a lot of discussion about AI replacing human roles in financial services, but a fully automated model is unlikely.

AI is highly effective at handling repetitive, high-volume tasks, like triaging customer queries or processing transactions. But more complex interactions still require human judgement, empathy, and context — particularly in areas such as lending, investments, or financial advice.

Customer expectations also play a role. While digital channels continue to grow, many customers still value the option to speak to a person, particularly in more sensitive or high-stakes situations.

As a result, the most effective model is increasingly a hybrid one. AI handles routine processes and surfaces insights, while people focus on more complex and sensitive interactions — provided both are working from the same reliable, up-to-date information.

Getting the foundations right

As AI adoption continues, the conversation is starting to shift.

Early investment has focused on what AI can do. Attention is now turning to what is needed to make it work properly.

That starts with data. Organisations need to ensure data is connected, governed, and available when it is needed. Too often, this work is treated as something to address later, when in practice delaying it tends to create more complexity and cost over time.

There is also a cultural element to consider. In many organisations, established ways of working can slow progress just as much as technology. Addressing that is just as important as modernising systems, particularly when AI initiatives span multiple teams.

A question of readiness

Financial services organisations are being asked to move quickly while maintaining high levels of control and accountability. That is not easy.

The organisations that make progress will be those that focus less on AI as a standalone capability and more on the conditions that allow it to work. If those conditions are not in place, results will remain inconsistent.

As more AI projects move into production, that gap will become harder to ignore. The conversation will shift away from what AI could do and towards what organisations are actually able to support in practice.

In most cases, it comes down to a simple question: is the data ready?

Learn more at confluent.io

  • Artificial Intelligence in FinTech
  • Data & AI

Author

Cien Solon

Published

17 August 2026

Estimated Read time

6Mins

Cien Solon, CEO & Co-Founder of LaunchLemonade, on why the future of financial services will be shaped by those who can balance AI innovation with responsibility

Every industry is racing to implement artificial intelligence. In financial services and other heavily regulated sectors, that race has been cautious. From automated underwriting and fraud detection to hyper-personalised customer experiences, AI offers FinTech companies unprecedented opportunities to innovate and scale. Yet alongside this promise comes the growing weight of regulation.

Different approaches to AI governance are already taking shape. In Europe, the EU is introducing a structured, risk-tiered framework that sets clear obligations depending on how AI is used. The UK is pursuing a more flexible, principles-based approach, allowing individual regulators to apply guidance tailored to their sectors. The US has a more decentralised model, blending federal direction with a growing patchwork of state-level rules. For FinTech founders operating across borders, this diversity can feel complex and, at times, overwhelming.

With these frameworks emerging, many fintech founders are beginning to hesitate. Questions around compliance costs, legal complexity and potential liability are causing some to reconsider or delay AI adoption altogether. For smaller fintech firms and scale-ups, the concern feels especially acute. Is the risk worth the reward?

It is. And founders who treat regulation as a strategic advantage, rather than a barrier, will be the ones who pull ahead.

Regulation as a Trust Multiplier

In financial services, trust is everything. Customers entrust FinTech platforms with their most sensitive data and critical financial decisions. Investors and partners demand reliability, transparency and accountability. Regulators expect firms to operate with integrity and control.

Robust AI governance can enhance trust across all of these stakeholders. FinTech companies that proactively adopt transparent and responsible AI practices signal maturity beyond their size. They demonstrate operational discipline alongside technical capability.

This matters especially now, when AI systems can appear opaque or unpredictable. Clearly communicating how AI is used, what decisions it informs, what data it relies on, and how risks are mitigated allows fintechs to differentiate themselves in a crowded market.

Compliance, when done well, builds credibility. And credibility, in financial services, is a powerful competitive advantage.

The Myth of ‘Compliance Paralysis‘

One of the most common misconceptions among FinTech SMEs is that AI regulation requires building entirely new compliance infrastructures from scratch. This belief often leads to what I call “compliance paralysis,” a reluctance to act because the problem feels too big to start.

In reality, much of AI governance maps directly onto frameworks that fintech companies already use. Risk management, model validation, data protection, auditability. These are familiar disciplines. AI extends them into new domains, but the foundations are already there.

Take model risk management. The steps a compliance team follows to validate a human-led underwriting process can be adapted to validate an AI-driven one. The logic is the same. The documentation requirements are the same. What changes is the technology performing the task, and the speed at which it operates.

I see this with our own clients. Regulated businesses assume they need to start from zero when it comes to AI governance. When we walk them through what they already have in place, the gap between current practices and regulatory expectations is almost always smaller than they feared.

The Agility Advantage of FinTech SMEs

Large banks may have dedicated compliance teams and significant legal budgets, but they also carry the weight of legacy systems. Retrofitting AI governance into complex, decades-old infrastructure is both costly and time-consuming.

Fintech SMEs have the advantage of agility. They can design and implement responsible AI practices from the ground up, embedding governance directly into their products and processes from day one.

This is exactly how we built LaunchLemonade. Every AI agent on our platform operates within a governance framework by default. Audit trails, data handling rules, transparency requirements. They are baked into the architecture, because bolting them on later is always harder and more expensive.

This compliant approach delivers real advantages. Long-term costs stay lower because governance is structural rather than retrofitted. Adapting to evolving regulatory requirements becomes faster because the foundations are already solid. And AI-enabled products reach the market ready to meet governance standards, rather than needing months of remediation after launch.

Regulation, in this sense, levels the playing field. Larger incumbents may have more resources, but smaller firms can move faster and design smarter. Those that embrace this will outpace competitors who delay.

Practical Steps for Leaders

For finance leaders looking to navigate AI regulation without overwhelming their teams, the key is to focus on pragmatic, high-impact actions.

Start by classifying AI risk early. A chatbot handling customer queries carries a very different risk profile from an algorithm making credit decisions. Understanding where your use cases sit on the risk spectrum allows you to prioritise governance efforts where they actually matter.

Be transparent by default. Inform users when AI is being used, explain its role in decision-making, and provide avenues for human review where appropriate. Clear communication builds trust and reduces regulatory friction.

Leverage what you already have. Map AI governance onto your existing risk and compliance processes rather than building a parallel system. This reduces duplication and accelerates implementation.

Build lightweight governance structures. You do not need a large compliance team to implement effective oversight. Documenting model assumptions, maintaining audit trails and assigning internal accountability can go a long way. At LaunchLemonade, we see regulated SMEs achieve meaningful governance with lean teams precisely because they focus on these fundamentals.

And know when to bring in expert input. Legal and regulatory expertise is valuable, but it does not need to be engaged at every step. Focus external support on high-risk use cases or areas of genuine uncertainty.

The Cost of Standing Still

If financial SMEs retreat from AI because of regulatory anxiety, innovation will not stop. It will simply concentrate in the hands of large incumbents and major technology companies with the resources to navigate complex compliance landscapes.

That concentration has broader implications. Competition reduces. Innovation cycles slow. Diversity in financial products and services narrows. Opportunities to improve financial inclusion and accessibility are missed.

Fintech and finserve has historically been a driver of disruption, challenging traditional models and expanding access to financial services. AI represents the next wave of that transformation. If smaller players step back now, they risk ceding that ground entirely.

Regulation as an Enabler

The conversation around AI regulation in financial services needs a shift in perspective. Regulation creates the guardrails that allow new technologies to scale safely and sustainably. It builds the trust required for widespread adoption. And it ensures that innovation benefits broadly, across industries and communities, rather than being concentrated among those who can afford to figure it out alone.

For fintech and finserve founders, the real question is how to innovate responsibly and strategically. Those who embrace that question will find that regulation is part of the answer.

AI is too important an opportunity for FinTech to ignore. Regulatory frameworks may seem daunting, but they are neither insurmountable nor inherently restrictive. When approached thoughtfully, they strengthen trust, streamline operations and unlock new avenues for growth.

Financial SMEs are uniquely positioned to lead in this space. Their agility, focus and ability to embed governance from the outset give them a distinct edge over larger, slower-moving competitors. The future of financial services will be shaped by those who can balance innovation with responsibility. The firms that recognise this will define the age of AI, rather than simply surviving it.

Learn more at launchlemonade.app

Cien Solon is the CEO and Founder of LaunchLemonade, a secure governed platform of AI agents for regulated industries. An experienced AI transformation leader, Cien has been building AI-powered solutions since 2018 and working with generative AI since 2022. Her mission is to ensure that smaller businesses in regulated sectors can adopt AI confidently, without the enterprise price tag. LaunchLemonade was included in Entrepreneur UK’s Top 100 Startups to Watch, and Cien was shortlisted in the Technology category of the 2025 Investec Early-Stage Entrepreneur of the Year Awards.

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech

Author

Dave Murphy

Published

10 August 2026

Estimated Read time

6Mins

Following the launch of its Core Modernisation Playbook, Publicis Sapient’s Dave Murphy, Head of Financial Services – EMEA & APAC, explores why so many banks struggle to modernise despite investing heavily in AI. The challenge is no longer knowing what to transform, but how to execute transformation safely

Most banks have spent the last decade applying AI where its impact is most visible, in chatbots, fraud scoring and personalised product recommendations. The results have been real, and in places significant, however, they have also been contained. AI has been working at the edges of the organisation, improving the surfaces that customers and compliance teams interact with, while the actual machinery of the bank carries on untouched. The core systems, the batch processing logic, the settlement rules written in COBOL three decades ago, have continued to run exactly as they always have, insulated from the transformation happening around them.

Meeting the AI Execution Challenge

That insulation is starting to break down. For CTOs weighing where AI investment should go next, the implications are considerable. The question is no longer whether banks need to modernise. Every major institution already knows it needs real-time payments, AI-ready operations, API-enabled architectures and cleaner data foundations. The challenge is execution. How do you modernise decades of critical systems without introducing unacceptable operational risk?

That is where AI is beginning to change the equation. Rather than focusing solely on customer experience or analytics, it is being applied to the engineering process itself. The discovery, extraction, transformation and validation work that has traditionally been the slowest, riskiest and most expensive phase of core modernisation is becoming increasingly automated. This is where the greatest opportunity now exists.

The Core Modernisation Playbook

Publicis Sapient explores this shift in its recent Core Modernisation Playbook. At the centre of that approach is Sapient Slingshot, a platform designed to apply AI across the engineering lifecycle rather than simply the coding stage. Slingshot applies AI across the entire modernisation lifecycle, from understanding legacy systems through to transformation and continuous validation within a governed workflow. The objective is not simply to write code faster. It is to make large-scale modernisation more predictable, auditable and easier to execute.

One global bank recently used this approach to analyse three million lines of COBOL and produce verified functional specifications in eight weeks, work that would previously have taken well over a year and required scarce legacy expertise that is becoming increasingly difficult to find. What changed was not the strategy or destination. It was the execution model.

Core banking code written over thirty or forty years rarely comes with a user manual. Business rules become embedded directly in software, operational workarounds accumulate over time, and critical knowledge often exists only in the minds of engineers who built the systems. As those engineers retire, banks are not simply losing people. They are losing the institutional knowledge needed to modernise safely.

This reflects a broader shift in how modernisation is delivered. Instead of treating discovery, code generation and testing as separate phases owned by different teams, they become part of a connected execution model. Understanding legacy systems, building modern services and validating every change happen as part of the same continuous process. That reduces risk while improving speed, which is exactly what heavily regulated organisations require.

AI-Powered Business

AI-powered business rule extraction is a direct response to that problem. Rather than depending on interviews with legacy experts and time-consuming manual code walkthroughs, automated analysis can surface the logic embedded in legacy programs, cross-reference it against actual processing behaviour, and generate specifications that engineers who’ve never touched the original COBOL can read and act on. In the case above, specification accuracy came in at 95%, and the time needed to analyse individual batch feeds dropped from 35 days to five. That compression changes the shape of what a modernisation programme looks like in its early phases. Work that used to occupy teams for the better part of two years can be substantially completed in months, and what comes out the other end isn’t an informal summary but a set of audit-ready specifications and, in this instance, more than 200 implementation-ready backlog items that become the foundation for everything that follows.

Establishing Strong Foundations For AI

Foundations matter because the validation phase of modernisation is where most programmes break down. There’s no shortage of ambition or strategic clarity in banking transformation. Most major institutions have roadmaps, target architectures and detailed business cases. What consistently falls apart is proving, with sufficient rigour, that a rewritten system behaves identically to the one it’s replacing.

Core banking processes handle exceptions, regulatory edge cases and accumulated business logic in ways that are difficult to fully enumerate up front, let alone test exhaustively by hand. Traditional testing models depend heavily on manually created test cases, subject-matter-expert review and long validation cycles. Coverage gaps are hard to detect, particularly when the legacy behaviour they’re meant to validate against is poorly documented in the first place. Banks end up completing the development work and then getting stuck proving the new system does what it’s supposed to.

AI-driven testing changes that equation by generating and executing test cases at a scale manual teams can’t match, covering standard transactions, edge cases and downstream dependencies systematically rather than sampling them. That doesn’t remove the need for human judgement on what ‘correct’ looks like, but, it does remove the bottleneck of generating enough coverage to have confidence in the answer.

Modernisation Strategy

None of this means banks can modernise the way digital natives do. Core banking platforms sit inside dense ecosystems of payments networks, risk engines, regulatory reporting systems and third-party providers. A change in one system can create consequences dozens of steps downstream, and banks must modernise around uptime, auditability and customer trust in a way a retailer redesigning a checkout flow simply doesn’t. That’s precisely why execution has become the defining challenge. Every bank already knows what it wants to build. The competitive advantage now lies in how effectively it modernises the foundations that make those ambitions possible.

Sapient Slingshot – Deploying An Execution Engine

What AI applied to the engineering process offers is a way to close that gap without pretending the underlying complexity has gone away. Understanding what a legacy system actually does, generating modern code and architecture that preserves the business behaviour underneath it, and validating the result continuously rather than at the end of a multi-year build: these are the three things that have historically made core modernisation slow, expensive and prone to stalling.

Applying AI across the engineering lifecycle does not remove the need for governance, auditability or human judgement. It strengthens them by making documentation, traceability and validation part of the execution process rather than activities completed afterwards. This is where platforms such as Sapient Slingshot are emerging as execution engines for modernisation, enabling banks to modernise with greater speed, control and confidence rather than simply generating code more quickly.

For CTOs, the practical takeaway is less about any single tool and more about where in the lifecycle AI is being pointed. The customer-facing layer of banking has benefited from a decade of AI investment. The engine room is only just beginning. The banks that move first will not simply modernise faster. They will build the AI-ready foundations that determine how quickly every future innovation can be delivered.

The Core Modernisation Playbook

Get your copy of the latest White Paper from Publicis Sapient here

  • Artificial Intelligence in FinTech
  • Digital Strategy
  • Fintech & Insurtech

Author

Dan Brightmore

Published

4 August 2026

Estimated Read time

4Mins

Graeme Donnelly, Founder & CEO of 1st Formations, on how small businesses can master cash flow and target growth

As British small businesses navigate a landscape of rising digital subscriptions and fluctuating utility costs, many are overlooking thousands of pounds in potential savings. While large corporations chase complex R&D credits, many limited companies are often leaving money on the table through sheer habit. 

In partnership with leading company formation agent, 1st Formations, we have identified 10 of the most effective, yet frequently ignored, financial hacks specifically tailored for UK SMEs. 

As Graeme Donnelly, founder and CEO of 1st Formations, puts it: “In 2026, operational efficiency is the new profitability. Many directors are so focused on top-line growth that they ignore the ‘silent drain’ of legacy bank fees, incorrect VAT categories, and dormant software subscriptions. Saving £200 a month through smarter digital choices is the equivalent of adding thousands to your annual turnover without the cost of acquisition. It’s about being as lean as you are ambitious.” 

1. The power of the ‘sector group’ 

Many directors believe the Federation of Small Businesses (FSB) or local Chambers of Commerce are only for established firms. In reality, the membership fee is often offset by the discounts they provide on business insurance, legal HR support, and software packages like Xero or Microsoft. 

2. The voluntary VAT advantage 

If your turnover is under the £90,000 threshold, you aren’t required to register for VAT, but doing so voluntarily allows you to reclaim VAT on everything from stock and web hosting to digital ads. While it does introduce an extra layer of administration, the ability to reclaim input VAT often far outweighs the effort involved. It also levels the playing field when pitching to larger corporate clients. 

3. Stress-test your small businesses VAT flat rate 

The VAT Flat Rate Scheme can significantly reduce your administrative burden, but many businesses are stuck in the wrong sector category. Reviewing your percentage, for instance 14.5% for IT consultancy vs. 7.5% for certain retail, could instantly boost your margins. 

4. Hunt for ‘hyper-local’ micro-grants 

While national grants get the headlines, local Growth Hubs and Local Enterprise Partnerships (LEPs) often have pots of £500–£5,000 earmarked for digital upgrades or energy efficiency. These are often poorly advertised but easier to secure than larger funds. 

5. Kill the manual spreadsheet 

Directors often miss out on tax deductions by forgetting small receipts. Using real-time scanning apps like Dext (for receipts), Pleo (for spending rules), or Expensify (for reimbursements) ensures every deductible expense is captured. Connecting these directly to your bank feed via QuickBooks or Xero turns a year-end headache into a streamlined process and makes bookkeeping easier. 

6. Look beyond the “big four” banks for funding 

Traditional loans are increasingly difficult for micro-companies to secure. Modern alternatives like Tide’s Funding Options, Uncapped (for revenue-based growth), or invoice finance platforms like Kriya offer the flexibility SMEs actually need without the long-term fixed commitments many traditional loans carry. 

7. Ditch the merchant account legacy 

If you’re still paying monthly rental fees for a card machine, you’re overpaying. Switching to fintech providers, such as Square, Zettle, or SumUp, eliminates fixed monthly costs and integrates sales data directly with your bookkeeping. 

8. Master the ‘work from home’ deduction 

If you run your limited company from home, you can charge relevant costs back to the business. These are essential business costs, such as a portion of your broadband bills, mobile phone contracts, and mileage or petrol costs. 

Whether using the flat-rate method (£6/week) or a formal rental agreement to claim a portion of mortgage interest and utilities, ensure you are documenting this correctly to stay HMRC-compliant. 

9. The quarterly subscription audit 

The ‘SaaS drain’ is real. Using tools like Cledara to flag duplicate project management tools or unused licences can shave hundreds off your monthly outgoings. To ensure this doesn’t slip through the cracks, set a recurring calendar reminder to audit your tools every quarter. Always negotiate at renewal; most providers have “retention rates” they don’t advertise.  

10. Stop paying for basic banking 

Many small firms are still paying monthly fees for basic banking, something digital-first providers have all but eliminated. Digital-first banks, such as Starling or Monzo Business, offer fee-free banking with superior app integration, saving both money and administrative hours. 

Learn more at 1stformations.co.uk

  • Digital Payments
  • Digital Strategy
  • Fintech & Insurtech
  • Neobanking

Author

Robbie Tilleard

Published

3 August 2026

Estimated Read time

4Mins

Robbie Tilleard, GM EMEA at Lorikeet, on why you don’t need a year, a team of twenty engineers, or a bespoke model trained on your data to deploy an AI in CX

Revolut launched its AI financial assistant in April this year. Starling launched one three weeks before that. An in-app AI financial assistant has become table stakes for FinTechs overnight.

The good news is you don’t need a year to get there. All you need is a plan and the right data.

Eight Years or Eight Days                                                                  

Revolut’s AI assistant took a year from announcement to launch, and Starling describes their assistant as the culmination of eight years of work. Both were building the infrastructure before it existed off-the-shelf. 

FinTechs we work with deploy an AI assistant in weeks, not months. They don’t build from scratch. They buy infrastructure that’s already solved the hard parts… Knowing when to escalate, handling ambiguity without hallucinating, navigating the tone of a conversation where someone has had their card blocked and is not in the mood for corporate language.

Sound familiar? It should. Every technology shift in financial services has looked like this for a period. Early movers build bespoke, the rest wait for the case studies, and then someone works out you don’t need to build when you can buy much faster. Then it simply comes down to having the right data in place, connecting your systems, and putting in place a clear implementation plan. Financial services knows the playbook. The difference is that the loop has simply gotten a lot faster with AI.

Why Not Wait Longer Then?

Customer expectations have moved, shaped by AI interactions people have outside financial services (support queries answered in seconds, refunds processed mid-conversation), and the gap between ChatGPT responses and waiting for a queued ticket to a human agent is becoming visible in churn data. The FinTechs that move first will set the bar for what customers expect from anyone in the same category.

There is also a regulatory dimension. In the UK, the FCA moved Consumer Duty into more active supervision this year. The review published in March 2025 found that vulnerable customers continue to receive worse outcomes than other customers, particularly where firms have primarily digital customer journeys, and that most firms have underestimated the depth of outcomes monitoring required.      

The reaction to AI in many compliance teams looks a lot like the reaction to autonomous vehicles. Every time a Waymo crashes, the genuine safety improvements (fewer accidents overall, faster hazard detection, no fatigue) get less coverage than the single incident. 

Yet AI is going to help Consumer Duty over time, not hinder it. Autonomy with the right guardrails produces more consistent outcomes than human handling at scale. An AI system can understand a customer’s problem immediately, resolve it on the spot where it can, and route to the right internal team where it can’t, without queue times, without agent fatigue, without twenty different people interpreting the same policy differently. This ensures the core customer care team remains focused and can prioritise the cases with the most need. And this isn’t a far off future for consumers. The infrastructure to build it already exists off the shelf.   

The European Dimension

There are additional points to consider if you operate in Europe. The EU AI Act’s full compliance requirements for high-risk AI sees financial services explicitly on the list. 

Being compliant means documented risk management, transparency mechanisms, human oversight baked into the architecture and a traceable record for every decision that the system makes. The cost of getting this wrong is high: up to €35 million or 7% of global revenue for the most serious breaches.

Most organisations aren’t close to achieving requirements. More than likely they’ve rolled out AI as an experiment (that’s good!) but without a plan to get it scaled in production (not so good). Systems built purely to retrieve and respond weren’t designed with auditability or explainability in mind and it isn’t something you can bolt on later. 

If you operate in Europe and have not mapped your current customer experience AI stack against these requirements, the time is now.

What You Actually Need                                                                                                

You don’t need a year, a team of twenty engineers, or a bespoke model trained on your data. You need focused use cases, infrastructure that handles the compliance layer, and a team willing to run a tight pilot with clear success criteria and a plan to scale.

You can move now. The companies that don’t are still lacing up.

Learn more at lorikeetcx.ai

  • Artificial Intelligence in FinTech
  • Cybersecurity in FinTech
  • Data & AI
  • Digital Strategy

Author

Diederik Wirtz

Published

31 July 2026

Estimated Read time

4Mins

Diederik Wirtz, Director of Business Development at Aryza, explains why your RCSA data contains far more intelligence than any quarterly heatmap conveys

Every organisation running a Risk and Control Self-Assessment programme is sitting on a goldmine of intelligence. Most treat it like a compliance filing cabinet. Updated periodically, reviewed by committee, archived until the next cycle. Heatmaps get produced, RAG statuses get refreshed, and everyone moves on.

Meanwhile, the COO is trying to understand operational fragility, the CFO is modelling downside scenarios, and the CEO is preparing for a board challenge on risk appetite. All receive dashboards that tell them what happened, but almost nothing about what is likely to happen next.

This is the gap between compliance-grade GRC and enterprise-grade GRC. The difference is whether your risk and control data produces outputs executives can use to make decisions or simply confirms that an assessment process took place. The signals are already in your RCSA data, the question is whether your tools are designed to extract them.

The Problem with Snapshots

Traditional RCSA programmes produce point-in-time snapshots. A risk is assessed, a control evaluated, a residual score assigned. But snapshots tell you almost nothing about trajectory, volatility, or the compounding effects of interconnected control weaknesses.

Consider a risk assessed as “medium” with controls rated “effective” for four consecutive cycles while three of its five mitigating controls have seen their effectiveness scores decline marginally each time. No single assessment raises an alarm. But the trend line tells a different story: gradual erosion that will eventually result in a control environment that can no longer hold.

This is the slow drift, the first and most fundamental signal organisations miss. It doesn’t show up on a heatmap. But it is visible the moment you analyse RCSA data longitudinally. The executive insight is simple: “This process area has looked stable for two years, but the controls underpinning it are weakening at a rate that will become material within 12 months.” That is enterprise-grade intelligence. A static heatmap is not.

Correlation Patterns Hiding in Plain Sight

Most RCSA frameworks assess risks individually within assigned business areas. A control weakness in procurement is evaluated separately from a similar weakness in vendor management. Each looks manageable in isolation.

But when you map data across domains, clusters emerge. Control effectiveness scores for anything related to manual data handling might have declined across four different business units simultaneously, a pattern revealing a systemic issue invisible to siloed programmes. These cross-domain correlations are precisely what a COO needs when deciding where to invest in process improvement, or a CRO needs when presenting enterprise-wide exposure to the board.

The Overconfidence Indicator

When a business unit consistently rates it controls as highly effective while simultaneously reporting rising operational incidents or audit findings in the same risk category, that disconnect is one of the clearest indicators of assessment bias and it is remarkably common.

The data to identify this gap already exists. RCSA scores sit in the GRC platform. Incident data sits in the loss event database. Audit findings sit in the internal audit tracker. The challenge has always been bringing these datasets together. When you do, the mismatches become immediately apparent and points directly to the areas of your control environment most likely to fail under stress. For an executive, this is not a technical finding. It is a credibility issue.

RCSA: From Assessment to Simulation

The real step change comes when you move from descriptive analysis to predictive modelling. Monte Carlo simulation transforms RCSA utility by running thousands of scenarios against your actual assessment data, applying random variation within defined probability distributions to produce a range of probable outcomes rather than a single point estimate.

The practical applications are directly executive-relevant. In merger and integration planning, simulation allows executives to see the impact of colliding control environments before they materialise. For capital allocation, it reveals which control improvements deliver the greatest reduction in probable exposure, enabling resource decisions based on quantified impact rather than qualitative judgment. For regulatory stress testing, it provides evidence that controls have been tested against plausible scenarios, grounded in operational data.

Signals Most Organisations Miss

Several patterns consistently emerge when RCSA data is examined with greater rigour:

  • Concentration risk in control ownership — a small number of individuals responsible for a disproportionate share of highest-rated controls, creating single points of failure invisible to traditional reporting.
  • Assessment fatigue — scoring clustering around previous assessments as cycles progress, indicating the programme may be generating the appearance of oversight rather than reliable information.
  • Phantom controls — controls receiving regular effectiveness ratings that have never been independently tested or are linked to processes that have fundamentally changed since the control was designed.
  • Velocity mismatches — risk categories (cyber, regulatory change) evolving faster than the assessment cycle, monitoring them, signalling where continuous monitoring should replace periodic review.

Your RCSA data contains far more intelligence than any quarterly heatmap conveys. Monte Carlo simulation, longitudinal trend analysis, and cross-domain correlation mapping turn RCSA from a compliance exercise into genuine executive decision-support. The signals are already in your data. Analysis is what locates them.

Learn more at aryza.com

  • Cybersecurity
  • Cybersecurity in FinTech

Author

Luke Trayfoot

Published

30 July 2026

Estimated Read time

5Mins

Luke Trayfoot, Global Head of Strategic Partnerships at YouLend, on how embedded finance can help create a more resilient and inclusive lending ecosystem for SMEs

Bank‑to‑business lending growth is expected to halve to just 3.5% this year, driven by global tensions, higher funding costs and ongoing economic uncertainty. For many small and medium‑sized enterprises (SMEs), this means tighter credit conditions and a growing risk of a funding gap. And at a time when fast, flexible capital is essential.

Traditional lenders will remain central to SME finance, but slower lending growth naturally limits how quickly banks can meet demand. SMEs often need capital at short notice to manage cash flow, invest in stock or respond to new opportunities. When bank lending slows, the pressure on these businesses increases. This is where alternative lenders and embedded finance providers are stepping in, offering models that work alongside the traditional system to ensure SMEs still have access to funding when they need it most.

A New Reality for SME Embedded Finance?

The slowdown in bank lending does not mean traditional finance is stepping back from the SME sector. UK Finance data shows that while gross SME lending rose for the second consecutive year in 2025, approval volumes have begun to flatten. Meanwhile, average loan sizes are trending downward, which signals that traditional lending capacity is tightening. What this really highlights is the need for a more diversified and resilient lending ecosystem. Alternative lenders, from online credit platforms to embedded finance providers, are becoming an important part of that shift by offering additional, data‑driven routes to capital.

These lenders are not here to replace banks. They are absorbing the demand that banks cannot meet quickly enough. Especially for SMEs that need fast decisions or more flexible repayment options. By using real‑time business performance data, alternative lenders can assess risk in a more dynamic way and offer funding that reflects how SMEs actually trade, rather than relying solely on historical, static credit files.

This becomes particularly valuable during periods of economic uncertainty. When traditional credit conditions tighten, SMEs with strong underlying performance can still access the capital they need, which helps prevent stalled growth or operational disruption.

Platforms as Distribution Hubs

E‑commerce marketplaces, payment providers and business software platforms are quickly becoming some of the most important channels for SME finance. A trend reflected in the scale of the market, with global embedded finance transactions projected to exceed $7 trillion by the end of 2026. These platforms sit at the centre of day‑to‑day business activity, which gives them a clear view of trading patterns, cash flow and overall performance. Because of that, they are in a strong position to offer funding at the exact moment a business needs it, whether that is during checkout, inside a dashboard or alongside a payment settlement.

For SMEs, this creates a much smoother path to capital. They can access funding without the paperwork or delays that often come with traditional loan applications. For platforms, it deepens customer relationships and opens the door to new value‑added services that support long‑term loyalty.

The Infrastructure Powering Embedded SME Finance

Behind the scenes, specialised FinTech providers supply the underwriting, risk modelling and compliance infrastructure that allows capital to move responsibly through non-financial channels. This infrastructure layer is what enables these business platforms to offer financing without becoming lenders themselves, while ensuring SMEs receive funding that is both fast and sustainable. It’s also creating a more standardised, interoperable foundation for SME credit delivery across the digital economy.

Government data shows that late payments continue to impact the UK economy to the tune of £11 billion annually, which creates a significant cash‑flow friction. By combining platform‑level insights with advanced risk analytics, embedded finance providers can tailor funding to real‑time business performance. This reduces default risk, improves capital efficiency and strengthens the resilience of the wider SME lending ecosystem, ultimately leading to better outcomes for both SMEs and the capital providers supporting them.

Smarter Underwriting is Becoming the Backbone of SME Finance

Real‑time performance data and AI‑enabled underwriting are reshaping SME finance by making funding decisions more accurate, more inclusive and more responsive to how businesses actually operate. Instead of relying solely on traditional credit scores, lenders can assess SMEs based on real trading activity such as sales patterns, seasonality and customer behaviour. This moves underwriting closer to the operational reality of a business, rather than a backwards-looking snapshot.

This approach opens the door for more businesses to access capital, including those that may be underserved by conventional credit models. It also helps ensure funding decisions remain stable even when market conditions are volatile, supporting business continuity and long‑term resilience. As these models continue to mature, they are becoming a core part of how lenders manage risk, price capital and build a more adaptive SME finance ecosystem.

Where SME Finance Goes from Here

As traditional lending growth slows, there is a real opportunity to build stronger partnerships between banks, FinTechs and digital platforms so SMEs can continue to access the capital they need. Embedded finance is becoming a mainstream, complementary channel that broadens access, speeds up decision‑making and supports day‑to‑day business growth.

The priority now is scaling these models responsibly. That means keeping transparency front and centre, maintaining strong risk frameworks and ensuring long‑term stability as more capital flows through embedded channels. If the industry can combine the strengths of traditional finance with the agility of modern FinTech infrastructure, we can create a more resilient and inclusive lending ecosystem for the SMEs that drive economic growth.

Learn more at youlend.com

  • Embedded Finance
  • Fintech & Insurtech

Author

Errol Rodericks

Published

29 July 2026

Estimated Read time

5Mins

Errol Rodericks, Director of product marketing EMEA and LATAM at Denodo, on why the future of insurance will be defined by whether humans and AI agents have the right data, in the right form, to make the right decisions

AI adoption across the UK insurance sector continues to accelerate, yet the impact is falling short of the industry’s expectations. The narrative is now shifting beyond systems that analyse data and generate recommendations towards agentic AI. Capable of taking action, whether that means initiating claims processes, identifying potential fraud in real time, refining underwriting decisions or triggering next-best actions. Many see this as the next major leap forward for insurance, and they may be correct, but not for the reasons most people think.

While AI development certainly doesn’t lack pace, most of the insurance industry remains constrained by a more fundamental issue. AI cannot make trusted business decisions until it first understands the business. That understanding depends on trusted, relevant data being delivered in the right context and at the right time. Without it, insurers will struggle to give AI systems meaningful autonomy, however advanced the models become.

This is not because insurers lack data or technology. Over the past decade, the industry has invested heavily in data lakes, advanced analytics and AI tools, and integration and data engineering pipelines. Yet despite these investments, many organisations continue to struggle to move AI beyond proof-of-concept projects and into day-to-day operations.

Meeting the Challenge

The challenge is not data availability. It is ensuring data is trusted, relevant and readily accessible when decisions need to be made.

Few industries experience this challenge quite like the insurance industry. Decision-making depends on information drawn from multiple policy, claims and customer systems, alongside external data sources such as telematics, weather and credit data. When that information is fragmented or delayed, even the most advanced AI systems are left working with an incomplete picture, making it difficult for AI to understand the wider business context behind every decision.

Agentic AI does not solve this problem. It exposes it.

Access Alone Is Not Enough

Many organisations respond by building a shared data foundation consisting of a unified layer where humans and AI agents can access the same information. While this is directionally right, it is incomplete. The challenge is not that organisations lack a shared data layer. They struggle to deliver the right version of data for each decision, at the moment it matters.

Insurance operates on multiple, decision-specific views of data, each with distinct requirements:

  • Claims decisions depend on real-time, enriched incident data.
  • Underwriting relies on forward-looking risk models and external signals.
  • Fraud detection requires cross-entity patterns and behavioural analysis.
  • Customer servicing depends on a simplified, current policyholder context.

These are not variations of the same dataset; they are purpose-built representations of data, shaped by different latency, governance, and semantic needs, which becomes even more critical with agentic AI. Different agents operate at different points in the decision lifecycle, and require different data, in different forms, at different times.

A shared data layer can improve access to information, but access alone does not guarantee better decisions. Context transforms data into business understanding, and business understanding is what enables trusted decisions.

Turning Data Into Action

This is where many AI strategies stall. Most architectures are designed to store, process, and analyse data, but not to activate it at the point of decision. There is a fundamental gap between data being available and data being usable within real-time workflows.

Agentic AI operates directly in this gap. Without access to live, governed, and contextually aligned data, agents operate with partial understanding, and their outputs become unreliable. This is why many AI initiatives remain stuck in experimentation.

To move forward, insurers need to rethink how data is delivered. Not as raw datasets or reports but as data products. A reusable, governed, and outcome-aligned data asset designed to support a specific decision or workflow is what’s needed. Instead of exposing raw data, insurers should deliver contextualised, decision-ready views, with embedded governance and policy controls, consistent business semantics, and real-time access to internal and external sources.

For example:

  • A claims data product unifying FNOL, policy data, repair estimates, and external signals.
  • A fraud data product combining claims history, network relationships, and behavioural indicators.
  • An underwriting data product integrating internal risk data with third-party enrichment.

These are not static datasets. They are decision-ready data assets, designed to deliver the right information, in the right context, for a specific business outcome.

Why Data Quality Matters Now More Than Ever

For agentic AI to deliver value, data must be live, governed at access, semantically consistent, and traceable. This is where a logical data layer becomes critical, not just as an integration approach, but as a way to connect distributed data in real time, apply governance dynamically, and deliver consistent, business-ready views across systems. This enables both humans and AI agents to act with confidence, without introducing further fragmentation.

The insurers that lead in 2026 will not be those with the most advanced models. They will be the ones that connect AI directly to business outcomes. That means starting with the outcome, such as reducing claims cycle time, improving fraud detection, increasing underwriting precision, or enhancing customer experience, and working backwards to define the decisions, data and systems required to support them.

This is how AI moves from experimentation to operational impact.

Where AI Initiatives Succeed or Fail

The next phase of AI in insurance will not be defined by advances in model performance. It will be determined by how well AI understands the business it is supporting. That requires trusted, relevant data, but it also needs the business context that allows AI to interpret that data correctly and make decisions insurers can trust.  

Agentic AI accelerates this realisation. It makes clear that data must be trusted, contextual, available at the moment of decision, and aligned to outcomes. The goal is not simply to give AI more information. It is to help AI understand how the business works well enough to make confident, consistent decisions. Those who solve this will scale AI successfully, and those who do not will continue to pilot without transformation.

The future of insurance will not be defined by whether humans and AI agents share the same data. It will be defined by whether they have the right data, in the right form, to make the right decisions. That requires a shift from shared data to decision-ready data, from access to activation, and from experimentation to measurable outcomes. The real inflection point for AI in insurance will come when organisations enable AI to understand the business well enough to make decisions that customers, employees, and regulators can trust.

Learn more at denodo.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • InsurTech

Author

Hugh Scantlebury

Published

22 July 2026

Estimated Read time

5Mins

Hugh Scantlebury, CEO and Founder of Aqilla , on why the finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight

At first glance, finance and accounting appear to be ideal environments for AI integration. The work is structured, rules-driven and built on numerical data, which AI can automate and process at scale and speed. As such, it’s ideal for much of the repetitive work — such as invoice capture, reconciliations, reporting, and anomaly detection. These tasks still consume so much time and reduce space and capacity for strategic and creative thinking.

But as confidence in AI grows across the sector, and as the technology is integrated into accounting and finance software, is there a risk that organisations once fearful of the technology may go to the other extreme? What might happen if they lean so deeply into AI that finance and accounting professionals become distant and removed from the numbers?

These questions matter because the decisions that sit behind the numbers are rarely driven by pure logic. Financial strategy is shaped by risk appetite, leadership judgement, organisational priorities and sometimes even internal politics.

So many entrepreneurial success stories include a fateful risk, gamble or moment of inspiration that defies established financial wisdom. A founder deciding whether to invest in growth or a charity balancing financial sustainability with its mission does not rely on numbers alone. That means a surprisingly high number of commercial decisions have a human dimension that AI cannot, and arguably should not, replace.

Balancing AI and Human Strengths

So how can organisations maintain the human intuition and instinct that sits behind so many corporate transactions while embracing AI? The answer is to use the technology to remove repetition rather than people from core financial processes. In practical terms, that means using automation to reduce cognitive load so people can focus on interpretation, creativity and decision-making — the human parts of finance and accounting. 

That’s a sensible approach because poorly implemented automation accelerates errors and obscures decision-making processes. It can also cut humans out of the process exactly at the point where instincts and experience are most needed. In that situation, AI-enabled systems simply become faster at making poor decisions. And those poor decisions end up costing time as well as money.

Once that happens, the space, time and resources that finance and accounting leaders are trying to create for more strategic work will rapidly shrink. By contrast, when humans actively guide AI systems, review outputs and set boundaries, automation becomes a powerful extension of human capability rather than a substitute for it.

The Limits of Automation in Decision-Making

In finance and accounting, numbers often create a sense of objectivity and certainty. But financial reporting still involves interpretation, context and judgement. A technically correct output is not always the same thing as the right commercial decision for a business, its employees or its long-term strategy.

As such, it’s important that accounting and finance professionals at every level do not lose a grip on their data when engaging with AI and automation. Aside from losing an understanding of the systems and processes behind the outputs, there’s still an ethical responsibility to deploy AI in a way that preserves human oversight, authority and compliance in financial reporting. That’s because, for the first time, we’re asking technology, in the form of AI, to provide an opinion on our data — not just deliver the logic and the numbers.

For that reason, finance leaders still need visibility into how AI-generated outputs are reached, the ability to challenge them when necessary, and a clear understanding of the underlying data behind the results. That’s important because AI output is ultimately based on prediction, and prediction is not the same as established and quantifiable truth.

Keeping Score 

One possible middle ground is confidence scoring and validation workflows. Rather than unquestioningly trusting every AI-generated output, organisations can introduce processes that flag lower-confidence results for human review before action is taken. That creates a more balanced relationship between automation and oversight, while also giving finance teams clearer visibility into how reliable or complete AI-generated outputs actually are. The goal should be confidence in AI-supported workflows, not unquestioning reliance.

That visibility, however, should not sit solely with grads and junior finance staff. They need to understand the manual calculations and processes sitting behind automated systems so they can properly challenge the results. But the same principle applies at senior levels too. Experience and seniority should not create distance from the underlying logic behind the numbers. If anything, AI makes that visibility even more important.

Otherwise, organisations risk creating the worst of both worlds: juniors who can’t challenge AI outputs because they haven’t learned the manual processes, and complacent seniors who know the manual systems and assume AI is following them. It means organisations can end up with more information at their fingertips than ever before, while simultaneously becoming more detached from the underlying data and logic behind it.

That risk becomes even more significant at senior levels, where financial decisions often carry wider operational, commercial and strategic consequences. The more detached leaders become from the logic behind the outputs, the greater the impact when reliability or credibility issues emerge within the data.

Conclusion

Productivity gains from AI and automation have the potential to create more space for higher-value thinking rather than remove people from the process. The information AI surfaces can ultimately help senior leaders make more creative and strategic decisions by revealing connections, patterns and insights that may previously have remained hidden within the data. 

Leaders have always relied on summaries, dashboards, and reporting layers to help them make decisions. But AI dramatically widens the gap between decision-makers and the operational reality beneath the numbers. The danger is not simply inaccurate data. It’s overconfidence. When systems appear highly intelligent and highly efficient, organisations can gradually stop questioning how conclusions are reached in the first place.

Ultimately, organisations may wish to focus on people-led automation rather than handing complete control to systems. The goal should be confidence in AI-supported workflows, not unswerving reliance on automated outputs — with AI acting as an extension of human capability rather than a replacement for judgement.

The finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight. Hand over the repetition and the manual processing, but don’t lose visibility into the data itself. Because if organisations surrender that understanding completely, all the entrepreneurial instinct, creativity and commercial judgement in the world may no longer be enough to compensate for the decisions being made underneath them.

Learn more at aqilla.com

  • Artificial Intelligence in FinTech
  • Data & AI

Author

Martin Jakobsen

Published

25 June 2026

Estimated Read time

5Mins

Martin Jakobsen, Managing Director at Cybanetix, on why SOC training and progression will be vital to meet the challenge of AI-driven cyber attacks

The accepted wisdom in cybersecurity is that the Security Operations Centre (SOC) has become overwhelmed. There’s some truth in this. Yes, the time taken to detect and remediate has grown, with IBM stating this now stands at 241 days. Yes, alert fatigue remains an issue, with SOC analysts snowed under 10,000 alerts per day leading to confirmation bias whereby positive alerts are discounted. And yes, alert volumes are rocketing due to a thriving black-market economy in attack kits and services, not to mention the emergence of AI-enabled attacks. Yet the SOC can adjust to these challenges and move with a rising tide if it adopts a paranoid posture.

One of the key attack trends over the past year is for threat actors to gain and maintain access legitimately. It’s for this reason we’ve seen a rise in credential theft (with an 800% increase in 1H2025) and the use of living off the land techniques (LotL). The attacker can log-in and use legitimate, trusted tools to evade detection and move laterally across the network to escalate those privileges. But the very fact these attacks don’t leave telltale footprints can make them much harder to detect. The SOC needs to rely on tools such as endpoint detection and response (EDR) and monitor tool usage to look for anomalies and that means listening to the less noisy network activity and low-severity alerts.

SOC: Selective Hearing

However, most SOCs focus on the high-severity alerts to such an extent that they even measure their success against the resolution of these incidents. SOC service level agreements typically specify a mean time to detection (MTTD) of 30 minutes and a mean time to respond (MTTR) of 15 minutes but the small print will often reveal this only applies critical alerts. Medium level alerts will typically be responded to within 1-2 hours and those low-level alerts that could indicate a LotL attack? Those are relegated to a window of 12 hours or more.

To capture this range of alerts will require the SOC to process ten times the usual volume of events. This presents the SOC with a quandary. How do you increase the monitoring and the ingest of alerts without submerging a SOC team who may already be drowning? For that to happen, the SOC must stop being selective and instead become more efficient.

To begin with, automation must be extended. Many teams will already be using Security Orchestration Automation and Response (SOAR) which can be configured to automatically remediate and close cases. But there are numerous other processes that can also benefit from automation. These can see the application of threat intelligence, automatic threat hunting and telemetry gathering, artificial testing, correlation with other alerts, and entity mapping, all of which can be used to enrich cases to the point where the SOC analyst doesn’t need to gather any further information when picking up the case.

Recovering from Cybersecurity Incidents

If we take the pre-defined playbooks that contain the recipe for how to detect, contain, eradicate and recover from specific cybersecurity incidents, for example, these can be automated to respond in a cascading effect. This sees one playbook used to trigger another depending on what the first has discerned, so that sub-playbooks iterate through lists of entities for each part of the case. Assuming 4,000 alerts coming into the SOC, triggering 100 playbooks per alert, with each playbook performing around ten actions such as enrichment lookups, correlation queries and containment steps, that’s equivalent to over four million triggered automations being executed per day before these cases are passed to a human analyst.

AI and the Analyst

AI, too, has a role to play. It currently enables the translation of free text questions into syntactical queries of Security Incident and Event Management (SIEM) and EDR data and can also be used to explain alerts, investigations and findings using human-friendly language and to summarise cases. It also lends itself to detection engineering, facilitating the faster creation of playbooks through the creation of code and detection syntax. With many detections sharing the same remedial actions, using AI to recommend a course of action makes sense, reducing analyst workloads substantially. And looking to the future, we can expect it to be used to optimise SOC operations by building playbooks on the fly and through the use of predictive investigative outcomes.

Applying these methods of automation enables any alert to be dealt with much more efficiently. The alert will initially be put through a process of enrichment, with threat intelligence used to compare the alert with known threats. SIEM and EDR searches will then correlate information, after which any associated alerts pertaining to the same entities (i.e. IPs, users, accounts, devices etc.) are linked to the case for the analyst to review. Using these processes, it’s possible to automate 65-70% of all SOC activity, dramatically reducing case workloads and allowing the SOC to process those massive alert volumes.

Human in the Loop

Yet, while automation is critical to adopting a paranoid posture capable of responding to evolving threats, it’s no substitute for the skills of the SOC analyst. Human expertise is invaluable in evaluating, verifying and deciding upon the best course of action and the likelihood is that those skills will become even more pertinent as AI becomes more widely used. This is because the technology can become so goal-driven that it becomes blinkered and can make incorrect deductions. In one instance, an AI agent went on to misinterpret a threat and produce a fictitious kill chain and mitigation advice, revealing the need for a human in the loop (HITL) and traceability in AI.

Paranoid monitoring does require the generation of more alerts for investigation. But we do have the tools and automation capabilities to handle those volumes so that this uptick needn’t equate to a massive increase in alert fatigue. What it does mean is that the role of the analyst will subtly change. They will be augmented by these processes and need to develop additional skillsets associated with validating these outputs, so the SOC will need to continue to invest in continuous training and progression, particularly if they want to hang on to that talent which remains in short supply. But unless we make these changes, the SOC won’t be able to keep pace with emerging threat patterns and will almost certainly fail to meet the challenge of AI-driven attacks.

Learn more at cybanetix.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • Digital Strategy
  • Infrastructure & Cloud

Author

Gareth Hewitt

Published

23 June 2026

Estimated Read time

5Mins

Gareth Hewitt, Founder of LemonEdge, on why the real lesson from DORA is that resilience has to run through the whole operating model, from the infrastructure underneath it to the controls and workflows used every day

The EU’s DORA regulation has exposed how firms operating in private markets face a set of risks that is not purely about IT.

Designed to reduce financial sector reliance on a small number of infrastructure vendors, the regulation came into force last year. Since implementation it continues to throw a spotlight on risks arising from continued use of manual methods and over-reliance on individual employees’ subject matter expertise.

The regulation is forcing firms to examine how their operating models work under pressure and where risk resides inside workflows. 

A private equity or private credit manager may sit on robust cloud infrastructure at one end of its technology stack, while still relying on spreadsheets, manual handoffs and offline approvals at the other. In that kind of environment, resilience is only as strong as the least controlled part of the workflow.

The real risk often sits inside the workflow

This is where risk accumulates in an organisation – in processes that are not properly systemised. This is amplified when they rely on manual intervention or a small number of people who know how to make them work.

A month-end close supported by spreadsheets and a sequence of offline steps may function well enough in normal conditions. However, it looks far less resilient when deadlines tighten, a key individual is unavailable or an investor query lands at exactly the wrong moment.

DORA, in effect, acts as a pressure test on whether resilience has been built into the operating model or simply assumed. Private markets firms have lived with this kind of exposure for years, often because experienced teams compensate for process weaknesses. Regulation leaves no room for that comfort. The question now is whether the workflow holds up under strain.

With DORA, accountability has moved to the top

One of the clearest shifts under DORA is the level at which accountability now sits. Historically, operational resilience could be treated as a technology matter and delegated accordingly. If a firm had a CTO, responsibility was often there. If it did not, it shifted into finance or operations. DORA has since changed that dynamic. Responsibility now sits with the management body, which is held accountable for the ICT risk management framework. It means resilience is now a governance issue with clear ownership at the top of the business.

That changes the conversation both inside firms and with suppliers. Firms increasingly want hard evidence from their providers. They want to understand incident processes, failover arrangements, access controls and recovery expectations in practical terms. They also want to know when documentation was last reviewed, whether controls have been tested – and what would happen if a system failed.

The weakest point is often internal

For many private markets firms, however, the central issue is not whether a major external provider is resilient in principle. It is whether the firm’s own workflows can withstand increased pressure.

Key-person risk is at the heart of this issue. Many firms have built effective processes around capable individuals who know every workaround and hidden dependency. That may keep the machine running, but it is not the same as resilience. If knowledge is not captured and supported by the underlying platform, the process is weaker than it appears, severely increasing dangers when individuals leave or are unavailable.

The same applies once essential work starts moving outside the core platform into spreadsheets, email chains or offline approvals. Control weakens, visibility drops, auditability becomes harder and recovery becomes more uncertain. In private markets, where reporting expectations are high and investor scrutiny remains intense, that is not a side issue. It goes to the heart of whether the operating model is fit for purpose.

That matters particularly in fund accounting, where complexity has only increased. Reporting expectations are higher, structures are more demanding, and firms need stronger control across workflows. A system built for a different era often pushes work outside the main platform at exactly the point where clarity and control matter most. Once reporting becomes a scramble and key numbers are being pieced together manually, the workflow has already become more fragile than it should be.

DORA is the prompt, not the end goal

Operational resilience has often been treated too narrowly as a compliance cost. In practice, stronger resilience usually brings clearer process ownership, better visibility across workflows and faster responses when investors, auditors or regulators ask harder questions. It also reduces key-person risk and makes growth easier to support because the process does not have to be rebuilt every time complexity increases.

DORA will not solve every vulnerability in private markets operations, nor was it meant to. What it does is raise the bar for accountability, testing and supplier oversight. It shines a harsher light on brittle processes that have survived largely through habit and human intervention. It also makes clear that operational resilience depends not just on policies and providers, but on whether the underlying systems and platforms are strong enough to support controlled, auditable workflows under pressure.

That is the real wake-up call. Resilience has to run through the whole operating model, from the infrastructure underneath it to the controls and workflows used every day. That’s the real underlying lesson of DORA that private markets firms need to heed.

Learn more at lemonedge.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • Digital Strategy
  • InsurTech

Author

Simon Pamplin

Published

19 June 2026

Estimated Read time

4Mins

Simon Pamplin, CTO at Certes, on how Quantum computing will eventually force a reckoning with the cryptographic assumptions on which the financial system is built.

Financial institutions hold some of the most sensitive and long-lived data of any sector. Customer identities, decades of transaction records, credit histories, and proprietary trading information must be kept confidential, not just today, but far into the future. The cryptographic systems guarding that data, however, were built for a different era; one in which quantum computing was purely theoretical. That era is ending.

Recent guidance from both the G7 Cyber Group and the UK’s National Cyber Security Centre has made the stakes explicit: financial organisations should complete their transition to post-quantum cryptography by 2035. For institutions managing sprawling, decades-old technology estates, that deadline is less reassuring than it might first appear. Ten years sounds like breathing room. In practice, for organisations of this complexity, it is barely enough.

Quantum Risk: The Threat Doesn’t Wait for the Technology

The most common mistake in discussions about quantum risk is treating it as a problem for tomorrow. The assumption is that until a quantum computer powerful enough to crack modern encryption actually exists, there is no immediate danger. That assumption is dangerously flawed.

Sophisticated adversaries are already operating on a longer time horizon. The strategy, widely referred to as “harvest now, decrypt later,” involves systematically collecting encrypted data today and storing it until quantum computing capabilities catch up. No quantum computer is needed to execute this phase of the attack, only patience and storage.

For the financial sector, the implications are particularly serious. The data being harvested now, customer records, transaction histories, and commercially sensitive communications, could have a useful life measured in decades. By the time it is decrypted, the individuals and organisations it concerns will still be very much affected by its exposure. This is not a theoretical future liability. It is a risk quietly accumulating in the background of every financial institution that has not yet begun to act on it.

Why Updating Financial Systems Is So Hard

The cryptographic methods underpinning most financial infrastructure today, primarily RSA and elliptic curve cryptography, derive their security from mathematical problems that are extraordinarily difficult for conventional computers to solve. Quantum computing threatens to render those problems tractable, potentially dismantling encryption that currently appears robust.

Responding to that threat, however, means working through some of the most technically complex environments in any industry. Core banking platforms were often built 20, 30, or 40 years ago. Cryptographic functions are frequently embedded deep within application logic, firmware, or hardware, in payment terminals, ATMs, and proprietary systems that were never designed with replaceability in mind.

Replacing or updating these components is not a software patch. It can mean recertifying devices, replacing physical infrastructure, or undertaking large-scale application rewrites, all while maintaining continuous service to customers and satisfying stringent regulatory requirements. Progress is possible, but it is measured in years, not quarters.

Rethinking Security from the Data Outward

The quantum threat is also accelerating a broader rethink of how financial institutions approach security architecture. Perimeter-based defences, firewalls, VPNs, and network segmentation have consistently shown their limitations when attackers can move laterally through systems using legitimate credentials or compromised access. Relying on the boundary to keep data safe has a poor track record.

A more resilient approach centres protection on the data itself. By applying strong controls directly to information as it moves across systems, organisations can maintain security even within legacy environments that cannot be immediately upgraded. Sensitive data remains protected regardless of where it travels or which network it traverses.

Alongside this, crypto agility, the ability to swap out cryptographic algorithms and rotate keys without overhauling entire systems, is becoming a fundamental requirement rather than a nice-to-have. The transition to post-quantum standards will not be a one-time event. Cryptographic best practice will continue to evolve, and institutions that have built flexibility into their architecture will be far better positioned to keep pace.

The Cost of Inaction

The financial sector has a well-established capacity to absorb and adapt to technological change. It has navigated the transition to digital banking, the rise of real-time payments, and successive waves of cybersecurity threats. The quantum challenge is different in one important respect: the timeline for harm is already running.

Regulatory expectations are being set now. Adversaries are collecting encrypted data now. And the internal processes required to modernise a major financial institution’s cryptographic infrastructure take years to complete. The gap between when action is needed and when it can realistically be delivered is not wide, and it is narrowing.

Preparation does not demand panic or the immediate replacement of every system. It demands a clear-eyed audit of where sensitive data lives, an honest assessment of which information carries long-term confidentiality requirements, and a commitment to building security architectures that can evolve as the threat landscape does.

Financial institutions that move early will not simply reduce their quantum exposure. They will emerge with stronger, more adaptable security postures and with the trust of customers, regulators, and partners intact.

Quantum computing will eventually force a reckoning with the cryptographic assumptions on which the financial system is built. The question is no longer whether that reckoning is coming. It is whether institutions will be ready when it arrives.

Learn more at certes.ai

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Cybersecurity in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech

Author

Chris Tredwell & Charis Thomas

Published

28 May 2026

Estimated Read time

5Mins

Chris Tredwell, Chief Operating Officer and Charis Thomas, Chief Product Officer at Aqilla, on why the question is no longer whether to adopt AI, but whether processes, governance structures and training pathways are ready for the workforce

Have you ever got into an old car with a Gen-Zer? If they were driving, chances are you wouldn’t have got very far. A recent survey has found that 39% of 14–29-year-olds couldn’t identify an ignition key. Proof, if it were needed, that once technology advances, old ideas are quickly forgotten. This isn’t just happening in cars. The internet and social media have produced their own native generations – people who have never known a world without those technologies.

The same pattern is starting to emerge with AI. That means Gen Z and Millennials are about to experience a similar shift. The first wave of true AI natives will soon enter the workforce – a cohort that has never known a world without AI. 

AI- The New Normal

People’s reactions will largely depend on their experience with AI. But one thing is certain: these graduate and entry-level employees won’t need to be convinced of its value. They’ve already seen what it can do, so if it’s missing, disbelief – or frustration – is likely to follow. It’s a bit like broadband. Here in the UK, it’s simply the standard we all expect. We don’t stop to think about how that connectivity reshaped our lives, helped us work from home or allowed us to stream high-definition media.

Many organisations are still in the early or experimental phases of AI adoption. They might be using the technology to automate basic email inbox management and take meeting minutes. Meanwhile, those further ahead of the curve are exploring more advanced tools and assessing where automation can be safely deployed, particularly for reporting and analysis.

But AI natives won’t see these use cases as experimental. In fact, they probably wouldn’t even refer to them as use cases. It’s just normal, like using a search engine rather than visiting a library to carry out research.

Prompting New Behaviour

Perhaps the biggest difference, however, is where organisations may integrate AI into their existing workflows, AI natives are more likely to structure work around it from the outset.

For them, work tends to start within an AI system, defining the objective clearly, setting constraints, and effectively “briefing” it, before iterating quickly and refining outputs as they go. For AI natives, this kind of prompt-based mindset isn’t a specialist skill; it’s simply how they approach tasks.

This is a fundamental shift. For AI natives, the question isn’t “Should we use AI here?” It’s “Why can’t I use it for this piece of work?” When their expectations collide with more cautious, process-led environments, friction is almost inevitable. Not because one approach is right and the other is wrong, but because both sides are starting from completely different assumptions.

Skills Transfer and Mentoring

But how does the need for AI natives to understand and work through basic manual processes coexist with intuitive prompt-based thinking? Should AI use come with experience-based restrictions in the finance sector? For example, do your three years first, and then you can use the tools.

It’s probably not what AI natives want to hear, but there is logic behind the approach. Learning the manual processes behind automation will enable new recruits to apply the necessary checks and balances to system outputs — putting them in a position to verify data rather than passively accept it.

Without taking this step, there’s a real risk that people will lose the ability to question the outputs they’re working with. That, in turn, has implications for how people are taught. Whether in an educational setting or on the job, that training needs to help AI natives understand the logic behind the systems they’ll be working with.

Lurking in the Shadows 

If AI natives encounter friction when trying to use the technology, there’s a risk they’ll seek informal workarounds. Organisations have seen similar patterns before with personal devices or early cloud adoption. With AI, the risks are more focused on data, traceability, and accountability than on system access and security, though these remain important considerations.

Rather than restricting AI use, organisations are already beginning to reshape it by building oversight into how systems are used. That might mean making the AI’s working assumptions more visible and requiring humans to validate outputs. It might also require AI systems to signal their confidence in those outputs and to request manual checks. Over time, this reduces risk and creates an environment where people can work confidently with AI without losing sight of who is responsible.

This approach also challenges a common narrative. Much of the current discussion around AI focuses on job displacement, but the reality is more nuanced. The issue isn’t a simple replacement of human intuition and experience, but how those qualities evolve alongside increasingly capable systems.

Rather than removing the need for people, this managed shift reinforces it. Greater emphasis is placed on human-in-the-loop models, in which individuals with a deep understanding of AI can interrogate, challenge, and interpret system outputs.

A different starting point

So, what happens next? AI tool adoption, for sure. But it goes far deeper than that. Getting ready for AI natives means shifting to a different starting point – and learning to “live in the prompt”.

As AI natives begin entering the workforce and eventually move into leadership roles, the expectation won’t be that AI is introduced; it will be that it is already there. That shift reshapes how people think, learn, and approach tasks from the outset. It will also change how tasks are conceived, carried out and reviewed. The ability to configure, interrogate and challenge systems will become as important as the ability to interpret their outputs.

For organisations, the question is no longer whether to adopt AI, but whether their processes, governance structures and training pathways are ready for a workforce that already assumes it – and will expect to work that way from day one.

Learn more at aqilla.com

  • Artificial Intelligence in FinTech
  • Data & AI

Author

Rob Demain

Published

22 May 2026

Estimated Read time

5Mins

Rob Demain, CEO at e2e-assure, on why the clock is ticking when it comes to IT/OT convergence for Critical National Infrastructure (CNI) and associated organisations

Many critical national infrastructure (CNI) operators lack the ability to protect their infrastructure despite the UK being subjected to daily sub-threshold cybersecurity attacks, according to the Strategic Defence Review 2025. It’s a situation that the Network and Information Systems (NIS) regulations, introduced back in 2018, sought to prevent. But since its inception, just over half of the operators of essential services have updated or strengthened their existing policies and processes, leaving many woefully unprotected. 

In desperate need of reform, NIS is set to be superseded by the Cyber Security and Resilience Bill (CSRB), which is expected become law later this year, at which point a consultation on implementation proposals will commence followed by secondary legislation and an adjustment period for stakeholders. The bill will broaden the scope to include other organisations deemed critical to the national economy i.e. data centres, Managed Service Providers (MSPs) and critical suppliers. Plus, the government reserves the right to extend those categories still further as part of its ‘future proofing’, which will enable changes to be made to the act to accommodate emerging threats and potential targets. 

New Demands 

All of these new entities will need to comply with the Cyber Assessment Framework (CAF), which lays out expected cybersecurity and resilience outcomes. First published in 2018 to support NIS, it has undergone a number of revisions since, with v4.0 released in August 2025. This version places a far greater emphasis on proactive security and decision making based on real threat intelligence. As well as adding new contributing outcomes on understanding threats and secure software development and support, it also expands the sections on security monitoring and response and recovery, while an entirely new category has been added on threat hunting.   

All of this points to a far greater emphasis on being able to demonstrate assurance and proactively monitor all aspects of CNI infrastructure and that means more scrutiny of both IT and Operational Technology (OT). Until recently, securing OT wasn’t seen as a priority. These systems were chiefly concerned with maintaining system availability and minimising downtime. But their increased integration with IT systems to connect with the industrial Internet of Things (IIoT) and deliver real-time monitoring, for example, are exposing these systems to attack, with threat actors able to move laterally from one environment to the other.  

The threat posed by IT/OT convergence is well known, but it continues to be the Achilles heel of CNI, as revealed by the Volt Typhoon attack. This saw Chinese nation state actors maintain persistence across CNI in the USA since at least 2021 through the use of Living off the Land techniques, illustrating just how insidious and sustained these cybersecurity attacks can be. 

Securing IT/OT Systems 

It’s these types of threat the CAF addresses through its risk and asset management requirements. Organisations must risk assess systems with respect to their dependencies and interactions with other systems such as IT/OT, and document and understand those dependencies. But other complementary frameworks can also be used to map IT/OT system security, such as the ISMS within ISO27001 from an IT perspective and IEC 62443 from the OT side, in addition to ISO/IEC 27019 for process control systems. 

Being able to follow these frameworks will require organisations to increase their security monitoring of both IT and OT and the transparency of their processes. They will need to transition from being reactive to proactive, and become resilient and risk informed, which will mean many will have to change their approach. These are really the only options available to them in this respect if they are to move the resilience needle. 

The first is to decentralise and harden OT systems while keeping them segregated from IT. However, hardening alone can’t keep pace with digital transformation. Many OT assets cannot support multi-factor authentication (MFA) or accommodate rapid patching because they are downtime sensitive. So, surface hardening alone won’t confer the resilience needed long term.  

The second option is to manage IT and OT together by giving everything an identity in a converged environment, but to do that you need to move the monitoring of OT into the Security Operations Centre (SOC). Centralised monitoring allows threats to be detected across both IT and OT networks, for teams to monitor east-west traffic, and to correlate alerts that might otherwise appear unrelated. And it’s this centralised management that will provide the visibility and control needed to improve IT/OT resilience.  

Converged Cybersecurity 

Such a converged SOC doesn’t just offer continuous visibility over IT, industrial control systems (ICS), OT and cloud environs, but also the real-time triage of critical alerts. These might include unauthorised PLC logic changes, unsafe set-point writes, abnormal OT protocol behaviour, lateral movement in ICS DMZs, OT malware,or unauthorised remote access into OT environments. These alerts are then grouped by operational impact, such as whether they present a safety critical risk or could lead to service degradation, so that they can be prioritised. Weekly threat hunts and detection surface validation over distributed environments provide the threat hunting capabilities needed to meet the CAF requirements and the SOC evidences and provides that all important audit-ready compliance mapping to meet the demands of other frameworks too such as IEC 62443, and ISO/IEC 27019. 

Whether standing up a converged SOC internally or outsourcing, this capability is the most efficient way to adapt to the tightening regulations, particularly as we can expect ‘future proofing’ to lead to yet more demands. The emphasis is now firmly focused on the proactive monitoring of both IT and OT systems together, given their growing dependencies, so it makes sense for those organisations in scope – as well as those who could soon be – to begin to move their OT monitoring from the plant and into the all-seeing all-knowing enclave of the SOC.  

Learn more at e2e-assure.com

  • Cybersecurity
  • Cybersecurity in FinTech
  • Digital Strategy
  • Fintech & Insurtech

Author

Adnan Patka

Published

21 May 2026

Estimated Read time

5Mins

Adnan Patka, Enterprise Manager – Blockchain, AI and Web3 at OVHcloud, on why the convergence of blockchain and AI heralds a brighter future for both

Anthropic’s Mythos team recently assessed various LLM technologies, scoring ChatGPT against Gemini, Grok, Claude and others. The metrics included trustworthiness, but also user deception, sycophancy, encouragement of user delusion and co-operation with human misuse.

Clearly, there’s no doubt that AI can be an enormously positive transformational force: the development of AlphaFold, whose founders won the Nobel Prize, showed us that very clearly. But when AI is being assessed for encouraging ‘user delusion’, it’s clear that it still has a PR problem.

In fact, AI has more than just a PR problem: research from McKinsey highlighted that although almost two thirds (64%) of decision-makers reported that AI was accelerating innovation within their organisation, less than half (39%) could actually report positive financial impact on the business as a result.

The AI Trust Gap

AI doesn’t exist in a vacuum, and the interplay of AI with other technologies is beginning to come into focus. We see that blockchain has great potential to complement AI, providing the transparency and trustworthiness that it needs. In a recent study of Web3 professionals, almost three-quarters (70%) said that blockchain had the potential to fill AI’s trust gaps.

But where else are these issues coming from?

It’s overly glib to say that people and businesses don’t always trust AI. What our research told us was that 32% of Web3 experts believe that privacy is AI’s main challenge. Historically, AI systems have been shown to be biased, and data has often been collected by AI systems for training or other uses – sometimes without user permission. One in seven (14%) of the study also reported that they believed AI needed more transparency to be trustworthy.

Blockchain’s Potential for Supporting AI

Blockchain is well-placed to help tackle these issues. Blockchain was originally created as a database with clear traceable links between items, without the need for a single governing authority.

For example, consider an AI system which optimises food supply chains, allowing a supermarket to maintain lower prices. In an environment where people may distrust AI, they may suspect that the system is simply buying lower-quality stock and shipping it more affordably, potentially meaning shorter shelf-lives and less tasty tomatoes! However, blockchain systems can help to track the provenance of these items, proving where the tomatoes were grown, when, by who, and the subsequent shipping and treatment of them, all in a publicly accessible ledger.

Blockchain systems also automatically check the validity of transactions and link them together in a way that makes fraud very difficult. Changing one transaction generally requires changing the entire ‘course of history’ or having significant control (specifically, controlling over half of the nodes) of an entire blockchain, which is usually unfeasible in most large, public blockchains today. 

Delivering Benefits with Blockchain

There are also a number of cases where blockchain can bring functionality benefits to AI systems. For example, as we saw, privacy is a significant concern for AI users, but blockchain systems are able to authenticate securely without exposing user credentials, through self-sovereign identity. This is especially useful where AI systems need to interface with other tools that might not be as trustworthy as the core system.

Blockchain principles can also support data exchange in federated learning systems, where data is shared between different machine learning platforms. Blockchain can establish the validity of this data while still preserving its integrity and user privacy. This means that AI can be trained on data that is consistent and robust – without breaking trust.

There are a huge number of applications for this kind of technology, but unfortunately, it’s not all plain sailing.

Blockchain’s Challenges

The industry is undergoing a huge change today. Interest in stablecoins – a crypto asset which is attached to the value of a more stable asset like a traditional (fiat) currency or other physical commodity – has quadrupled since October 2025. Large, well-established organisations are considering what private and public blockchains can do for them, which could ultimately have a positive and stabilising impact on the regular bear and bull cycle that the crypto market experiences.

However, some more negative parts of blockchain’s legacy persist. Almost two thirds (61%) of our research sample said that concerns about blockchain’s credibility were having an impact on its integration into the enterprise ecosystem. This includes blockchain’s links with the more unsavoury side of cryptocurrency, including cybercriminal activity. Over half (54%) simply said that there wasn’t enough understanding about blockchain’s capabilities and what it could really do, holding IT decision-makers back from making the most of it.

At the same time, this isn’t a one-way street. AI’s capabilities – in particular, automated, agentic AI – can carry out blockchain activities without needing user input. For example, AI can issue transactions or analyse data and summarise it for monitoring systems. There is tremendous potential for AI to support many different parts of the blockchain ecosystem, summarising trends, automatically carrying out tasks, or executing smart contracts without human intervention.

A Mutually Beneficial Relationship

Blockchain and AI have enormous potential to support each other. There are some organisations already starting to integrate blockchain into AI and vice versa, but it will still be a number of years before the relationship is mature. That said, two-thirds of the Web3 experts we surveyed (67%) believed that it’d take roughly one to four years, with only eight percent saying that it might take five to six years.

Indeed, given the enormous benefits that blockchain can bring to AI, not to mention the rapidly increasing acceptance of blockchain technologies across the financial sector in particular, the convergence of these two technologies is almost inevitable. If we can push past the challenges, the future is bright for both blockchain and AI.

Learn more at ovhcloud.com

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Data & AI
  • Digital Strategy

Author

Dan Brightmore

Published

5 May 2026

Estimated Read time

4Mins

Asia’s FinTech ecosystem reached new heights with breakthrough announcements and industry-defining moments that will reshape the region’s financial landscape

Money20/20, the world’s leading FinTech show and the place where money does business, concluded three days of industry‑shaping conversations. High‑impact networking, and packed stages were seen across three days from April 21–23 at the Queen Sirikit National Convention Center (QSNCC) in Bangkok, Thailand.

Money20/20 Asia – 2026

This year’s show welcomed over 4,500 attendees, a 40% increase in its third year. With over 360 speakers delivering over 100 hours of content across four stages. A very busy Money Hall show floor brought together delegates, sponsors and exhibitors from 90 countries. Across the show floor, companies reported a surge in commercial activity, with hundreds of partnership meetings, product demos, and investor conversations driving tangible deal‑flow throughout the three days. 

“Money20/20 Asia is where Asia does business. The record attendance we’re seeing, combined with our relentless focus on quality – of audience, conversations, content, and experience – is translating into real partnerships and outcomes for the leaders shaping the future of financial services in the region. It’s why we are firmly established as Asia’s #1 quality FinTech show.” Danny Levy, Money20/20’s Executive Vice President & Managing Director for Money20/20 Asia and Middle East.

Headline Sessions

AI, cross‑border payments, digital banking, and the accelerating rise of digital assets, including stablecoins, tokenisation, and CBDCs shaped the headline sessions at Money20/20 Asia. Leaders from global banks, FinTechs, and regulators unpacked these themes across discussions on trust in banking, invisible finance, intelligent infrastructure, the future of digital banks, and ASEAN’s sustainability transition. 

“Our goal is not just to digitise finance, but to humanise it,” said Daranee Saeju, Assistant Governor, Bank of Thailand, reinforcing the event’s focus on people‑centred innovation and trusted digital ecosystems. 

These themes came to life across the agenda’s most talked about sessions. On Day 1, Technology, Trust and the Future of Banking in Asia brought together Scarlett Sieber of Money20/20 and Kattiya Indaravijaya of KASIKORNBANK to explore how modern infrastructure and AI‑driven intelligence are redefining trust and resilience in banking. “Uncertainty is a new normal. Banking will exist, but the way banks have to do the business will totally change,” said CEO Indaravijaya, Chief Executive Officer.

Day 2 spotlighted the rapid evolution of digital assets with The Rise of Blockchain and Stablecoin Payment Rails, featuring leaders from Uquid, Pantera Capital, VelaFi, and Trace Finance in a forward looking discussion on next‑generation cross‑border payment rails.

On Day 3, The New Paradigm of Cross‑Border Money Flows and International Expansion saw speakers from Money20/20, TenPay Global (Tencent), Fiserv, and Ant International unpack how Asia’s payment giants are building interoperable, multi‑rail systems to power global expansion. 

Three Days of Expert Inisghts

Along with KASIKORNBANK, the programme saw strong participation from leading global and regional banks, with senior executives from Standard Chartered, Deutsche Bank, J.P. Morgan, Citi, and DBS Bank present across the three days.

FXC Intelligence and Money20/20 presented new findings from The New Era of Asia’s Cross‑Border Payments, underscoring Asia’s rapid growth, rising interoperability, and the convergence of key technologies shaping cross‑border finance. The Press Lunch additionally marked the debut of Money20/20’s new book, The New Intersection of Money – Where TradFi and DeFi Converge, exploring how traditional and decentralised finance are now co‑creating the future of financial services. 

Money20/20 Asia spotlighted five rising startups Boost Capital, TrustPlus AI, Continuum, Eazy Digital, and zkMe, Money20/20’s Startup pitch winner during its Startup Media Panel. The show also hosted the Startup and Investor Park, where APAC startups met global investors and competed for a Golden Ticket to the 2026 Startupbootcamp Sustainability Singapore Accelerator. The program supports founders with pitching opportunities, investor access, and pathways to scale across Asia’s fast-growing fintech markets. 

Policy20 at Money20/20 Asia 2026 brought together more than 80 of Asia’s top policymakers, regulators, and industry leaders to address the rise of sovereign intelligence in global finance. In a closed-door Governors’ and Chairs’ Roundtable, participants aligned on three priorities: embedding regional values into global standards, building harmonised but sovereignty‑respecting cross‑border rails, and advancing intelligence‑led governance through AI and real‑time data. 

After a highly successful third year in Bangkok, Money20/20 Asia has confirmed its return to the Queen Sirikit National Convention Center next year from 27-29 April 2027. 

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Events
  • Host Perspectives

Author

Dan Brightmore

Published

29 April 2026

Estimated Read time

6Mins

Andrew Power, Head of UK&I at Tricentis, on why the right approach to AI can deliver the foundation for more resilient, predictable systems

Artificial intelligence is reshaping software delivery in financial services. Code that once took teams weeks to develop can now be generated and deployed in a matter of hours. This isn’t just about faster delivery; it changes the fundamentals of how software is built and how it behaves in production.

Financial institutions have moved quickly to integrate AI across core systems, from customer operations to anti-money laundering (AML) and software development to capture efficiency and innovation gains. UK parliamentary evidence shows adoption is already widespread, with the majority of firms using AI, and more planning to follow.

But as adoption spreads and becomes more embedded within key systems, so does exposure. Risk is no longer confined to individual defects, but shaped by how quickly those defects can spread across interconnected environments.

AI has removed the limits on how quickly software can be created, but not on how confidently it can be trusted, and financial institutions can now generate and deploy code faster than they can safely validate it.

This creates a new paradox: AI is both accelerating the pace of software change and increasing the speed and scale at which failures can materialise.

Machine-Speed Failure

AI-driven development shortens the distance between change and consequence. Software updates can move through the pipeline from creation to production with significantly less friction. However, this also reduces the time available to identify, flag and contain any issues before they have an impact.

AI-driven software changes don’t just move fast, they scale fast. Unlike traditional failures, these are systemic risks. A single misstep in an AI-generated update can propagate unpredictably.

For financial services, this is especially significant when key systems are deeply interconnected, spanning complex layers of infrastructure, integrations, and third-party services. Even a minor defect can propagate quickly across systems, amplifying its impact.

What would once have been contained can now escalate, cascading across systems and causing wider disruption that affects customers, operations and, in some cases, market activity. In financial services, this is not just a technical issue but a business risk with direct implications for customer trust, regulatory compliance and financial stability. The challenge is no longer simply identifying defects but maintaining confidence in what is being deployed.

This risk is already being felt across the sector. Institutions are accelerating delivery to meet customer expectations and competitive pressures, but often without corresponding advances in validation. Tricentis’ research shows 68% of financial services organisations anticipate outages or serious incidents due to poor software quality.

Regulatory Pressure for AI is Increasing

The issue is also drawing attention from regulators. Earlier this year, the UK Treasury Committee warned that current approaches to AI in financial services are inadequate and could expose customers and the wider system to “serious harm”, highlighting the need for stronger guardrails, clearer accountability and more robust oversight to deploy it safely.

Traditional resilience frameworks were never designed for systems evolving in real time, and AI can no longer be treated as a marginal technology risk. It must become central to how organisations manage and assess resilience.

This marks a shift from software quality being an engineering concern to a board-level issue of operational resilience. If machine-speed change is the new operational hazard, then failure to address it becomes a strategic issue rather than a technical one. With that in mind, financial leaders must acknowledge AI’s dual role as both a driver of risk and a mechanism for preventing it.

AI as Both a Safeguard & Source of Risk

AI also offers the most effective and scalable way to manage the risks it introduces. Advanced AI-driven validation, continuous monitoring and risk-prioritised testing can identify issues earlier than any manual process, helping reduce the likelihood they reach production.

In effect, the same AI that accelerates software creation must now be applied to validation and governance – operating at the same speed and scale.

The same capabilities that facilitate rapid software production can be applied to validation and governance, continuously evaluating system behaviour, detecting anomalies and prioritising testing based on potential business impact, rather than volume. This allows organisations to move beyond rigid approaches and towards more adaptive, responsive quality models that more accurately reflect the way AI behaves.

Instead of relying on standard periodic testing cycles, systems can be validated on an ongoing basis. This enables earlier intervention before issues escalate.

AI can also help organisations better understand the complexity of their own systems. By analysing dependencies across applications and infrastructure, it becomes possible to identify which processes are most critical and where failures would have the greatest impact.

From Acceleration to Control

There is a clear mismatch in how financial organisations approach AI. While many are leveraging AI to accelerate development, far fewer are evolving their validation and governance to keep pace, and it’s in this gap that risk emerges.

This is the “confidence gap”, where organisations can create software faster than they can safely deploy it.

To address this imbalance, firms must treat software quality as a core component of their AI strategy. Development and validation must move forward together. Governance must adapt to continuous, AI-driven change. This requires a move from static testing and coverage metrics to continuous, risk-based validation, where software is assessed in real time based on potential business impact.

If AI is the engine driving software creation, validation must act as the braking system – built in, not bolted on at the end. At machine speed, gaps in control become points of failure. The aim is not to slow innovation, but to ensure it progresses in a way that is sustainable and safe. When validation keeps pace with development, firms can move quickly and competitively, whilst maintaining control over how risk is introduced and managed.

This is a change we are seeing across large enterprises adopting AI-driven quality approaches, where validation, monitoring and governance are increasingly orchestrated together rather than treated as separate processes.

Preventing the Next Outage

The financial sector has already seen how quickly failures can escalate in complex, interconnected environments. In March, an IT error at Lloyds Banking Group exposed the private financial information of nearly half a million customers, prompting the bank to issue £139,000 in compensation.

Such incidents aren’t isolated: over the last two years, more than 33 days of unplanned banking outages have been reported to Parliament, underlining the scale of the issue.

As AI increases the velocity of change, it also raises the stakes for getting it wrong. But the irony is that it also provides the tools needed to prevent these failures from happening in the first place. AI is both contributing to the risk of outages and becoming the most effective way to prevent them.

By applying AI to continuous validation, monitoring and risk detection, organisations can spot issues earlier, understand their potential impact and intervene before disruption occurs. This shifts the focus from reacting to outages to preventing them, and it’s where the paradox becomes constructive. AI doesn’t have to be a source of instability.

With the right approach, it can become the foundation for more resilient, predictable systems. Those that fail risk trading innovation for instability. In the AI era, speed without confidence is simply another form of risk.

Learn more at tricentis.com

  • Artificial Intelligence in FinTech
  • Cybersecurity
  • Cybersecurity in FinTech
  • Fintech & Insurtech

Author

Dan Brightmore

Published

28 April 2026

Estimated Read time

2Mins

Peer-reviewed Physical Review Research paper shows efficient preparation of financial distributions on quantum hardware

Haiqu, a leading developer of quantum middleware, has announced the publication of joint research with HSBC in Physical Review Research demonstrating an efficient approach to encoding real-world probability distributions into quantum circuits.

Quantum State Preparation

Quantum state preparation, the process of encoding classical data into quantum states, is widely recognised as a major bottleneck when implementing many algorithms on hardware. This challenge is particularly relevant for applications such as financial risk modelling and simulation, where complex probability distributions must be loaded onto quantum devices.

The research uses matrix product state (MPS) methods to construct shallow circuits that encode smooth functions, including probability distributions, directly into quantum states. It also introduces a sampling-based workflow that avoids storing the full discretised dataset in classical memory, enabling larger encoding circuits to be generated.

The approach was validated on finance-relevant models including heavy-tailed Lévy distributions, commonly used to capture extreme market events. 

On IBM quantum hardware, circuits up to 25 qubits produced samples that passed standard statistical tests, showing the method can accurately reproduce the probability distributions these models rely on in practice. 

Sampling-Based Workflow

Using the sampling-based workflow, the researchers also executed circuits up to 64 qubits, reproducing qualitative features of the target distributions under realistic device noise and demonstrating feasibility at larger scales. Similar behavior was observed in simulations up to 156 qubits, indicating the approach can extend to substantially larger problem sizes.

“Preparing complex probability distributions efficiently is a key step in many quantum algorithms,” said Dr. Philip Intallura, Group Head of Quantum Technologies at HSBC. “This work shows how they can be implemented with much shallower circuits, bringing practical applications such as financial risk modelling closer.” 

“One of the biggest practical barriers is getting realistic financial data onto today’s quantum hardware. This work shows a scalable path around that barrier and helps move quantum finance workflows from theory toward execution.” Mykola Maksymenko, Co-founder & CTO, Haiqu

Read the paper in Physical Review Research. 

About Haiqu   

Haiqu is an emerging leader in quantum software that supports the notion that near-term, commercially viable applications are achievable with the right software, even on current hardware. Haiqu’s hardware-agnostic software can run applications with up to 100x more operations on current devices compared to competitors. Headquartered in New York City in the United States, Haiqu’s expert team operates from US, Canada, Ukraine, UK, EU, and Singapore, contributing to the company’s mission to make quantum computing practical as soon as possible.

  • Blockchain & Crypto
  • Digital Payments
  • Fintech & Insurtech
  • Infrastructure & Cloud

Author

Dan Brightmore

Published

24 April 2026

Estimated Read time

7Mins

The Innovate Finance Global Summit (IFGS) once again set the tone for London FinTech Week, transforming the historic Guildhall into…

The Innovate Finance Global Summit (IFGS) once again set the tone for London FinTech Week, transforming the historic Guildhall into the beating heart of global financial innovation on Tuesday April 21st 2026. As the flagship event of the week-long programme, IFGS brought together more than 1,500 senior leaders from over 70 countries. Cementing its reputation as one of the most influential gatherings in the FinTech calendar.

A mix of founders, regulators, policymakers, investors and financial institutions filled the venue’s grand halls, all drawn by a shared ambition: to shape the future of financial services. IFGS has long positioned itself as “where decision-makers meet,” and in 2026 that promise felt particularly tangible.

A Global Stage for a Rapidly Evolving Industry

The summit’s scale and structure reflected the complexity of today’s FinTech ecosystem. Across four stages and more than 100 sessions, attendees were immersed in a dense programme of keynote speeches, panel discussions and live demonstrations.

What distinguishes IFGS from many industry events is its ability to convene not just innovators, but the entire value chain. Fom early-stage startups to global banks, Big Tech firms and regulators. Companies such as Google Cloud, Amazon Web Services and Microsoft were prominent throughout the programme, reflecting the growing convergence of financial services and cloud infrastructure. Where policy meets practice and innovation meets implementation.

The agenda in 2026 was firmly aligned with the industry’s most pressing challenges and opportunities. Themes ranged from AI in financial services and open finance to stablecoins, fraud prevention and financial inclusion. Not juts abstract talking points, but the battlegrounds on which the future of finance will be decided.

Opening Momentum: Optimism with a Call for Action

The day began with a keynote from Innovate Finance CEO Janine Hirt, who set a confident yet pragmatic tone.

“We have built one of the most dynamic fintech ecosystems in the world here in the UK,” she said. “But leadership is not guaranteed. It requires constant innovation, investment and collaboration.”

Hirt highlighted the UK’s continued strength in areas such as open banking, while stressing the need to accelerate progress in digital assets, fraud prevention and global competitiveness.

A recorded address from Chancellor Rachel Reeves reinforced this narrative, positioning fintech as a cornerstone of the UK economy.

“Fintech is not just a success story—it is a strategic priority,” Reeves noted. “We will continue to back innovation, support digital assets development and ensure the UK remains a global hub for financial services.”

The political backing on display underlined fintech’s growing importance—not just as a sector, but as a driver of national economic growth.

AI, Data and the Next Wave of Innovation

Unsurprisingly, artificial intelligence dominated much of the conversation. Across multiple sessions, speakers from firms including NVIDIA, IBM and Accenture explored how AI is reshaping everything from customer onboarding to fraud detection and credit decisioning.

What stood out was the shift in tone. The discussion has moved beyond hype to implementation.

“We’re past the experimentation phase,” said a senior executive from Mastercard during a panel on AI in payments. “The focus now is on scaling responsibly. Embedding AI into core systems while maintaining trust and transparency.”

Similarly, Revolut’s leadership team pointed to AI-driven personalisation as a key differentiator in the next generation of digital banking. “Customers increasingly expect financial services to anticipate their needs. AI enables us to deliver that, but only if it’s built on strong data foundations.”

Closely linked to AI was the continued evolution of open banking into open finance and ‘smart data’. Speakers from the Financial Conduct Authority (FCA) and firms such as Plaid and TrueLayer emphasised the importance of interoperability and regulatory clarity.

“Open finance is the natural next step,” said an FCA representative. “The challenge is ensuring it delivers real value to consumers while maintaining robust safeguards.”

Meanwhile, stablecoins and tokenisation emerged as key themes in the context of global competition. Circle, Ripple and several UK-based digital asset firms contributed to discussions around the future of programmable money.

“The question is no longer if tokenisation will reshape markets, but how quickly,” said a Ripple executive during a digital assets panel.

The Exhibition Floor: Innovation in Action

Beyond the stages, the expo hall offered a tangible view of FinTech’s evolution. Established players such as Lloyds Banking Group, NatWest and Santander UK showcased innovation initiatives alongside emerging startups presenting solutions in regtech, embedded finance and payments.

Fintech scale-ups including Checkout.com and Adyen demonstrated new capabilities in cross-border payments and merchant services, while regtech firms, such as Entrust, highlighted advances in compliance automation and fraud detection.

This blend of established and emerging companies is one of IFGS’s defining strengths. It creates an environment where partnerships can form organically. Whether between a startup seeking scale or an incumbent looking to innovate.

The presence of regulators, notably the FCA, added another layer of value. Initiatives such as onsite ‘office hours’ enabled direct engagement between FinTech firms and regulators.

“Dialogue is essential,” one FCA official commented. “Events like IFGS give us the opportunity to hear directly from industry and shape regulation that supports innovation.”

Networking: Where Deals and Ideas Converge

If content is the backbone of IFGS, networking is its lifeblood. The summit’s design – from curated meetings to informal discussions in the Guildhall’s historic spaces – encouraged meaningful interaction.

With thousands of attendees and a high concentration of C-level executives, the event offered unparalleled access to decision-makers. Conversations between banks, FinTechs and investors were constant, with many attendees citing partnership discussions as a key outcome.

Even external challenges—such as a London Tube strike on the day—did little to dampen participation. If anything, it reinforced the determination of attendees to be part of the conversation.

“Nothing was going to keep people away,” remarked one venture investor. “IFGS is where the industry sets its agenda for the year.”

Key Takeaways: Collaboration, Competition and Confidence

Several clear themes emerged from IFGS 2026.

First, collaboration remains critical. The UK’s fintech success is built on strong relationships between startups, incumbents and regulators.

Second, global competition is intensifying. Whether in AI, digital assets or open finance, the race to lead the next phase of financial innovation is well underway.

Third, there is a growing sense of confidence in fintech’s role as an economic driver.

“Fintech is no longer a disruptor, it is the foundation of modern financial services,” said a senior executive from HSBC during a closing session.

Conclusion: A Catalyst for the Year Ahead

IFGS 2026 delivered on its promise as the centrepiece of London FinTech Week. By bringing together the full spectrum of the FinTech ecosystem in a single venue, it created a powerful platform for dialogue, collaboration and innovation.

More than just a conference, IFGS serves as a barometer for the industry’s direction. The conversations held within the Guildhall’s walls will shape strategies, partnerships and policies in the months and years ahead.

As FinTech continues to evolve at pace, events like IFGS are not just important; they are essential. They provide the space for ideas to collide, for relationships to form and for the future of financial services to be defined.

In 2026, IFGS once again proved why London remains at the forefront of global FinTech. And why the world continues to look to the UK for leadership in shaping what comes next.

  • Artificial Intelligence in FinTech
  • Blockchain & Crypto
  • Cybersecurity in FinTech
  • Events
  • Host Perspectives

Author

Mark Talbot

Published

22 April 2026

Estimated Read time

5Mins

Mark Talbot, Director, CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it

Many organisations have long treated improvement as something that arrives as a top-down effort, not something built with the people doing the work. Specialists designed new processes, discussed them in formal forums, and introduced them through large change programmes that often felt detached from daily work. For most employees, ‘transformation’ meant being asked to follow new rules, rather than designing better ways of working.

AI is starting to reverse that pattern. Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day. This shift – which can be described as the democratisation of AI – changes who participates in improving the business. However, it is worth remembering that this shift only works at scale when AI is embedded within a platform that maintains governance, visibility and control. 

Process Improvement in the Hands of Many

Until recently, fixing a broken process often meant filing tickets, waiting for a slot on an IT roadmap, or hoping that a specialist team would eventually address the issue. Creating applications, building automations or redesigning workflows were seen as highly technical tasks. For most employees, waste and inefficiency were things to work around, not things they had the tools or authority to change.

That obstacle is now deteriorating, as long as organizations don’t lose sight of the fact that governance remains essential, particularly in highly regulated environments

AI agents, generative AI and conversational interfaces allow people across the business to shape how work is structured. Within this model, someone in operations can describe an outcome in plain language and have an AI system propose and embed the steps within existing processes. Within a governed platform, non-technical users can adapt existing solutions and automate repetitive tasks without waiting months for central support. At the same time, process insights give developers visibility into what is being built, enabling them to refine, standardise and scale applications more quickly across the organisation.

Data is opening up as well. Data fabrics and related architectures connect scattered information sources into governed layers that a wider audience can access safely. Instead of waiting on static reports, people can access relevant, trusted data when they need it, and use AI to interpret and apply it to their decisions.

When process insight and data access reach this level, best practices move beyond documentation or occasional training. Tools and workflows embed them into daily work, improving performance across the organisation.

Scaling Improvement Across the Organisation

As more individuals understand how their work connects to broader outcomes, organisations unlock a powerful driver of change. Process improvement no longer depends only on a small group of specialists. Employees can recognise when processes are inefficient or risky and have the means to address them at scale, inside an AI platform.

By encoding domain knowledge into AI assistants and digital coworkers within an enterprise-grade AI platform, organisations can share expertise across roles and levels. These AI-powered helpers do not replace professional judgment. They strengthen it. They surface options, highlight inconsistencies and provide context, while humans make the final decision. Over time, each interaction becomes both a learning moment and a new piece of institutional knowledge that organisations can capture and reuse.

In this model, process improvement is no longer episodic or confined to formal transformation projects. It becomes part of everyday work, inside a platform with AI tools that provide real-time feedback and recommendations.

AI, Noise Reduction, and Better Oversight

This shift raises a key question: if AI platforms make analysis, decision support, and process design more accessible, what happens to deep expertise?

There is a concern that easy access to AI advice might weaken people’s understanding. If answers are always a prompt away, will teams still develop the knowledge that comes from working through complexity? If people follow AI suggestions without grasping the logic, how meaningful can human oversight really be?

Over-reliance on instant guidance can create only surface-level competence. People may treat AI outputs as instructions rather than as inputs to their own reasoning.

On the other hand, used well, AI can create more room for expertise, not less.

By handling repetitive tasks and routine decisions, AI reduces the volume of low-value work that consumes people’s time. Teams can then focus on exceptions and refine how they make decisions. Instead of dealing with every routine request themselves, they can focus on work where context and experience matter most.

When AI removes more of the routine burden, teams have more capacity to focus on judgement, process design and oversight. That helps build expertise while keeping improvement connected to the wider goals and governance of the business.

Shaping AI, Not Just Living With It

As organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. AI is moving from something that happens to the workforce to AI being something that is built and refined with the workforce.

Organisations should treat people as partners in shaping AI, rather than as operators of automated systems. When AI platforms can be combined with process visibility and human judgement, employees can have an outsized effect on the systems around them. They can influence how work is structured and how decisions are made. In that sense, AI redistributes who participates in designing better ways of working, and creates an opportunity to anchor that shift in thoughtful design and human expertise.

Learn more at appian.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Strategy
  • Fintech & Insurtech
  • People & Culture

Author

Daniel Ehnhage

Published

20 April 2026

Estimated Read time

5Mins

Daniel Ehnhage, Head of AI Transformation at Unit4, on why those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

As the head of AI transformation, it might sound counterintuitive to suggest that artificial intelligence is not the most important part of my work. It makes a significant contribution to the radical change we are looking to achieve, but the technology itself is only about 10% of the solution. A significant part of the planning and investment must be based around addressing issues like the integration of siloed information systems, the building of the organisational capability required to adopt AI safely and finding the right business case. The key is to understand that adopting AI is not only about improving existing processes – it’s about gradually reshaping how we work in a sustainable way. The goal should be phased, practical improvements that build maturity over time.

This can be daunting for any organisation that has well-established operating practices. It requires a deliberate shift from problem‑solving to rethinking how value is created across the organisation. AI is far more capable and can empower your teams to find new solutions such as gathering more intelligence about market opportunities to improve productivity and decision making. The focus should be on enabling internal teams to work smarter through safe, responsible AI adoption. If your organisation is prepared to embark on such change, you must recognise AI becomes most powerful when you bring the right data together. Today, many organisations, including ours, are still maturing in this area. Successful adopters of AI prioritise building data readiness step by step so AI can create real value without overpromising.

Obviously, the role of AI transformation then becomes much broader with the added challenge of having to implement change without disrupting existing business performance. Consequently, there are some key areas where organisations must focus their attention, beyond ensuring they pick the right AI tool…

Structural Change – Put the AI Board in Place

    AI transformation evolves how organisations work. It does not replace everything we humans do today. The goal is to focus on practical, high‑value use cases that improve productivity, quality, and employee experience without creating disruption. A widely debated expression of this change is the concern that AI will replace human employees. Personally, I think this lacks imagination around the positive impact that AI can have on a workplace. Yes, it may reduce the number of repetitive, mundane tasks, but more importantly it will create new ways of working and collaborating.

    However, given how rapidly the technology is moving it is critical your organisation puts the right safeguards in place and agrees policies about ethical usage. That requires adoption of a cross-functional AI Board to provide a framework for embracing AI which will manage the impact of the structural change. This provides focus for your organisation’s approach to AI. The goal should be to agree which tools offer the most benefit for your teams and concentrate on exploiting use cases that will deliver the most benefit.

    The AI Board should be responsible for establishing the governance structure to help the IT and cybersecurity teams to ensure the use of AI is not creating new vulnerabilities. It should provide clarity and safeguards so employees can use AI confidently and responsibly. Our goal is to enable safe experimentation – not restrict innovation.

    People Change – Enabling Collaboration and Experimentation

    The ambition should be to get employees excited about the potential of AI to open up new ways of working that can lead to rewarding opportunities and exciting new challenges. Indeed, it is widely accepted that helping your people to accommodate the change is the biggest challenge you will face, taking up about 70% of the time required to implement the technology. This is because successful implementations depend on collaboration between distinct teams, which in turn depends on breaking down barriers, both for individuals and teams.

    For example, imagine being able to use AI to analyse data from diverse systems such as customer service, product development and marketing to identify new opportunities to support customers.

    Integrating these data sources could be seen as interfering with distinct job functions, so for all employees it is critical to educate them on what will be expected of them and a good start point is explaining how they will be measured. It could include simple measures such as demonstrating usage of AI tools, but if an organisation wants employees to adopt the technology it is also important to empower them through training.

    With the right support, employees will want to experiment, which should also enable them to understand use cases for AI in their work and the competencies they need to develop. This can be achieved through opportunities for cross-functional teams to explore new ways of working and innovating and should be encouraged by senior leaders. It is crucial they set the right tone, support initiatives, celebrate successes and listen to employee feedback.

    Business Change – Building the Right Business Case

    The business case is not just about saving money to solve a specific problem. It is too easy to look at the saved hours and productivity gains from adopting AI as the sum total of the investment costs you must deal with. There are a number of internally focused requirements that you must build into your thinking about AI transformation. There will be costs around the integration work to enable AI to access data from disparate systems. Competence development must be a top priority. Time must also be allocated to the process of change management and how it may disrupt existing business processes. Security must be a top consideration. These are all internally focused tasks, but you must look at them if you are to capitalise effectively on your AI investment.

    It is tempting to become overly excited by the potential of AI as a technology, and certainly it will bring dramatic change to organisations in the years to come, but having experienced the factors necessary for successful transformations, it is absolutely critical senior leadership teams approach AI-enabled change with cool heads and clarity on what they want to achieve. Above all, they must remember success is not dependent on the implementation of the technology, but predominantly on bringing employees with them on the journey. Many commentators talk about the rise of AI-first organisations. Those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

    Learn more at unit4.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Vincent Guillevic

    Published

    16 April 2026

    Estimated Read time

    5Mins

    Vincent Guillevic, Director of Fraud Labs at Entrust, argues companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers

    Identity verification and tackling fraud began as a face-to-face process, built on human trust. Opening a bank account involved meeting a banker in person and from there, trust was established because both parties could see and interact with each other directly in branch.

    Fast forward to the digital age and a lot of services have moved online. Identity verification has therefore shifted from in-person checks to remote identity verification. Today, we’re in an era where identity is now central to every interaction we have online.

    Fraud has followed the same trajectory. Much like a burglar would test every possible entry point rather than just the front door, fraudsters probe every stage of the customer journey. They look for weaknesses at onboarding, during login, and throughout ongoing transactions and data requests.

    That challenge has intensified in recent years. AI has given fraudsters faster, sophisticated and scalable tools. Deepfakes can bypass checks, AI‑generated documents can appear real, and phishing and impersonation attacks can now be automated at scale.

    Once a fraudster gains access to a legitimate account, the damage escalates quickly. Global losses from account takeover (ATO) fraud were projected to reach $17 billion in 2025, up from $13 billion in 2024. While the underlying intent of fraudsters seeking the weakest point of entry, the breadth, speed and sophistication of modern attacks have.

    Identity Fraud Patterns Across the Customer Lifecycle

    Fraud can occur at any stage of the customer journey. From verifying identity at onboarding to securing connections and fighting fraud in everyday transactions. Each stage introduces its own risks, and attackers adapt their tactics based on where value can be extracted most efficiently.

    In 2025, patterns showed a clear distinction between industries targeted for new account fraud and those targeted for account takeover fraud. Businesses that offer immediate incentives such as promotional offers or sign-up bonuses are primarily targeted for new account fraud. In contrast, businesses where accounts accumulate long-term financial or data value face higher levels of ATO.

    Industries built around sign-up incentives or instance access experience most fraud at onboarding. For instance, in crypto, 67% of fraud attempts occur during account creation, largely driven by sign-up incentives. Vehicle rental follows a similar pattern, with 67% of fraud taking place at onboarding as attackers use fake identities to gain short-term access to high-value assets. In these sectors, low-friction onboarding creates opportunities to harvest incentives or establish accounts that later become avenues for future money laundering.

    Account takeover fraud reflects a different strategy. Rather than creating fake accounts, attackers focus on compromising established accounts using tactics such as stolen credentials, phishing, malware, or social engineering. Entrust data shows this is most common in industries where accounts hold enduring value. In payments, 82% of fraud attempts occur after onboarding, while in professional services the figure is 62%. High-value, long-standing accounts are attractive because they enable fund transfers, loans, and access to identity-rich data, making them more valuable than newly created accounts.

    These patterns highlight two critical realities. First, organisations can no longer optimise for one type of risk at the expense of another. Defending a single point in the journey inevitably leaves gaps elsewhere. Second, fraud has become highly professionalised. Modern fraud operations are organised, strategic, and adaptive, moving toward the highest rewards and the weakest controls.

    Prevention Must Span the Entire Journey

    If fraud can occur at any stage, prevention must operate at every stage. Organisations that implement robust, lifecycle-wide identity strategies save an average of $8 million per year in fraud-related costs. These savings come from detecting threats earlier, more accurately, and beyond a single checkpoint.

    There are three areas where that lifecycle approach needs to be strongest.

    Get onboarding right

    Onboarding is the first opportunity to establish genuine trust. Strong Know Your Customer (KYC) or Know Your Employee (KYE) processes combine document verification with biometric checks such as face recognition or fingerprint scanning to confirm that the person applying is who they claim to be. Liveness detection adds a further layer by distinguishing real users from synthetic identities and deepfakes, which are linked to approximately one in five biometric fraud attempts.

    With strong identity verification at onboarding not only reduces immediate fraud, but also limits the downstream damage caused with fraudulent accounts.

    Secure existing accounts with continuous authentication

    Verifying identity once is no longer sufficient. Continuous authentication, combining multi-factor authentication with biometric re-verification like facial recognition, allows businesses to protect established accounts without creating unnecessary friction for legitimate users.

    Crucially, it enables authentication requirements to adapt dynamically as risk levels change, rather than applying the same static check regardless of context. In payments businesses, where most fraud targets the authentication process itself, this adaptability is key to mitigating attacks before losses occur.

    Monitor behaviour in real time, not just identity

    Device intelligence and behavioural signals make it possible to assess risk based on how users interact with services, flagging unusual login patterns, device anomalies, or out-of-character transactions.

    As AI-driven fraud becomes more sophisticated and convincing, behavioural indicators provide another layer of ongoing fraud detection. Focusing monitoring on high-risk actions, rather than only high-risk identities closes a critical gap in traditional defences.

    The Window of Opportunity

    Fraud has always followed the customer journey. What has changed is the availability of advanced technology capable of tracking, analysing, and responding to threats at every stage. The key question for organisations is whether these capabilities are deployed as a connected strategy or left as isolated controls with gaps in between.

    Companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers, and preserve the trust that underpins long-term digital relationships.

    Learn more at entrust.com and meet the team at IFGS in London on April 21

    • Artificial Intelligence in FinTech
    • Cybersecurity
    • Cybersecurity in FinTech
    • Fintech & Insurtech

    Author

    Dan Brightmore

    Published

    15 April 2026

    Estimated Read time

    4Mins

    Read in brochure format

    FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial…

    FinTech Strategy is back with more key insights from the industry experts and thought leaders shaping the future of financial services.

    Read the latest issue here

    Vibrant Capital: Scaling AI on Main Street

    Our cover star Shadman Zafar, Founder & CEO of Vibrant Capital, is building a CIO-led model for enterprise transformation. Vibrant Capital is an operator-led investment and company-building platform focused on scaling AI in the real economy. “We don’t spray investments across hundreds of AI startups. We curate a portfolio with purpose – selecting companies that solve the real mission-critical problems CIOs face in scaling AI adoption.”

    FNB: Redefining Data Science in Commercial Banking

    We also hear from Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge. “Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful.”

    Virginia Farm Bureau: An Enterprise CIO’s Journey

    Shifting focus to the world of insurance at the Virginia Farm Bureau, we spoke withan Enterprise CIO at a complex mission-driven organisation. As he approaches retirement, Patrick (Pat) Caine reflects on his career as a CIO and the centennial of an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. “In my role as CIO, I’ve always been that person who connects the dots between business needs and technology execution. Virginia Farm Bureau is digitally relevant, collaborative, and well‑positioned for the future.”

    Mastercard: Protecting Trust in the Digital Economy

    Michele Centemero, EVP Services at Mastercard Europe explains why promoting awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems, will be critical in reducing the impact of scams and protecting trust in the digital economy. “The combination of AI, robust identity controls and open banking can help protect consumers from scams, whether across card and account‑to‑account payments or in fraudulent account openings.”

    Thales on AI Security: How FinServ’s Budget Priorities Signal a Boardroom Shift

    Todd Moore, Global VP – Data Security Products at Thales, reveals why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation. “Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring.”

    Paymentology: The First Live AI-Agent Payment Is a Test for Credit Infrastructure

    Thomas Benjaminsen Normann, Product Director at Paymentology, dissects the future for agentic payments and the progress still to be made. “Agentic payments demand something more granular: a clearer account of who or what acted, under what limits, and with what right to create a liability on the customer’s behalf.”

    Also in this issue, we hear from Publicis Sapient, on why asset managers must redesign their enterprise for AI-driven decision intelligence; learn from Bitpace why the most resilient payments infrastructure will be the one with the most adaptability; rank the AI maturity of 12 of the largest payments networks in the latest Evident AI Index; and round up the key FinTech events and conferences across the globe.

    Enjoy the issue!

    Read the latest issue here

    • Artificial Intelligence in FinTech
    • Blockchain & Crypto
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Payments
    • Embedded Finance
    • Fintech & Insurtech
    • InsurTech
    • Neobanking

    Author

    Michele Centemero

    Published

    14 April 2026

    Estimated Read time

    5Mins

    Michele Centemero, EVP Services, Mastercard Europe on why promoting awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems, will be critical in reducing the impact of scams and protecting trust in the digital economy

    As our world becomes faster, smarter and more interconnected, scammers are evolving in parallel, developing increasingly sophisticated ways to exploit people’s trust. By harnessing new technologies and behavioural insights, they are refining their methods to appear ever more credible and convincing.

    While attacks on systems continue, today’s fraudsters are increasingly targeting people, often relying on psychological manipulation to achieve their goals.

    Understanding Social Engineering

    Many modern scams fall under the umbrella of social engineering,which isthe use of deception and emotional manipulation to influence a person’s behaviour.

    In the digital world, cybercriminals use these tactics to build false trust, create urgency or fear, and ultimately trick people into sharing confidential information or taking actions that can cause financial harm to themselves or their employer.

    Recent European industry data indicates that social engineering-related fraud and authorised push payments (APPs) – where victims are tricked into sending money to fraudsters posing as legitimate payees – now account for a growing share of overall scam losses[1].

    This is directly impacting a growing number of consumers, with the majority of people saying they’ve experienced some form of scam or fraudulent attempt to capture their personal information highlighting why awareness and vigilance are critical for people of all ages.

    Education is the First Line of Defence

    Protecting consumers and businesses from malicious activity is a priority, and it starts with awareness. When people understand how scams work, they’re more likely to spot the warning signs before it’s too late and be empowered to protect themselves against fraudsters.

    Three of the most common social engineering scams to watch out for are:

    • Imposter fraud – Criminals pose as trusted organisations (such as banks, retailers, or government bodies) to pressure victims into sharing personal or financial details. Research indicates over half (53%) of European consumers have been targeted via phone or voice call scams, with social media scams affecting around two in five people, and tech support impersonation tricking roughly one in three.*
    • Phishing – Fraudulent emails, texts, or messages that are designed to look legitimate, often urging immediate action like clicking a link or resetting a password, leading victims to disclose sensitive information or install malicious software. Nearly three in five (58%) have received phishing emails or fraudulent text messages (63%) and QR code scams are on the rise, impacting nearly a quarter of Europeans.*
    • Romance or honeypot scams – Scammers build emotional relationships over time, gaining trust before exploiting it for financial gain. These types of attacks are also widespread, with one in four people (24%) encountering fake profiles, requests for money, or online relationships that lead to financial exploitation. These scams hit younger generations hardest, with 40% of Gen Z and 35% of Millennials affected, compared with 21% of Gen X and 11% of Boomers.*

    How Businesses Can Protect Consumers from Scams

    With fraudsters increasingly using AI to commit more sophisticated, larger scale attacks, businesses and banks should also consider how they deploy technology to protect customers from bad actors.

    The combination of AI, robust identity controls and open banking can help protect consumers from scams, whether across card and account‑to‑account payments or in fraudulent account openings.

    Looking at identity controls specifically – take the example of continuous identity verification, a fraud prevention measure that verifies the user is who they claim to be throughout the entire lifecycle journey. This helps to prevent scammers from opening or taking over accounts to apply for credit, create ‘mule’ accounts or impersonate others.

    Behavioural biometric data is often used as part of this and can be used to analyse how a user interacts with their device – from typing patterns to on‑screen movements – to flag unusual behaviour.

    More in depth, AI powered transaction analysis can also help banks and financial institutions to stay ahead of payment threats. It provides banks with the intelligence needed to detect and stop payments to scammers, using AI and a network-level view of account‑to‑account transactions to enable intervention before funds leave an account.

    Staying Ahead of an Ever-Evolving Threat

    As social engineering tactics continue to evolve, staying ahead requires a combination of intelligent technology, consumer education, and proactive action from businesses and financial institutions.

    While no single measure can eliminate risk entirely, greater awareness, stronger collaboration and data-sharing, and continued innovation of payments ecosystems will be critical in reducing the impact of scams and protecting trust in the digital economy.

    *Source: This study was conducted by The Harris Poll on behalf of Mastercard from September 8 to September 25, 2025, among 5000+ consumers in the following European markets: EUR: France (n=1,005), Germany (n=1,002), Italy (n=1,016), Spain (n=1,005), UK (n=1,004)

    Mastercard: Transforming the Fight Against Scams

    Innovation – Our advanced AI-powered Identity insights examine digital footprints and assess unique patterns to detect risk and flag suspicious activity indicative of scams.

    Collaboration – We collaborate across industries, partners and organizations worldwide to secure the digital ecosystem, ensuring payments are safe for all. Combating the growing threat of scams demands a collective effort.

    Education – We work with and through our collaborators to provide knowledge and tools that help people protect themselves and their loved ones from scams, while also working to destigmatise the experience of being a victim.

    • $12.5bn in losses from U.S. consumer reported online scams in 2023
    • $486bn in global losses from scams and bank fraud schemes in 2023
    • 22% YoY growth in U.S. consumer scam losses suffered in 2023

    From sender to recipient, we vigilantly monitor accounts and transactions for any elevated scam risk

    Identity insights – Provides actionable identity insights and risk scores for businesses to improve identifying their good customers from the scammers creating “mule” accounts or impersonating someone else with a false identity.

    Transaction patterns – Flags suspicious activity across the money movement flow to prevent payments to scammers before it is sent through the real-time analysis of transaction elements.

    Account confirmation – Enables account validation to confirm account ownership and validate identity details in real-time through our open banking capability, which draws on the safe exchange of consumer-permissioned data to facilitate frictionless and secure payments.

    Learn more at mastercard.com

    [1] Joint EBA-ECB report on payment fraud: strong authentication remains effective, but fraudsters are adapting

    • Artificial Intelligence in FinTech
    • Cybersecurity
    • Cybersecurity in FinTech
    • Digital Strategy
    • InsurTech

    Author

    Todd Moore

    Published

    Estimated Read time

    4Mins

    Todd Moore, Global Vice President, Data Security Products at Thales, on why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation

    Financial Services organisations are responsible for some of the biggest growth in the global economy. Equally, they’re some of the most vulnerable. Like many other sectors, they’re racing to embrace AI, but with adoption comes new security risks.

    According to Thales’ Data Threat Report: Financial Services Edition 81% of FinServ organisations are now investing in GenAI-specific security tools, with nearly a quarter using newly allocated budget. This surge in funding marks a turning point: AI security has moved from being an IT concern to a boardroom priority.

    The fact that new budget lines are being carved out specifically for AI security signals a fundamental shift in corporate strategy. Boards increasingly recognise that protecting AI systems is as critical as safeguarding payment rails or core banking infrastructure. For an industry built on trust, resilience, and regulatory compliance, this investment wave shows how central AI has become to both risk management and competitive growth.

    Balancing AI Innovation and Security

    While FinServ organisations are aware of the security risks AI poses, they’re also seizing upon the opportunities it presents. The report has found that in 2024, FinServ businesses outpaced the broader market in AI deployment, leading in enabling employees to use AI and ahead in AI integration, which has continued into 2025. Additionally, 45% say they’re in the ‘integration’ or ‘transformation’ phases of their GenAI journey, compared to just 33% across wider industries.

    AI’s ability to accelerate services, automate processes, and analyse data at scale makes it an exciting prospect, especially in the financial sector. This makes securing AI systems a priority for FinServ organisations, with increased GenAI integration reflecting developing organisational maturity and progress beyond experimentation.

    The Risk

    Yet the scale of opportunity is matched by the scale of challenge. AI systems require vast amounts of structured and unstructured data to conduct analysis and make recommendations.

    For FinServ organisations, this often includes highly sensitive customer and transactional information, proprietary algorithms, and records bound by strict regulatory oversight. The risk is not only about whether AI systems themselves are secure, but whether the data they’re working from is accurate, as well as whether their adoption inadvertently creates new routes to data exposure and exfiltration.

    Businesses need a clear strategy to fully understand how AI models are operating within their IT infrastructure, the applications they’re interacting with, and the data they’re accessing and pulling from.

    The Response

    Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring. Newly allocated budgets for AI security, with nearly a quarter of FinServ firms making such investments, show how central AI has become to board-level strategy. These investments move firms beyond reactive fixes to proactive frameworks that evolve with the technology. AI security is no longer just an IT concern, it’s a strategic priority requiring collaboration between security, compliance, and business leaders. By factoring risk into early planning, organisations can align innovation with responsibility and build resilience for the long term.

    Pioneering AI Security

    Building on investment in AI-specific security is only the beginning. As scrutiny intensifies, the firms that will lead are those that treat AI security as integral to business strategy, not a bolt-on layer. Success will require visibility into how models behave, continuous validation against emerging risks, and adaptive controls that evolve with the threat landscape.

    The financial services organisations that embed these safeguards into their core infrastructure will protect sensitive data as well as setting a benchmark for resilience and trust in an AI-driven economy. By making AI security a boardroom priority today, these firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation.

    Thales: AI is the New Insider Threat 

    Thales 2026 Data Threat Report Finds 70% of Organisations Rank AI as Top Data Security Risk

    Data security has taken centre stage as the success of enterprise AI initiatives increasingly hinges on consistent, controlled access to proprietary organisational data sources. The 2026 Thales Data Threat Report examines the complex calculus that organizations must undertake to enable innovation while securing their most valuable asset – their data.

    This research was based on a global survey of 3,120 respondents fielded via web survey with targeted populations for each country, aimed at professionals in security and IT management. 

    Read the Report

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Lee Fredricks

    Published

    10 April 2026

    Estimated Read time

    6Mins

    Lee Fredricks, Director – Solutions Consulting, EMEA at PagerDuty, on why technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability

    Technology leaders should see 2026 as a time for operational resilience to shift from ambition to accountability. In 2025, too many cloud services outages and disruptions took place across the public and private sectors, and now regulatory, technological and cultural pressures are converging to say that enough is enough.

    Outages often translate into broader repercussions for the organisation, including revenue impact, customer churn, share price pressure and potentially regulatory reporting obligations. Operational metrics must now be discussed alongside financial KPIs at the board level. C-suite leaders understand accountability, especially within the very regulated financial sector.

    DORA’s First Birthday

    It’s now been one year since the implementation of the Digital Operational Resilience Act, or DORA, introduced by the EU to strengthen the digital resilience of financial institutions. By now, organisations have had time to consider moving from mere compliance to creating a competitive edge from their investments.

    Enterprise tech leaders are in the middle of a balancing act. They’re managing ongoing modernisation and transformation initiatives while navigating multi-jurisdictional regulatory scrutiny. At the same time, they face constant pressure from the board and must meet evolving customer needs—all competing for immediate attention. The stakes have never been higher. Operations teams are no longer viewed as a back-office IT function. Their success in keeping the organisation running and driving revenue is now a board-level concern.

    For organisations today, IT is business delivery.

    A year of DORA has seen organisations make the shift from focusing solely on mere compliance to setting meaningful demonstrable testing, third-party risk visibility and strictly mandated incident reporting timelines. Financial firms have lessened their exposure to risky situations. Payments providers aren’t only reliant on a single cloud region or SaaS supplier, or unable to provide evidence of real time incident response efforts and auditable logs after a disruption.

    One benefit of these overall systemic improvements is enhanced supply chain accountability. Financial institutions and their technology partners are both liable for potential penalties and reputational risk, which makes it highly critical that they can prove their resilience capabilities.

    Nevertheless, operational resilience is a continuous discipline. A fragmented incident response can expose firms to regulatory and reputational risk again and again if not addressed systemically. As such, many organisations are looking toward AI agents as part of a move towards ‘no-touch’ operations.

    From Autonomy to Self-Healing

    Under set policies, autonomous agents can handle incident response and operational tasks, such as detection, triage and remediation. AI agents deployed in operations may become the backbone of L1 (first contact) and L2 (more skilled) support. Contrast this with the traditional, reactive, ticket-driven model of IT. The industry can move much faster and with a higher successful close rate. Leveraging intelligent automation reduces mean time to detection/resolution and KPIs around lower incident volumes reaching L3. Additionally, it can lead to improved service availability percentages. Well integrated agents that actually support existing operations teams also help manage the issues around talent shortages faced by many organisations.

    A typical incident lifecycle with agentic processes includes several stages depending on the model, but can be summarised as: Anomaly detected, correlated with recent deployment, a remediation script triggered and a human notified if set thresholds were breached. Such no-touch operations are golden in any sector, but particularly with industries such as digital banking and retail, where peak traffic periods demand near-instant response and poor customer experience is a powerful motivator for users to instantly change providers.

    IT Standardisation

    In addition, consider standardisation as part of strategic infrastructure best practices. There is a role for central operations clouds and operational ‘golden paths’ as solid foundations for reliable operational scale and dependability. Standardisation enables consistent, scalable operational excellence especially across large, distributed enterprises. ‘There is one way and it is the right way’ can be a great time and stress saver for operational teams – particularly if a regulatory notification and clear evidence is required.

    For example, a global bank might define a single golden path for deploying customer-facing applications with pre-approved monitoring, incident response workflows, and regulatory reporting templates built in. In an outage, teams follow the same process and automatically capture the evidence required for regulators, avoiding confusion, delays, and compliance risk.

    All of these possibilities take us to an exciting new place for an evolved set of developer and operational roles. When organisations enable AI to reshape daily engineering work away from manual firefighting and low-value work it frees headspace and time for developers and engineers to move into more architectural thinking and intelligent oversight of automated systems. These augmented teams will be empowered to manage simple situations instantly and devote more time and attention to the more difficult issues – the edge cases and the strategic necessities.

    Enabling Agentic AI

    Using another lens, businesses with agentic IT operations capabilities support their current talent, extending their reach and the speed of their response. The winning organisations will be those who deploy agents strategically, freeing up humans for that higher-value work – i.e. L3 expert support – and setting new standards for operational excellence that customers can rely on. Ideally this means making commensurate investment in existing people, training and organisational change management. A culture of continual upskilling and forecasting that points humans to where they make the best impact will be just as important as the autonomous tech tools working alongside them.

    Autonomous agents allow many new services, and one of those can be described as self-healing operations. This evolution of the operations world is where predictive detection, automated remediation and embedded resilience all coalesce. With an autonomous process of testing, maintenance and remediation, organisations can focus on finely measuring improved customer trust. They can also enjoy the productivity and revenue benefits of high business continuity and availability.

    AI is still a new technology, and many are legitimately concerned with the concept of autonomous agents. There is a need for clear guardrails, audit trails and explainability in automated remediation, and many technology partners have invested in their ability to support across these areas. Moreover, firms must maintain direction with policy-driven automation rather than uncontrolled autonomy, particularly in regulated industries.

    Mandate Operational Excellence

    This year is very likely to reward organisations that treat operational resilience as core to their business strategy. Those investing in automation, standardisation and governance will set the pace for their industries in an AI-enabled and increasingly autonomous world.

    Regulators are already expanding their scrutiny and reliability expectations beyond financial services firms. Across the world, jurisdictions are increasingly looking to strengthen their economies and digital services in particular through resilience and cybersecurity measures. At the same time, agentic operations, and the organisational performance benefits they support, will rapidly become table stakes technology in all sectors. Inevitably, customers will judge brands on digital reliability as much as price or product features when evidence of outages are a click or a headline search away.

    Start now. Audit internal incident response maturity, review the potentially complex web of third-party IT dependencies and identify where automation makes clear business sense. While resilience is an investment in compliance, it is also critical to ensure customer trust and future stability.

    Learn more at pagerduty.com

    • Artificial Intelligence in FinTech
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech
    • Infrastructure & Cloud

    Author

    Jamil Jiva

    Published

    8 April 2026

    Estimated Read time

    5Mins

    Jamil Jiva, Global Head of Asset Management at Linedata, on why the next chapter of AI-driven finance will be shaped not just by technology, but by creativity

    Beyond Data: Where AI Finds Unexpected Inspiration

    The discussion about training AI largely focuses on concerns that accessible, human-generated data is limited and may soon run out completely. If this is the case, how can technology that depends on a seemingly endless stream of inputs to iterate, test, and adapt deliver the results we expect? AI relies on structured, high-quality data to thrive, but what happens when we run out of spreadsheets and financial models to train AI? We need new data sources to ensure it continues to learn, adapt, and deliver accurate insights. Video games stand out as offering some of the richest, most expansive, and complex environments for AI training.

    At first glance, video games and financial operations seem to belong to entirely separate worlds. However, AI connects these domains, with models leveraging virtual-world training to tackle real-world financial tasks. Financial documents such as credit agreements and tax returns are often convoluted, unstructured, and labour-intensive to process. Therefore, AI designed to interpret such data must possess strategic reasoning, real-time adaptability, and advanced pattern recognition. So, could video games be the ideal training ground?

    Contrary to popular belief, gameplay can significantly improve how people think, learn, and solve problems. The abilities required to excel at video games closely reflect the skills AI systems must acquire today.

    Levelling Up: What Virtual Worlds Teach Machines

    Practice leads to proficiency, a principle that applies to both humans and AI. Interestingly, many of the most significant advances in AI development have emerged not from conventional data training, but from taking creative approaches. Games push AI to emulate human thinking and sharpen its statistical intuition.

    These game-trained models are neither expensive nor heavily reliant on resources, and they sidestep the issue of data scarcity. As a result, they are actively shaping the future of financial intelligence. The examples below offer a clear demonstration of the potential of gameplay.

    Virtual Economies: Lessons from World of Warcraft

    World of Warcraft, with millions of players interacting in an immersive and dynamic world, features an economy that closely mirrors real-world financial systems, complete with inflation, supply and demand cycles, and fraud risks. The game even inspired one of the most renowned epidemiological studies: when the in-game ‘Corrupted Blood’ plague spread unpredictably, scientists used it as a model for real-world pandemic simulations.

    Financial models depend on vast, interconnected data networks, much like the economy in World of Warcraft. Organisations employ AI to continuously monitor patterns, detect anomalies such as fraud or misstatements, and optimise data extraction for financial reporting, mirroring the way AI analyses virtual economies.

    Urban Chaos: GTA V and Real-World Simulation

    While Grand Theft Auto (GTA) V is famous for its open-world chaos, researchers have leveraged its traffic systems and non-player character behaviours to train AI for applications such as self-driving cars, crime pattern recognition, and urban planning. At its heart, GTA provides a platform for AI to process vast amounts of unstructured data in real time.

    Similarly, financial institutions manage millions of data points from a wide range of sources. Their AI tools must automatically extract insights, classify information, and normalise complex formats. GTA serves as a controlled yet intricate environment for simulating scenarios, enabling AI to optimise for real-world tasks through ongoing feedback loops.

    Sandbox Creativity: Minecraft and Adaptive Thinking

    Minecraft provides a sandbox environment where AI learns through exploration. OpenAI even trained an AI to play Minecraft by watching YouTube tutorials, closely mimicking the way humans learn. Similarly, any AI used by financial institutions must be able to self-learn from new document types and structures, adapting just as a Minecraft AI learns to survive.

    Reinforcement learning, where AI improves based on feedback, is a key element of intelligent document processing. Thanks to its vast scalability and dynamic, hierarchical environments, Minecraft serves as an ideal setting for navigation and repeated feedback loops, helping models develop domain-flexible reasoning.

    Multiplayer Mayhem: Dota 2 and the Art of Teamwork

    Dota 2 stands out as one of the most complex competitive games ever created, presenting AI with challenges in real-time decision-making, strategic coordination, and adaptability. OpenAI Five, trained on the equivalent of 45,000 years of gameplay within just 10 months, managed to defeat renowned, professional human teams. As anyone who has mastered StarCraft knows, tactical adaptability is essential for gaining the upper hand.

    Financial institutions operate in environments that are just as dynamic as the shifting levels of a video game. Market conditions, regulations, and data formats are in constant flux. AI must be able to adjust to new document structures, handle missing information, and navigate edge cases, much like AlphaStar adapts to an opponent’s unpredictable strategies.

    From Pixels to Profits: Bringing Game Logic to Finance

    Whether to streamline operations, mitigate risks, or make informed decisions in today’s data-intensive financial landscape, AI has the potential to fundamentally transform financial offerings, delivering personalised and evolving experiences that foster understanding and combine seamlessness with regulatory compliance.

    Yet AI does not simply require more data from which to learn; it needs better data. Video games offer near limitless, pre-built, highly complex digital worlds where AI can test hypotheses, simulate scenarios, and refine decision-making models. By utilising these unique environments, AI is challenged to enhance its speed, accuracy, and efficiency. 

    The world of video games has many lessons we can learn when building AI, and given AI’s remarkable ability for transferable learning, it makes sense to leverage these pre-trained models to power essential financial workflows. It is more than just document processing; it is thinking, and the same intelligence that enables AI to defeat world champions in Dota 2 is now driving the next generation of financial AI solutions.

    The next chapter of AI-driven finance will be shaped not just by technology, but by creativity. By embracing unconventional data sources such as the immersive complexity of video games, industry leaders will unlock new possibilities for personalisation, security, and customer engagement.

    Learn more at linedata.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech
    • Neobanking

    Author

    Richard Doherty

    Published

    Estimated Read time

    6Mins

    Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence

    The asset management industry is entering a structural inflexion point. The first wave of AI focused on improving productivity through copilots and automation. The next wave will fundamentally reshape how decisions are made, executed, and governed across the enterprise. This is not a technology upgrade. It is an operating model shift.

    Despite significant investment, many firms remain trapped in fragmented AI experimentation. A majority are yet to realise meaningful economic returns from AI, not due to lack of capability, but due to a failure to redesign how intelligence is applied across the organisation. The gap between ambition and outcome is not a technology problem. It is a structural one.

    From Automation to Decision Intelligence

    The industry conversation has evolved. The question is no longer whether to adopt AI, but how to scale it across the enterprise. However, most firms are still approaching this challenge through the lens of automation, identifying tasks that can be executed faster or at lower cost. This delivers incremental value, but does not address the underlying constraint: the structure of decision-making within the organisation.

    Traditional operating models are built around sequential workflows. Work moves from function to function: research, compliance, operations, and distribution, each dependent on the previous stage. This creates latency, duplication, and fragmentation. Agentic operating models shift the focus from tasks to decisions.

    Instead of asking “Which processes can we automate?”, leading firms are asking: “Which decisions can be augmented or owned by intelligent systems?”

    This shift enables organisations to move from sequential workflows to parallel decision systems; from human-led analysis to AI-assisted reasoning; from periodic insight to continuous intelligence. The result is not a marginal improvement. It is a step-change in how the enterprise operates.

    The Pressures Driving Change

    This transformation is not happening in a vacuum. Asset managers face mounting structural pressures: margin compression driven by fee pressure and passive competition; rising operational complexity from regulation and product proliferation; and advisor capacity constraints that limit scalable growth. Agentic operating models directly address all three.

    By automating complex workflows, rather than individual tasks, firms can significantly increase advisor and analyst capacity without proportional cost increases. Parallel decision systems reduce the time required to launch products, respond to market events, and deliver client insights. This compresses cycles from months to days. Continuous monitoring of guidelines, portfolios, and operational processes reduces exposure to regulatory breaches and operational failures.

    These are not theoretical benefits. They represent measurable improvements in cost-to-serve, time-to-market, and operational resilience.

    Not all Intelligence is the Same

    To scale AI effectively, organisations must recognise that not all problems require the same type of intelligence. Enterprise AI operates across three distinct layers, and conflating them is one of the primary reasons AI initiatives fail to scale.

    Deterministic systems execute predefined rules with complete consistency. They are essential for functions where there is zero tolerance for error, trade validation, settlement processing, and regulatory reporting. If a business outcome must be identical every time, deterministic logic remains the correct approach.

    Predictive systems use historical data to forecast outcomes. Applied in areas such as portfolio risk modelling, fraud detection, and client churn prediction, they generate probabilities and insights, but they do not interpret context or make decisions independently.

    Agentic systems operate where problems require interpretation, judgment, and contextual understanding, investment guideline interpretation, regulatory document analysis, portfolio insights, and client communication. These systems can reason across complex information, generate insights, and take action within defined boundaries.

    The ‘Different but Valid’ Dilemma

    A critical challenge in adopting agentic systems is understanding how they behave. Traditional software produces identical outputs. Agentic systems produce reasoned outputs.

    This introduces what I call the ‘different but valid’ dilemma. An agent may take a different reasoning path from a human and arrive at a different, but still correct, conclusion. This variability is not an error. It is inherent to reasoning systems.

    The real risk lies in hallucination, outputs that are not grounded in data or evidence. Managing this requires organisations to clearly define where variability is acceptable. All AI-driven processes sit on a spectrum: deterministic actions with no variability (trade execution), predictive actions with controlled variability (risk scoring), and agentic actions with higher variability (investment insights).

    Leading firms design systems where agents perform reasoning, deterministic systems enforce execution, and humans retain oversight on high-consequence decisions. This balance enables both flexibility and control.

    The Operating Model Shift

    The most significant change is not technological; it is organisational. Traditional models are built on functional workflows. Agentic models are built on coordinated decision systems.

    Consider what launching a new investment product looks like under each model. In a traditional model, it involves sequential handoffs between teams, compliance reviews the guidelines, operations configures the systems, and distribution drafts the client narrative. Each stage waits for the last.

    In an agentic model, intelligent systems operate in parallel: compliance agents interpret guidelines, operations agents configure constraints, distribution agents generate client narratives, and governance agents validate outputs. This orchestration compresses timelines, reduces friction, and enables continuous decision-making. It represents a fundamental redesign of how work is performed.

    Governance: the Foundation for Trust

    Trust is the prerequisite for scaling AI. Without it, adoption stalls, not because the technology fails, but because the organisation cannot adequately explain or defend the decisions it makes.

    Leading firms implement governance models built on three principles. First, explainability: every decision must be traceable and auditable. Second, authority boundaries: agents operate within clearly defined limits. Third, human oversight: high-consequence decisions remain under human control.

    Regulatory expectations will continue to evolve, but one principle remains constant: organisations must be able to explain how decisions are made.

    Scaling AI is a Leadership Challenge

    Executives must take a deliberate approach across four areas:

    • Define the intelligence model: map business problems to deterministic, predictive, or agentic systems.
    • Build the foundation: invest in data, infrastructure, and orchestration capabilities.
    • Redesign the operating model: shift from workflows to decision systems.
    • Implement governance to ensure transparency, control, and compliance.

    Start with high-value use cases and expand rapidly across the enterprise. The firms that act now will establish a structural advantage in cost, speed, and decision quality. Those that do not risk being constrained by legacy operating models that cannot scale with the demands of modern markets.

    The Question is not if, it is Who

    The industry is not simply adopting new technology. It is redefining how decisions are made. The firms that succeed will not be those that deploy AI tools in isolation. They will be those who design the right form of intelligence for each problem, redesign their operating models around intelligent systems, and scale agentic capabilities across the enterprise.

    This shift is already underway. The question is no longer whether it will happen. The question is which firms will lead, and which will be forced to follow.

    Learn more at publicissapient.com

    • Artificial Intelligence in FinTech
    • Blockchain & Crypto
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Martijn Gribnauis

    Published

    23 March 2026

    Estimated Read time

    5Mins

    Martijn Gribnauis, Chief Customer Success Officer at Quant, on why Agentic AI will redefine financial services

    A recent Google Cloud survey showed that only 13% of finance organisations are currently using agentic artificial intelligence. This number needs to, and will rise when you consider that 88% of financial leaders are seeing ROI from generative AI already. Agentic is the next and most advanced evolution of artificial intelligence the world has ever seen. 

    Agentic AI is not on the way. It is here and already reshaping how forward-leaning financial institutions operate. In 2026, for IT and finance leaders to build an insurmountable competitive lead they must deploy agentic AI in every area where it can safely and effectively create value. The institutions that hesitate will find their business models under threat from familiar competitors and newcomers alike.

    Reinvention of Core Processes

    Agentic AI is poised to reinvent core financial processes. Bookkeeping, record maintenance, and period-end close are nearing complete automation. Month-end processes that once required late-night, stress-filled marathons will evolve into continuous, largely automated cycles. IT teams will no longer spend evenings on high alert waiting for failures. 

    This shift also frees IT leaders, finance teams, and operations functions from monotonous repetitive tasks. Instead of focusing on system uptime and manual reconciliation, they will collaborate with the C-suite on strategic initiatives that drive growth and revenue. 

    Understanding Why Adoption Is So Low

    Despite the promise of Agentic AI, there is understandable caution. Some 80% of organisations have reported ‘risky behaviour’ from AI agents, and in the world of finance that is an alarming number. Finance is one of the most regulated, risk-averse sectors in the world. The fear of losing control remains the primary reason so few in the industry have embraced Agentic AI.

    Loss of control and fear of catastrophic error

    Financial leaders fear that an autonomous system could go ‘off script’, mis-route payments, misinterpret rules, or inadvertently cause compliance breaches. In finance, even small errors can trigger major financial or regulatory consequences.

    Security and data privacy concerns

    Large AI models require huge quantities of sensitive data. Organisations worry about breaches, cyber-attacks, or manipulation. An AI agent with improperly configured permissions could, in theory, execute fraudulent transactions or expose confidential customer information.

    Bias and fairness risks

    If AI agents make decisions using incomplete or fragmented data, they risk perpetuating or amplifying bias. At scale, biased decision-making can undermine customer trust and expose firms to legal and regulatory challenges.

    Regulatory ambiguity and audit difficulty

    Regulators are still determining how to govern agentic AI. Some organisations fear that early adoption could unintentionally violate rules or create future audit vulnerabilities.

    These fears are legitimate, but not insurmountable.

    Tackling the Adoption Barriers: A Practical Blueprint for Finance Leaders

    To capitalise on Agentic AI’s immense potential, leaders must take a structured approach grounded in business value, security, and trust.

    1. Start With Clear, Measurable ROI and Efficiency Gains

    In finance, adoption accelerates when decision-makers see proof of value.

    Start by automating repetitive processes. Agentic AI can handle tasks like data entry, reconciliation, invoice matching, and initial fraud checks faster and more accurately than humans. This leads to reduced operational overhead as automation lowers labour costs, shortens processing times, and reduces error rates. Demonstrating these savings through case studies or internal pilots is critical to changing minds. 

    AI agents can enable revenue growth by analysing huge data sets to identify new investment opportunities, optimise trading strategies, and generate personalised product recommendations. Each of these capabilities directly impacts top-line growth.

    2. Strengthen Risk Management and Compliance Through AI

    Agentic AI will improve risk management when deployed responsibly. This starts with real-time fraud detection. AI agents can monitor transactions continuously, identifying patterns that suggest fraud long before traditional systems would detect an anomaly.

    Continuous monitoring is also incredibly helpful when it comes to compliance. AI agents excel at ensuring adherence to KYC and AML regulations. They can automatically maintain audit trails, identify missing documentation, flag anomalies, and escalate issues instantly.

    Enhanced stress testing and scenario modelling can both be completed via Agentic AI. It can simulate complex market environments more dynamically than legacy tools, providing deeper insights into vulnerabilities and improving resilience. When showcased and presented in this context, agentic AI becomes a risk-reduction tool in the eyes of decision makers. 

    3. Directly Address Security and Trust Concerns

    Trust is the cornerstone of adoption. Implement enterprise-grade security architecture that includes encryption, secure APIs, strict access controls, and continuous monitoring of agent behaviour. And, use explainable and transparent AI systems (XAI) so your finance teams understand the reasoning behind decisions. XAI helps provide interpretable outputs that support auditability and regulatory compliance.

    Start small with a controlled, low-risk pilot. A proof-of-concept in a non-critical workflow helps teams understand the technology, gather evidence, and build internal support before scaling. Produce numbers based reporting that speaks the language of the people who make the decisions. Show, don’t just tell them how agentic will move the business forward.

    4. Highlight the Competitive Advantage

    Agentic AI adoption is not just an efficiency upgrade. It is a competitive imperative. AI agents create faster innovation cycles by accelerating product development, service delivery, and operational improvements.

    They also provide superior customer experience. From instant account servicing to personalised financial recommendations, Agentic AI delivers the speed, personalisation, and convenience customers expect. Plus, it scales exponentially. No matter how many people call in at the same time, an agentic agent will answer immediately. Agentic AI reduces up to 86% of time spent in complex workflows that were traditionally handled only by people. This will be huge in getting ahead of your competition. 

    5. Build Momentum Through Internal Champions

    Adoption increases when respected leaders advocate from within. Mid-level managers, AI-literate staff, or members of the C-suite who understand the technology can serve as champions. Use them and their beliefs to drive alignment, communicate benefits, and counter misconceptions. The more people from different departments and levels of the organisation that talk up the technology, the more likely you are to get buy-in. 

    Your Time is Now

    Agentic AI will redefine financial services. The organisations that act today will build capabilities, insights, and competitive advantages that late adopters will not be able to replicate. Finance leaders must begin asking where agentic AI can support their business, where it can remove friction, where it can unlock growth, and where it can transform operations. The firms that act now will lead the industry. Those that hesitate will not get the chance to catch up.

    The only remaining question for finance organisations is not whether agentic AI will change the industry, but how quickly they choose to deploy it.

    Learn more at quant.ai

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Payments
    • Digital Strategy

    Author

    Richard Ford

    Published

    20 March 2026

    Estimated Read time

    3Mins

    Richard Ford, Chief Technology Officer at Integrity360, on why cybersecurity must move beyond control and embrace trust

    Cybersecurity has long been focused on building walls, but the biggest threat is already inside. Today, insider risk accounts for nearly half of all data breaches. This isn’t just about malicious actors, it’s about regular employees and trusted contractors who make simple, costly mistakes.

    Remote and hybrid working has only intensified the problem. With teams distributed and work happening across cloud platforms and collaboration tools, it’s harder than ever to track what’s happening, let alone why. Although AI tools promise efficiency, they also introduce new vulnerabilities. Employees pasting code into chatbots or bypassing corporate tools to meet deadlines. All seemingly innocent, but highly risky.

    Insider Risk

    Ransomware gangs know this and are now skipping the technical breach altogether and going straight to the source – a company’s insiders. Whether through bribery or social engineering, attackers are finding that humans can be the weakest link in even the most well-defended environments. Despite this, most security budgets still focus outward.

    Traditional tools like data loss prevention (DLP) struggle to keep up with today’s dynamic and unpredictable user behaviour. Meanwhile, simulated phishing tests and punitive training schemes often breed resentment, not resilience. It’s time to rethink the model.

    Human Error, Human Fix

    We need to stop treating employees as the problem and start making them part of the solution. Enter Human Risk Management (HRM), a behavioural approach to cybersecurity that recognises the complexity of modern work. HRM tools monitor real-world user behaviour, detect anomalies in context, and deliver just-in-time nudges to prevent risky actions before they happen. Instead of punishing mistakes, they help users avoid them in the first place.

    Of course, technology alone won’t fix the issue, culture is key. Leadership must champion security as a shared responsibility, not an IT rulebook. Success should be measured by how quickly employees improve, not how often they slip up. Awareness campaigns need to be practical and rooted in real-world behaviour.

    Organisations also need to understand how digital transformation has changed the risk landscape. Shadow IT is no longer a fringe issue, it’s how work gets done. Whether it’s a developer using an AI plugin or a marketer sharing files via a personal drive, employees will always find the fastest path to productivity. Security must meet them there, not block the way.

    Cybersecurity Built on Trust

    The smartest businesses are those that treat identity like infrastructure, and behaviour like a vital data stream. They invest in tools that adapt to people, not the other way around. This means a move away from a surveillance approach and embracing the nuance of human error and design systems that support.

    In a world where threats are increasingly internal and AI is both a risk and a tool, cybersecurity can no longer be about control. It must be about trust, and that starts with understanding the humans behind the keyboards.

    Learn more at integrity360.com

    • Cybersecurity
    • Cybersecurity in FinTech
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Pierre Noel

    Published

    Estimated Read time

    4Mins

    Pierre Noel, Field Chief Information Security Officer at Expel, on why security with community-based governance is a key business pillar that better positions organisations to become more resilient and target growth

    It’s been a particularly rocky start to 2026 for the global cybersecurity landscape. From the Substack data breach to PayPal credential-stuffing attacks in February, we are not looking at IT failures alone. These attacks are balance-sheet events: direct assaults on business value, triggering remediation costs and long-term impacts on financial health. Compounded with the conflict with Iran, leading to potential ramifications in the cyber realm, it’s more important than ever for the C-suite to be aligned on cybersecurity priorities.

    Despite this, a glaring disconnect remains in planning and execution. Expel’s research found that while 85% of finance leaders view cybersecurity as a key component of business planning, only 40% express full confidence in security’s ability to align with business strategy. To bridge this gap, CISOs must move from reporting on activity and start reporting on resilience and unit cost.

    Translating Alert Volume Into Unit Cost

    CISOs must change how they present the value of their operations. CFOs are largely indifferent to technical metrics like the ‘millions of blocks pings’ or ‘SOC alert volume’ – to a finance leader, an alert is simply another form of disruption to daily operations.

    To fix this, CISOs should introduce the ‘unit of cost protection’. By breaking down security spend into the cost required for a single transaction or business unit, CFOs can understand and manage it from experience. A tiered approach works best here: high-risk business units justify higher protection costs than low-risk ones. This allows CFOs to treat security as a scalable operational expense rather than a black hole of additional tooling – the kind of framing that also resonates in a boardroom.

    Mapping Investment to Business Risk Exposure

    Expel’s research shows that while 43% of finance decision-makers are confident that security can prioritise investments based on risk, only 46% are confident that security can deliver cost-efficient solutions. To move in the right direction, CISOs should shift from ‘vulnerability management’ to thinking about ‘business risk exposure’, requiring a different view of how threats unfold over time.

    It’s all about asking the right questions. Instead of requesting more firewalls to protect a specific timeframe, start asking for the cost of securing diverse digital ecosystems across an extended risk window. The 2026 Winter Olympics is a good example: Russian-led cyber campaigns began raising concerns months before a single athlete arrived in Italy, proving that risk isn’t a one-day event but an ongoing operational cost.

    For European organisations, this framing is increasingly non-negotiable. While NIS2 and DORA help make the cost of under-investment concrete and quantifiable, the upcoming Cyber Resilience Act (CRA), with key reporting requirements starting in September 2026, extends this pressure to anyone manufacturing or selling digital products in the EU. Even for purely domestic UK entities, the new UK Cyber Security and Resilience Bill is moving the goalposts toward these same high standards. Ultimately, CFOs must understand that cybersecurity isn’t just about preventing loss; it’s a prerequisite for safe and secure growth.

    The Reputational Multiplier

    So those are the questions to ask, but how do CISOs deal with the ‘unknown unknowns’, specifically long-term brand damage? While compliance fines under NIS2 or DORA may be straightforward (and important) to model, they rarely represent the full scope of the potential damage. In such scenarios, CISOs should propose a reputation multiplier: a framework for quantifying the financial fallout of brand damage in a language CFOs know and trust, looking past immediate recovery costs to factor in the long-term implications of re-establishing market trust.

    The 2026 CarGurus breach illustrates this well. Impacting 12 million users, the cost wasn’t purely technical; it also came from the stock price dip and marketing spend required to repair the brand. For UK companies, where regulatory scrutiny is heightened, that multiplier effect is even more pronounced. This is the language of a CFO, and it helps CISOs better translate the urgency and relevance of a strong cybersecurity posture.

    Standardising the Language of ROI

    Closing the gap between CFOs and CISOs needs more than just better data; it needs a shared vocabulary. By standardising the language of ROI, CISOs transform cybersecurity from a vague insurance policy into a transparent value driver fully trusted by finance teams. Move away from complicated defensive jargon toward a unified framework of unit costs, and the gap between the CISO and CFO starts to close.

    Security has become a key pillar of business operations, and in the current threat environment, it’s genuinely a community-based governance issue. The organisations that get this right aren’t just more resilient. They’re better positioned to grow.

    Learn more at expel.com

    • Cybersecurity
    • Cybersecurity in FinTech
    • Digital Strategy
    • Infrastructure & Cloud

    Author

    Dr. Yvonne Bernard

    Published

    18 March 2026

    Estimated Read time

    5Mins

    Dr. Yvonne Bernard, CTO at Hornetsecurity, on meeting the challenge of managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption

    The past year has been defined by acceleration. Threat actors rapidly embraced automation, AI, and social engineering. Scaling their tactics at unprecedented speed, while defenders raced to keep pace. Historically, defensive resilience evolves in step with attacker innovation, but in 2025 that balance began to falter.

    In an analysis of over 6 billion monthly emails, Hornetsecurity’s Security Labs found that the volume of sophisticated threats grew faster than most security teams could adapt to. Malware-infected emails soared by 131%, scams increased by nearly 35%, and phishing attempts – powered by access to advanced AI – rose by 21% from the previous year.

    Typically, attacks, even at volume, are easily filtered by good firewalls and secure email gateways. But the sophistication and AI-led nature of 2025’s boom made it even harder for organisations to defend themselves. The question now is: can security teams and businesses wrestle back control?

    Evolving Cyberattack Landscape

    ​​AI enhances efficiency and precision. As such, cybercriminals use it to launch faster, more convincing and adaptive attacks, ranging from deepfakes to credential stuffing. As an example, there is a concerning trend of attackers increasingly using ‘MFA bypass kits’ to create deceptive login pages. These pages capture not only the user’s credentials but also have logic built in to handle MFA prompts as well. ​​The unsuspecting user is then passed to the real login page for the target service and meanwhile the ‘kit’ grabs a copy of the user’s session token. This allows the attacker to impersonate the person and access their data. ​​​​​

    Examples of such kits include Evilginx (open source) and the W3LL panel. Protecting against these attacks can be challenging, as they are adept at bypassing MFA safeguards. Threat actors often use compromised LinkedIn accounts, for example, to gain access to substantial information and connections. This enables them to impersonate trusted business connections. Paired with the weaponisation of Agentic AI, this will magnify existing vulnerabilities within an organisation, while introducing new ones that defy traditional containment models.

    As it stands, the lack of oversight within organisations on the extent of AI’s adoption by cybercriminals has enabled the emergence of ‘Ransomware 3.0.’ Ransomware has evolved past simple encryption and exfiltration, with this next phase focusing on LLM-driven orchestration and a shift to data integrity manipulation.

    To counter AI-accelerated compromises and ‘Ransomware 3.0’ in 2026, organisations must adopt a Zero Trust-based cyber resiliency strategy. This requires businesses to implement strong, non-phishable machine authentication, strict least-privilege access, and constant monitoring to protect the integrity of the data that users and AI agents can access. It should become the baseline expectations rather than aspirational goals for this year.

    The Secret Value of ‘Least Privilege’ Access

    Another strategy to proactively improve cybersecurity defences in 2026 is to enforce the principle of ‘least privilege’ access. This tactic grants users access only to the data that’s needed for their role. Limiting excessive access is important for preventing the potential for widespread data exposure and damage in the case of an account compromise.

    Businesses, however, must strike a balance over access; if it’s too strict, it can hinder productivity and lead to shadow IT issues. Getting this balance right when it comes to privileged access is where sophisticated permission managers are invaluable tools to work with. They streamline the process and remove the guessing game of who and what to grant access to, thereby ensuring, in the case of an attack, that the entire organisation won’t be brought to its knees.

    How CISOs are Adopting ‘Resilience, not Perfection’

    The rate at which AI is advancing means not every organisation will be equipped with the tools or the know-how to tackle every AI-inspired attack. But as the saying goes, ‘prevention is better than cure’. It’s better to create a strong security culture than to continually chase after the next best tool. 

    Organisations can’t strengthen their resilience without involving every single person under their umbrella. That’s why CISOs must continue to invest in cybersecurity awareness programs.

    These should include simulated AI-phishing attacks (phishing remains the number one attack vector) to test users and enable them to apply learnings from the modules.

    If any user clicks on a phishing email, they should receive additional training at that very moment, to cement the learning. Over time, a good training system should automatically identify users who rarely fall for such attacks and reduce the training they receive while making the simulations they do receive more difficult. Conversely, giving persistent offenders additional bite-sized training and simulations can help improve security outcomes over time.

    The key challenge for 2026 is managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption. But with excellent training, cyberattack practice runs, and the adoption of Zero Trust principles, organisations will find themselves in a strong position.

    About Dr. Yvonne Bernard

    Dr. Yvonne Bernard is the CTO of Hornetsecurity by Proofpoint, Proofpoint’s business unit leveraging the Hornetsecurity product suite dedicated to managed service providers (MSPs) and small to mid-sized businesses (SMBs), providing next-generation cloud-based security, compliance, backup, and security awareness solutions that help companies and organisations of all sizes around the world.

    Learn more at hornetsecurity.com

    • Cybersecurity
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Dr Megha Kumar

    Published

    17 March 2026

    Estimated Read time

    6Mins

    Dr Megha Kumar, Chief Product Officer and Head of Geopolitical Risk at CyXcel, on whether our risk and regulatory frameworks and institutional cultures can keep pace with Agentic AI

    Within the next couple of years, Agentic AI is likely to progress from early stages of operation to be fully embedded within systems. Its expansion will be subtle rather than spectacular. It will integrate steadily into enterprise platforms, logistics networks, compliance workflows, cybersecurity operations centres and executive decision-support tools. Processes will move faster, operating expenses will decline and performance indicators will trend upward.

    Yet these visible improvements mask a deeper challenge. The regulatory exposure, data governance pressures and erosion-of-trust risks associated with Agentic AI are being misjudged.

    Unlike earlier AI applications designed primarily to generate outputs – whether text, imagery, or predictive insights – agentic systems are built to act. They sequence decisions, draw from multiple data environments, initiate consequential processes and function at scale with differing levels of human supervision. In sandbox environments this can seem contained and controllable. Over extended periods in live environments, however, sustained oversight, traceability and effective governance become significantly more complex.

    Evolving Operational Complexity

    There are two key challenges that businesses must address.

    First, how do organisations monitor what agentic systems are doing once deployed? These systems evolve through updates, integrations and retraining and they interact with new data environments.

    Second, how do you ensure responsible behaviour throughout the lifecycle? Regulators, policymakers and customers will likely expect firms to shift from compliance assurance to risk assurance and demonstrable evidence of trust and transparency.

    The prevailing assumption is that human oversight will mitigate these risks. Human in the loop or human over the loop has become the default reassurance. In practice, however, that assumption breaks down far faster than many anticipate.

    When a system works 95 per cent of the time, human reviewers limit their scrutiny. Behavioural science tells us that automation bias and complacency occur when automated systems are high-performing. Employees often become validators of AI outputs rather than critical examiners. The diligence gap widens gradually and then suddenly.

    Facing Up to Difficult Questions

    How do you incentivise employees to remain diligent checkers when the system mostly ‘works’?  And how much time does effective oversight actually require? True review is not a cursory glance at a dashboard. It involves interrogating assumptions, validating inputs, checking context and assessing downstream consequences. In many cases, meaningful oversight may take nearly as long as performing the original task manually. When checking becomes more costly than doing the job yourself, pressure to ‘trust the system’ intensifies.

    And what happens to accountability when oversight exists on paper but not in practice? Governance documentation may show layered review structures, escalation pathways and audit processes. Yet if humans are functionally disengaged, responsibility becomes dispersed. When errors surface, organisations may struggle to attribute fault – was it the model design, the data, the integrator, the operator or the reviewer who signed off without fully scrutinising?

    Regulators are only beginning to grapple with these realities. In jurisdictions such as the European Union, the EU AI Act introduces risk-based obligations, documentation requirements and human oversight provisions. These are important steps, however, the operationalisation of those requirements in dynamic, agentic environments remain untested at scale. Compliance on paper will not automatically translate into resilient governance in practice.

    Addressing the Trust Challenge

    Beyond regulatory exposure, there is a broader trust challenge emerging.

    As Agentic AI systems scale across industries, they will generate vast volumes of automated outputs – reports, communications, risk assessments, content, decisions and transactions. If errors or manipulations spread through interconnected systems, confidence in digital outputs may erode.

    In geopolitically sensitive contexts, this has profound implications. Agentic systems interacting with external data sources could amplify disinformation, introduce biased datasets or make decisions based on manipulated inputs. The speed of automation may outpace the speed of verification. Trust, once diluted, is difficult to restore.

    Data protection risks will also intensify. Agentic systems frequently require broad access privileges to perform tasks effectively. They may access internal databases and personal data and interact with third-party platforms. Each interaction creates potential exposure points. A single misconfiguration or prompt injection attack could trigger cascading consequences across systems.

    The next phase of AI adoption will not simply amplify productivity: it will amplify regulatory, legal and reputational risk. This moment therefore demands serious scrutiny before agentic AI becomes deeply embedded in business infrastructure.

    The Moment for Action has Arrived

    So, what should organisations be doing now?

    To begin with, organisations need to look past superficial, tick-box compliance. Effective governance cannot live solely in policy documents – it must function in day-to-day operations. This means investing in continuous monitoring capabilities, robust audit trails and real-time anomaly detection tailored specifically to Agentic AI behaviours.

    In parallel, incentive structures should be redesigned. Meaningful human oversight will not happen if it is treated as secondary to speed or output. If employees are expected to provide meaningful review, organisations must allocate time, training and authority accordingly. Performance metrics should reflect risk management responsibilities, not just output rate.

    Clear lines of accountability are equally important. Senior leadership and boards should determine who carries ultimate responsibility for outcomes produced by agents. Where third-party vendors are involved, responsibilities must be contractually and operationally defined. Incident response mechanisms should be rehearsed in advance, rather than presumed to work when pressure is high.

    Expertise must also be integrated across functions. Legal, risk, compliance, cybersecurity, data protection and operational teams should be engaged from the outset. Deploying Agentic AI is not simply a technical upgrade – it reshapes the organisation’s risk profile.

    Finally, resilience demands deliberate stress-testing. Leaders should examine not only pathways to success but how models fail at scale. How would the organisation respond if a system update embedded systemic bias, if an integration vulnerability enabled unauthorised activity or if automated actions eroded customer confidence? Rigorous scenario exercises, however uncomfortable, are essential to building genuine preparedness.

    As Agentic AI advances, Risk Management Should Match its Pace

    None of this is an argument against adoption. Agentic AI presents meaningful productivity improvements and the potential for sustained competitive differentiation. Organisations that deploy it with discipline and foresight may secure a measurable advantage. The danger lies not in adoption itself, but in pursuing acceleration without knowing the risks and putting the right guardrails in place.

    The coming two years are critical for businesses. Before these systems become deeply embedded in core processes, organisations have an opportunity to shape the control environment around them.  However, once agentic systems are fully embedded, retrofitting controls will be far more difficult and costly. Leaders must therefore treat this period as a design phase for oversight, not merely a race for competitive advantage.

    Agentic AI is advancing rapidly. The defining question is whether our risk and regulatory frameworks and institutional cultures can evolve just as quickly.

    Learn more at cyxcel.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    9 March 2026

    Estimated Read time

    5Mins

    Visa is leading the AI race in payments, according to Evident’s AI Index for Payments, a major new ranking of…

    Visa is leading the AI race in payments, according to Evident’s AI Index for Payments, a major new ranking of AI adoption within the industry. 

    The Index shows industry stalwarts Visa and Mastercard outpacing their peers and delivering tangible AI outcomes thanks to early investments in talent and innovation.

    Behind them, PayPal (3rd), American Express (4th), Stripe (5th) and Block (6th) emerge as the challengers. They outperformed the Index average, but are yet to match the leaders’ scale of deployment and outcome disclosure.

    AI Moving from Experimentation to Deployment

    Over the past two years, the 12 payments companies in the Index have publicly documented nearly 100 AI use cases. Underscoring how rapidly AI has moved from experimentation to deployment across core payment workflows. It’s a landscape defined by constantly evolving fraud threats and rising customer expectations for faultless, high-speed processing. Evident notes that nearly a third of these use cases disclose measurable outcomes, including efficiency gains, risk reduction and revenue uplift.

    “Payments firms adopted AI out of necessity long before many other industries – their business models demanded it. Companies who invested early – like Visa and Mastercard – have gained a clear advantage over their peers, both in AI capabilities and the value their deployments are realising.” Alexandra Mousavizadeh, Co-Founder and Co-CEO of Evident.

    Talent, Innovation, Leadership and Transparency

    The Evident AI Index for Payments provides the most comprehensive independent benchmark of AI maturity across the industry. It is based on publicly available data around four pillars critical to successful AI deployment: Talent, Innovation, Leadership and Transparency.

    According to Evident, Visa’s lead is based on consistent performance across the four pillars. And because it demonstrates the clearest evidence that AI is institutionalised across its core transaction network. Visa and Mastercard show maturity in areas such as fraud detection, cybersecurity and network-level risk reduction. Visa stands out for the scale and measurable impact of a handful of large, multi-year deployments focused on the integrity and security of its entire ecosystem.

    “Mastercard shows strong evidence of scaled deployment and quantified performance improvements. Particularly in areas like fraud detection and AML tracing,” continued Mousavizadeh. “But what sets Visa apart is the degree to which the company is demonstrating impact at scale over multiple years. From applications of AI across its operations and network. It signals a shift from individual use cases to AI as institutional capability.

    “What the Index also reveals is the importance of consistent innovation to maintain competitive advantage. With relatively nascent industry players like Stripe and Block performing well – and showing their AI potential reflected in their valuations – the Index leaders cannot afford to drop off the pace.”

    AI Impact on Show, but ROI Reporting Scarce 

    Firms in the top half of the Index account for nearly 80% of use case disclosures (with the top three providing a significant 54%). Highlighting the link between AI maturity and the ability to scale deployment.

    Visa performed strongly in this regard. For instance, its latest threat report disclosed advanced AI/ML blocked nearly 85% more fraud compared to one year prior. Similarly, when Mastercard incorporated Gen AI technology into its Decision Intelligence solution, initial modelling showed AI enhancements improved fraud detection rates from an average of 20% to as high as 300% in some instances.

    However, Evident notes that no payments company has disclosed realised or projected ROI across all enterprise or group-wide AI activities. 

    “The Index leaders are locked in a tight race at a point when the thinking around corporate AI adoption is shifting – away from chasing the biggest models to building technologies that solve real operational problems efficiently,” commented Annabel Ayles, Co-Founder and Co-CEO of Evident. “Against this backdrop, the absence of ROI disclosure – or any group targets for AI ROI – is increasingly conspicuous. Currently, 1-in-5 banks now report on group-level AI returns. However, payments firms have yet to quantify the aggregate impact of their AI investments. To keep justifying this expenditure, the market will sooner or later demand clearer evidence of value.”

    A Hotbed of AI Talent

    The Index also reveals that the average payments company has over 30% more AI-focused workers than other financial institutions, despite substantially smaller employee numbers. 

    The three major card networks – Visa, Mastercard and American Express – account for nearly half (48%) of the payments industry’s AI talent stack. PayPal is currently the biggest employer, accounting for nearly a fifth (18%) of that AI talent.

    PayPal’s AI talent has allowed it to build proprietary models tightly integrated with its data and workflows. Consequently, it accounts for nearly a quarter (24%) of the 98 AI use cases documented by its peers over the past two years – 1.7x as many AI applications as detailed by Visa or Mastercard.

    “AI maturity is no longer defined by talent volume alone, and the Index leaders combine AI development, data engineering and product capabilities in ways that allow them to move rapidly from model experimentation to production deployment,” concluded Ayles.

    The Evident AI Index Methodology

    The Evident AI Payments Index ranks the AI maturity of 12 of the largest payment networks and processors across the globe. These 12 entities were chosen by aggregating the largest payment companies, with a minimum of $2B in annual revenue. 

    It is an independent, ‘outside-in’ assessment based exclusively on publicly available information. Each company was assessed against 60+ individual indicators, organised into four pillars critical to successful AI deployment at scale: Talent (45% weighting), Innovation (30%), Leadership (15%) and Transparency of Responsible AI activity (10%).

    Data is gathered through a combination of extensive manual research and proprietary machine learning tools that extract key data points from company reporting and public disclosures (including press releases, investor relations materials, group-level website pages, group-level social media accounts, and media interviews with senior leadership), as well as a range of third-party data platforms.

    Further information on the methodology of the Index can be found at evidentinsights.com

    • Artificial Intelligence in FinTech
    • Digital Payments
    • Neobanking

    Author

    Adam Spearing

    Published

    5 March 2026

    Estimated Read time

    5Mins

    Adam Spearing, VP of AI GTM EMEA at ServiceNow, on why those that invest in AI foundations now will shape their operating models on their own terms

    Much of the debate around AI still centres on pilots: which tools to test, which use cases to prioritise, which risks to manage. Executive teams commission proofs of concept, establish governance forums and assess compliance exposure. Far less scrutiny is applied to the consequences of waiting.

    Traditional technical debt is familiar territory for CIOs. It stems from shortcuts, ageing platforms and deferred upgrades. It builds over time and is eventually addressed through structured modernisation programmes. Visible in legacy code, brittle integrations and manual workarounds. It appears on risk registers and capital plans. Leaders know how to describe it and, in principle, how to resolve it.

    Forward-looking technical debt is different. It arises when organisations postpone the foundational changes needed for new ways of working. It is not created by past expediency, but by present hesitation. And it accumulates faster.

    AI Adoption

    In the context of AI, the effects are already emerging. Each quarter spent debating readiness instead of building it increases the distance between legacy operating models and AI-enabled competitors. As models improve and user expectations shift, that distance widens, reshaping competitive baselines. What begins as a modest capability gap can harden into structural disadvantage.

    While companies debate whether to adopt AI, the margin for strategic choice narrows. Many organisations frame AI adoption as a binary decision: adopt now or wait until the technology matures further. In practice, the room for discretion is smaller than it appears. Time spent stalled in pilots or governance loops increases the gap between internal capability and market expectation.

    More than 75% of organisations are expected to face moderate to severe AI-related technical debt in 2026, predicts Forrester. The issue will not simply be missed efficiency gains. It will be structural misalignment between how their systems operate and how work is increasingly done.

    This misalignment often appears gradually. Teams rely on manual data preparation because underlying systems cannot support automation. AI tools are layered onto fragmented architectures and deliver inconsistent outputs. Employees experiment with external tools because internal platforms cannot provide the functionality they need. Each workaround creates further fragmentation.

    Over time, these patterns compound. Integration backlogs expand. Security and risk teams struggle to enforce consistent controls across proliferating tools. Data governance becomes reactive rather than designed. What began as caution begins to constrain strategic options.

    The AI Paradox

    Here’s the paradox: organisations are either rushing into unsuccessful AI pilots that create immediate technical debt, or they’re avoiding AI entirely and creating forward-looking debt through inaction. Both paths lead to the same place – systems that can’t support the future of work.

    AI isn’t just another technology layer to bolt onto existing infrastructure. It’s fundamentally changing how people interact with systems and how work gets done. Increasingly, AI becomes an interface through which employees access information, execute tasks and navigate processes. When AI becomes the interface – not just for customers but for employees navigating their daily tasks – organisations without AI-ready foundations will find themselves unable to compete on speed, efficiency, or experience.

    The companies that hesitate aren’t just missing out on automation benefits today. They’re building a deficit that grows exponentially as AI capabilities advance. Each new model release, each competitor’s successful implementation, each customer expectation shift adds to the debt. Each significant model improvement raises the performance benchmark across the market. Unlike legacy systems that degrade slowly, this gap accelerates.

    From Avoidance to Advantage

    Breaking free from forward-looking technical debt requires a fundamental mindset shift. This isn’t about buying more technology or launching more AI pilots. It’s about creating the conditions for sustainable AI adoption that builds capability rather than complexity.

    The organisations succeeding with AI aren’t the ones with the biggest budgets or the most aggressive rollouts. They’re the ones that took a deliberate, phased approach to ensuring their data, systems, and culture could support AI at scale. They treated readiness as an operational discipline rather than an innovation side project. They understood that AI adoption isn’t a destination, it’s a continuous capability that requires solid foundations.

    This starts with honest visibility into current technology estates. Leaders must understand what systems can realistically support AI workloads, where data quality creates barriers, and which processes are ready for automation. Only then can organisations introduce AI incrementally, modernising systems where necessary rather than forcing new capabilities onto brittle foundations. Without that clarity, AI risks being layered onto structural weaknesses.

    Modernisation therefore becomes targeted. Consolidating fragmented workflows, standardising data models and reducing unnecessary integration points increase the feasibility of scaling AI across multiple use cases. Early deployments focused on well-defined processes with clear data lineage can build internal confidence while strengthening governance practices.

    Clear Debt to Stay Competitive

    Forward-looking technical debt does not appear on a balance sheet. It shows up in slower product cycles, manual workarounds, integration backlogs and frustrated employees. It surfaces when competitors deliver AI-assisted services as standard and customers begin to expect the same everywhere. By the time these symptoms are visible, the underlying gap has already widened.

    Timing therefore becomes a strategic variable. AI capability builds cumulatively: early investment in clean data, modern workflows and interoperable systems creates a base for continuous improvement. Each iteration becomes easier, faster and more reliable. Those that delay face the opposite trajectory: increasing complexity, rising retrofit costs and shrinking room for strategic choice.

    The real issue is not adoption in principle. It is whether leadership teams are prepared to treat readiness as urgent rather than optional.

    Reducing forward-looking technical debt requires acting before competitive pressure dictates terms, aligning technology modernisation with operating model reform, and accepting that disciplined progress now is less risky than accelerated catch-up later.

    AI adoption will continue irrespective of individual organisational hesitation. Vendors will continue to refine their offerings. Regulators will clarify expectations. Customers and employees will adjust their behaviours. Those that invest in foundations now will shape their operating models on their own terms. Those that delay risk reacting to a competitive gap that is already commercially significant.

    Learn more at servicenow.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Chris Gunner

    Published

    4 March 2026

    Estimated Read time

    4Mins

    Chris Gunner, vCSO at Thrive – a leading NextGen MSP/MSSP, delivering global AI, cybersecurity, cloud, compliance, and digital transformation managed services – on how CISOs can position their cyber strategy to to become part of how a business navigates uncertainty

    Quantification of cyber risk is a growing trend. While this can be genuinely useful, in practice it is often misunderstood or over-applied by security leaders. It can range from an arbitrary figure to attempting to model every possible risk on the register in a Monte Carlo simulation. The focus can fall on the mechanics of quantification, rather than how financial decision-makers actually use the information.

    Think of the CFO – they don’t walk through every penny in the budget. Instead, they usually focus on the board-level levers that can materially affect the business. These often include three key areas: strategic optionality, removing friction from capital events and avoiding shocks and smoothing operating costs. Security conversations should be anchored the same way.

    The Importance of Strategic Optionality

    If faced with a credible one-year growth plan, CFOs may recommend a one-year office lease despite a 20% premium. This is because it maintains the option later of moving or re-contracting once the growth trajectory becomes more visible. Like most strategic decisions, it is about preserving flexibility in the face of uncertainty, even if that flexibility comes at a short-term cost.

    If we apply this to a cyber context, there are often businesses that have taken a calculated gamble with their existing business strategies. While the plan is sound, there is a chance it might not land as expected. When they require security services, the choice between a ‘standard’ and ‘premium’ SOC frames the decision as one of optionality rather than security spend. Paying more now to preserve the ability to adapt later down the line. A simple illustration is incident response. An on-call retainer with defined response times can look more expensive than ad hoc support. Until an incident occurs and procurement becomes the bottleneck. In those moments, flexibility is often far more valuable than marginal savings achieved earlier.

    Removing Friction from Capital Events

    For CFOs, especially those operating in the alternative investment space, the focus is on structuring capital events. As opposed to managing day-to-day operational costs. One of the most painful points in that process is due diligence. The careful exchange between acquirer and target that aims to provide enough information for each to price risk, without giving the entire game away.

    CISOs can materially influence how smooth or painful that process becomes. The most effective support often comes from understanding upfront what the diligence process will look like and preparing accordingly.

    For example, they might develop executive-level ‘Security at ACME’ overviews to sit alongside more detailed trust centre or technical reports. Being available to diligence teams for interviews, and for example clearly articulating which services are outsourced to an MSSP, and why, builds credibility between those executive teams.

    Decision-makers often don’t look at penetration test reports at a deal level. They are assessing whether the organisation understands its own control environment. A well-prepared CISO who can clearly explain why certain controls exist acts as a trust amplifier during transactions.

    It is often the difference between a diligence process that closes cleanly and one that drifts. Two organisations can have similar maturity. Yet the one that can respond within a day with clear, consistent evidence reduces follow-up questions, avoids uncertainty premiums in pricing discussions and prevents security from becoming a late-stage negotiation point.

    Avoiding Shocks and Smoothing Operating Costs

    For any individual who has worked with a finance partner to define a departmental budget will know that predictability often takes precedence over absolute cost. Contract value can be secondary to payment terms, renewal timing or the ability to forecast spend with confidence.

    CISOs can align with this by looking to reduce unplanned operating expenditure. In addition to understanding the cost structure of their controls by communicating with the technical pre-sales engineer, procurement and account teams.

    A good example is cyber insurance. While often purchased directly by finance teams, many policies are relatively off-the-shelf and provide access to services the security team already operates or has under contract. Other policies include notable exclusions for the events most likely to occur. Such as a ransomware incident without business interruption cover. In many cases, these gaps can be addressed in-policy with a flat fee or a more predictable cost model.

    The value here extends beyond risk transfer and into more predictable costs: replacing reactive spend with planned expenditure.

    Aligning Cyber Conversations to Board Priorities

    Across all of the above examples, the common thread is that the board is rarely asking security to prove its value in isolation, and is surprisingly comfortable with uncertainty. But they are asking whether the cyber papers support better decisions, fewer constraints and more predictable outcomes for the business as a whole.

    CISOs who frame their priorities in those terms will find their conversations move away from justifying individual controls and towards understanding how security choices shape the organisation’s ability to respond to change. In that context, cyber becomes part of how the business navigates uncertainty, rather than a specialist function defending its budget. Speaking the board’s language, ultimately, is less about converting cyber risk into pounds and pence. It is more about understanding which levers matter at that level and showing how security choices influence them.

    Learn more at thrivenextgen.com

    • Cybersecurity
    • Cybersecurity in FinTech
    • Digital Strategy

    Author

    Tom Lanaway

    Published

    2 March 2026

    Estimated Read time

    4Mins

    Tom Lanaway is Head of Innovation at Connective3, a global brand & performance marketing agency. He leads a team building AI-powered marketing measurement and marketing intelligence tools.

    Most businesses are asking the wrong question about AI. They’re asking, ‘Which AI tool should we use?’ They should be asking: ‘Can our people actually think with AI?’ 

    I run an innovation team at a marketing agency. We’ve spent the last two years building AI into everything we do, including measurement, content, strategy, and automation. We’ve got lots of tools, 18 different products to be precise. 

    Below is what I’ve learned. But the tools aren’t always the bottleneck; sometimes the skills are. 

    The Tennis Racket Problem 

    A colleague put it perfectly recently: “AI is a tool. Think of it as if you’ve got a smart assistant sat there. But it’s saying, I’m going to give you the best tennis racket, now go and play in a Grand Slam.” 

    That metaphor stuck with me because it captures something the artificial intelligence hype cycle keeps missing. We’ve convinced ourselves it democratises everything. That anyone can now do anything. That the barrier to entry has collapsed. And there’s truth in that, but it’s incomplete. The barrier to access has collapsed, but the barrier to effectiveness hasn’t. Give someone GPT-4, and they can generate text. Give them the best tennis racket, and they can hit a ball. But the gap between hitting a ball and playing at Wimbledon is still vast. Most organisations are stuck in that gap, wondering why their AI investments aren’t transforming anything. 

    Three Skills That Aren’t Always Present 

    When I look at where teams struggle and where I see the same patterns across other businesses, three specific competencies keep showing up as gaps: 

    1. Problem Decomposition 

    Not everyone knows how to break down complex work into chunks that AI can help with. This sounds simple, but it isn’t. Most people approach AI with whole tasks such as ‘Write me a marketing strategy’, ‘Analyse this data’ Or ‘Create a campaign’. AI will then produce something, but it’s usually mediocre, because the person hasn’t done the harder work of understanding which specific parts of that task AI is good at, and which parts need human judgment. The skill isn’t using AI; it’s knowing what to give it. Someone who is brilliant at their job but can’t decompose problems will get worse results from AI than someone more junior who understands how to break work into the right pieces.  

    2. Output Assessment 

    How do you know if what AI gives you is good? This is where intuition becomes essential and it’s also where the ‘AI replaces expertise’ narrative falls apart. You need domain knowledge to evaluate AI output. You need enough experience to feel when something’s off, even if you can’t immediately articulate why. You need the pattern recognition that comes from years of doing the actual work. Artificial Intelligence doesn’t replace that intuition; it requires it. The best AI users I’ve observed aren’t the most technical; they’re the ones who’ve built up enough expertise in their field to quickly assess whether AI output is useful, directionally correct, or completely off base. They know what good looks like, so they can recognise it when they see it, or notice when it’s missing.

    3. Articulation 

    Can you clearly express what you really want? This is the unglamorous core of the whole thing. Some people struggle to articulate their requirements to other humans, let alone to AI. We’ve all sat in meetings where someone spends 20 minutes explaining what they need, and you’re still not sure what they want. AI makes that problem worse. The skill isn’t ‘prompt engineering’ in the technical sense; it’s the much older skill of clear thinking and clear communication. If you can’t articulate what you want specifically, precisely, with the right context and constraints, you won’t get useful output from AI or from anyone else. 

    The Uncomfortable Implication 

    Here’s what this means for how businesses should think about AI investment: 

    Stop leading with tools: Most organisations have tool fatigue already. Another platform, another integration, another training session on which buttons to click. It’s not working. 

    Start with the human work: Before asking ‘What AI should we use?’, ask ‘Can our people break down problems, assess output, and articulate requirements?’ If they can’t do those things well without AI, they won’t do them well with AI either. 

    Invest in the skills, not just the access: This doesn’t mean AI prompt engineering courses; it means developing clearer thinking, better problem decomposition, and sharper articulation. These are old skills, applied to new tools. 

    Accept that expertise still matters: The people who’ll use AI best are the ones who already know their domain deeply. AI amplifies competence; it doesn’t create it.

    Connected Intelligence Isn’t About Connected Systems 

    I’ve spent a lot of time thinking about how different marketing channels and data sources connect and how you build intelligence across systems rather than in silos.

    But I’ve come to think the more important connection isn’t between systems, it’s between human judgment and AI capability. The integration layer that matters most is the one between the person and the tool. 

    Get that wrong, and it doesn’t matter how sophisticated your AI stack is. Get it right, and even basic tools become powerful. 

    Learn more at connective3.com

    • AI in Procurement
    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • People & Culture

    Author

    Dan Brightmore

    Published

    Estimated Read time

    2Mins

    Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…

    Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams to identify and respond quickly, whilst also meeting regulatory timeframes for handling complaints and supporting vulnerable customers.

    Netcall: AI-Powered Sentiment

    The specialist bank has worked with Netcall to deploy AI-powered sentiment analysis using Netcall’s Liberty Create platform. The solution reduces manual effort and improves operational efficiency by bringing customer emails from multiple mailboxes into a single interface. Incoming messages are automatically analysed to identify dissatisfaction, highlighting cases that may require faster intervention. This allows urgent cases to be prioritised, helping HTB to resolve issues before they escalate and improve the customer experience.

    “Our AI-powered sentiment analysis solution rapidly processes vast amounts of email data. Its efficiency allows our team to focus on resolving customer enquiries and issues rather than sorting priorities. The streamlined process ensures swifter responses and better customer outcomes, upholding our reputation for exceptional customer service.” Ed Eames, Head of Customer Savings Operations at Hampshire Trust Bank.

    The application was built by the Hampshire Trust Bank development team using Liberty Create. It worked closely with Netcall to integrate AI sentiment analysis into existing processes. Customer-facing teams were involved throughout to ensure the solution aligned with established workflows and regulatory requirements.

    Customer Service Control

    A key benefit of the approach is the level of control it gives internal teams. Keywords, sentiment thresholds, and classifications can be adjusted directly. This allows rapid refinement as customer behaviour changes or new regulatory considerations emerge, without waiting for development cycles.

    “Liberty Create has enabled my development team to work with remarkable agility. The ability to rapidly create and refine applications to meet ever-evolving business needs has significantly enhanced our efficiency. This allows us to deliver a wealth of new features to end users and customers with speed. With the integration of AI, we’ve been able to advance our processes while ensuring exceptional customer service. Our Sentiment Analysis application launch is a prime example of this.” Trina Burnett, Head of Engineering at Hampshire Trust Bank.

    The sentiment analysis system also supports automated and ad-hoc reporting. This provides a single source of insight into customer interactions and actions taken. This helps reduce manual effort, supports audit and compliance activity, and enables teams to continuously improve customer service operations.

    “As scrutiny around customer experience and accountability increases across UK financial services, the ability to listen, adapt and respond at pace is becoming a defining capability for banks seeking to maintain trust and service standards,” said Alex Ballingall, Key Account Manager at Netcall.

    “HTB’s approach shows how banks can use AI-driven insight practically. Turning customer communications into faster action without adding operational complexity,” Ballingall concluded.

    About Netcall

    Netcall is a leading provider of low-code and customer engagement solutions. A UK company quoted on the AIM market of the London Stock Exchange. By enabling customer-facing and IT talent to collaborate, Netcall takes the pain out of big change projects. It helps businesses dramatically improve the customer experience, while lowering costs. Over 600 organisations in financial services, insurance, local government and healthcare use the Netcall Liberty platform to make life easier for the people they serve. Netcall aims to help organisations radically improve customer experience through collaborative CX.

    Learn more at netcall.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Payments
    • Digital Strategy
    • Fintech & Insurtech
    • InsurTech

    Author

    Dr. George Papamargaritis & Dr. Konstantia Barmpatsalou

    Published

    26 February 2026

    Estimated Read time

    4Mins

    Obrela’s Dr. George Papamargaritis (EVP MSS) and Dr. Konstantia Barmpatsalou,  (Blue Team Support Manager) on why embracing a risk-led cybersecurity model will leave financial organisations better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems

    Cybersecurity in the financial sector was once viewed as a compliance-driven discipline. But as attackers have increasingly targeted institutions with sophisticated, persistent and often internally driven campaigns, it has become a strategic priority.

    According to the Digital Universe Report H1 2025, financial services were the second most targeted industry globally, accounting for 19% of all observed cyberattacks. This reflects both the sector’s value to adversaries and the complexity of the digital ecosystems it now operates within.

    Regulatory frameworks such as the FCA and PRA’s operational resilience rules, the EU’s Digital Operational Resilience Act (DORA) and NIS2 have strengthened baseline protections. However, the report’s findings demonstrate that regulation alone cannot deliver true cyber resilience. Institutions must adopt a strategic, risk-led approach that looks beyond compliance to understand real threats, behaviours and operational dependencies.

    Tailored, Internal and Stealthier Threats

    One of the most striking insights from the report is how targeted financial sector attacks have become. Industry-specific security risks now represent 32% of all incidents in the sector. This is an indication that adversaries are designing attacks using detailed knowledge of financial operations, from trading workflows to payment systems.

    Internal activity is also a major concern. Suspicious internal activity accounts for 26% of detections across financial services, reflecting the frequency of compromised accounts, misused privileges and lateral movement. For a sector historically focused on defending the perimeter, this shift highlights the need for deeper visibility into user behaviour and identity-driven risks.

    The wider threat landscape reveals adversaries are moving away from overt, signature-based attacks. In H1 2025, brute force activity made up 27% of global alerts, while vulnerability scanning accounted for 22% and known malicious indicators for 20%. Notably, direct malware payloads dropped to 0% of trending alerts, replaced by fileless techniques and living-off-the-land methods that bypass traditional defences.

    For financial institutions, this is a challenge. Many compliance requirements still centre on endpoint protection, patching and malware controls. These will of course, remain important, but they cannot address threats that are increasingly behavioural, stealth-driven and identity-focused.

    Operational Complexity

    The financial sector’s cyber risk is intensified by its expanding operational footprint. Cloud adoption, open banking, digital identity models and extensive third-party ecosystems have all created new points of exposure. Financial services operate within a global digital infrastructure that is both vast and increasingly interconnected. This level of complexity cannot be effectively protected through compliance checklists alone.

    Regulators are recognising these realities. DORA’s emphasis on ICT third-party risk, operational resilience testing and continuous oversight reflects the need for more proactive, intelligence-driven approaches. But DORA still only sets a minimum standard. True resilience requires institutions to move beyond regulatory expectations and embed cybersecurity into broader business strategy.

    Strategic, Risk-Led Cybersecurity

    A risk-led approach begins with understanding the threats that pose the greatest risk to operations and customers. Financial institutions remain priority targets for groups such as FIN7, TA505, Cobalt Group and various state-backed actors. Their tactics, such as credential harvesting, remote access tools, web-injection frameworks and lateral movement, are specifically designed to exploit the digital fabric of financial services.

    This evolving threat profile puts identity and behaviour at the heart of cyber defence. With credential-driven and internal threats so prevalent, institutions must prioritise behavioural analytics, continuous authentication and zero-trust models that verify users and devices contextually rather than relying on static controls.

    Strategic cyber resilience also needs to have continuous assurance. Traditional audits, annual testing and scheduled penetration exercises cannot keep pace with rapidly evolving threats. Leading institutions are shifting toward continuous control monitoring, automated attack simulation and persistent adversarial testing. These practices align with the Bank of England’s CBEST framework and demonstrate a sector-wide move toward ongoing, intelligence-led assurance.

    Crucially, cyber risk must be treated as an operational issue, not just a technical one. Embedding cybersecurity into enterprise risk management, financial planning, product development and board oversight is essential. This integrated approach also mirrors the direction of FCA and PRA regulation, which increasingly emphasises governance, accountability, and resilience across the entire organisation.

    Beyond Compliance

    Financial services underpin national economies and public confidence. As digital ecosystems grow and adversaries become more sophisticated, the sector faces a dual challenge: meeting rising regulatory expectations while defending against complex, targeted attacks. It is clear that cybersecurity must evolve from compliance-driven activity to a strategic capability built on intelligence, continuous assurance and behavioural insight.

    Institutions that embrace this risk-led model will be better positioned not just to meet regulatory requirements but to strengthen resilience, protect customers and uphold the trust that is so essential to the future of financial systems.

    Learn more at obrela.com

    • Cybersecurity
    • Cybersecurity in FinTech
    • Digital Strategy
    • Fintech & Insurtech
    • InsurTech

    Author

    Gregory Mostyn

    Published

    24 February 2026

    Estimated Read time

    5Mins

    Gregory Mostyn, CEO and co-founder of Wexler, on why the era of generalist AI tools is over, and how the future will focus on high-precision AI designed for specific industries

    For decades, the UK’s professional services sector, including areas such as Law, Insurance, and Wealth Management, has argued that its business value is locked in its access to proprietary data and the specialised labour required to navigate it. Investors, lured by the moat of institutional knowledge, priced these companies accordingly. However, the first quarter of 2026 has seen significant AI disruption within the professional services market. The catalyst wasn’t a single event, but rather a move by foundational model providers that turned the industry’s most defensible assets into commodities. 

    When Anthropic launched its specialised legal AI plugin, OpenAI integrated a real-time insurance underwriting engine directly into its interface, and Alturist Corp automated bespoke tax strategies, the market reacted harshly. As professional services titans such as RELX, MoneySuperMarket, and St James’s Place saw their share prices decline by more than 10% in a matter of hours, the message became clear: the era of treating AI as a ‘future risk’ is over. 

    The market has been awoken to the fact that foundational AI models are no longer just plugins or nice ‘add-on’ tools; they are competitors. The move by foundation-model providers into professional services – like the legal sector – is not a one-off shock, but rather an inevitability. 

    The Proliferation of Information 

    Historically, a law firm’s competitive advantage was its access to information – repositories of case law, proprietary research, and historical contracts. Investors and clients valued these companies on the assumption that this data constituted an impenetrable barrier to competitors. Before AI entered the mainstream, the cost of extracting actionable information from thousands of pages of data required a small army of junior associates and hundreds of billable hours. 

    In 2026, that moat has mostly evaporated. Recent benchmarks show that frontier models now achieve 80% accuracy on complex documents, compared with the 71% average of a human associate. More importantly, they do it at a fraction of the cost. It is now estimated that the inference cost for a system at the level of GPT-3.5 dropped by more than 280-fold between November 2022 and October 2024. It’s predicted that UK law firms will reduce their chargeable hours by 16% through the implementation of AI. 

    The narrative that AI would be able to handle only ‘low-level’ tasks, such as NDAs or simple contract summaries, has all but evaporated. Anthropic’s move into high-stakes litigation support validates this trend. 

    AI – From Swiss Army Knives to Scalpels 

    An error made by many law firms when AI became entrenched within the market was to treat it as a ‘plug-in’, a nice-to-have built onto existing internal software. Many adopted general-purpose tools, often referred to as ‘Swiss Army knife’ solutions, that covered the breadth of legal work but lacked the precision, jurisdictional nuance, and risk-weighted requirements for high-stakes professional services. 

    The 2026 market reaction highlighted the needs of a ‘scalpel’ approach – those that go deep in a specialised vertical within a legal workflow. For example, instead of a junior associate spending billable hours searching through case files to establish the facts of a case, they could use a ‘fact intelligence’ platform that can automate that process into minutes, whilst increasing accuracy by 95% versus 78% for human reviewers and up to 90% savings in large-scale litigation. The market is no longer rewarding firms for having information. Rather, it rewards those who can apply it at the lowest possible cost and friction. 

    Reallocating Capital Across Professional Services

    We’re already seeing investors withdrawing from the traditional software market and reallocating that capital into specialised AI firms. However, the risk for legacy players is that they are being disrupted from both ends. From the bottom, they are losing the efficiency game to generalist foundation models from companies such as OpenAI and Google, which are commoditising the ‘knowledge’ aspect of professional services, including basic advice and contract drafting. At the top, they are losing the expertise game to specialised firms that use AI as a precision instrument; their overhead would be lower than that of a traditional Magic Circle firm, allowing them to undercut prices while maintaining profit margins. 

    The result is a massive reallocation of capital. Investments into vertical AI (AI built for one specific industry) are expected to surge to $115 billion by 2034. The market no longer bets on labour with tools, but on autonomous workflows. Investors have realised that the value lies in the middle layer – the software that sits between a general foundation model and a specific industry’s needs. 

    Innovation or Obsolescence 

    So far, the first market fluctuation of 2026 has taught us that you cannot outrun new technologies. To survive, firms must stop treating AI as an add-on and treat it as a foundation for their core business infrastructure. 

    For UK professional services, the choice is no longer whether to adopt AI, but whether they can evolve quickly enough to avoid becoming the training data for companies building foundational models. The firms that remain in 2030 will recognise that the competitive landscape has changed. You’re not just competing with your peers, but with the compute cycles of the world’s most powerful AI labs. 

    The era of generalist AI tools is over, and the future will focus on high-precision AI designed for specific industries. 

    Learn more at wexler.ai

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Jack Bingham

    Published

    20 February 2026

    Estimated Read time

    5Mins

    Jack Bingham, Regional Director of Digital Native UK, Ireland & South Africa, Confluent on how data, treated properly, compounds in value to drive digital disruption

    When I talk to founders and tech leaders, one question seems to consistently come up: what separates today’s disruptors from the last decade’s? In 2010, being cloud-first was what made investors sit up and take note. In 2026, it will be streaming-first.

    I’ve spent the last year or so working closely with companies that are, quite literally, building their businesses in real time. For them, real-time capability isn’t a department or a layer that supports the business. It is the business. The acid test is simple: how quickly can you capture a critical event – a payment, a login, a failed delivery – and respond with the next best action? That focus shapes how they build products, structure teams, and think about innovation.

    Here’s what I’ve learned from them:

    Lesson 1: Data is a Product, Not a By-Product

    Many traditional companies still treat data as something to collect, store, and analyse later. The new generation of businesses, on the other hand, treats it as a reusable, governed product that everyone can access. When it’s built and shared this way, teams stop rebuilding the same foundations for every new use case. They move faster because they’re working from a single, trusted view of the truth, shortening product cycles, speeding up iteration, and spending more time solving problems that matter.

    That mindset, rather than the size of the tech stack or the number of engineers, is what sets disruptive businesses apart. In these organisations, technology, data, and business strategy move in lockstep. Decisions aren’t passed up and down hierarchies, they’re made by teams who understand both the data and the customer problem in front of them.

    When you can trust your data and respond in real time, innovation stops being a department. It becomes a reflex.

    Lesson 2: Real-Time isn’t a Feature, it’s a Foundation

    A few years ago, one of the world’s largest supermarket chains realised it didn’t have a single real-time view of its inventory. Without that visibility, omnichannel experiences were impossible. Once it shifted to a streaming architecture, every transaction became a live event that updated stock, triggered supply chains, and even made it possible to get your groceries delivered straight to your kitchen fridge – coordinated through live inventory data, smart home devices, and real-time security feeds.

    That’s the practical power of streaming: it connects what happens in your business to what should happen next so you can provide products and services that take customer satisfaction to a whole other level. Real-time data stops being a reporting tool and becomes the foundation of every decision, interaction, and innovation.

    I often ask businesses what they would do differently, if they knew the state of every event in their organisation. The most forward-thinking companies already have the answer. They’re using streaming to turn business events into reusable building blocks, creating new experiences by connecting the data they already have in smarter ways.

    Lesson 3: Culture is the Multiplier

    Being streaming-first is only half about architecture. The other half is attitude. The best digital enterprises don’t wait for permission to experiment. They map their most important business events, align teams around them, and empower people at every level to react fast and learn faster.

    And the difference is visible. Feedback loops are shorter. Structures are flatter. Failure is treated as information. This culture of continuous experimentation is why these companies can move at the pace they do.

    We often run ‘Event Storming’ workshops with teams to map their critical business events. The idea is to create alignment – getting people from engineering, product, and operations to agree on what really matters and how those moments connect. That process reveals a lot. 

    Digital disruptors go beyond simply deploying streaming architectures. They build streaming mindsets. Leadership plays a crucial role here: data must be treated as a strategic asset. If it isn’t up top, it won’t be anywhere else in the organisation either.

    Lesson 4: Streaming and AI will Converge

    AI is only as good as the data you feed it. Unfortunately, most enterprises are still feeding it yesterday’s data. Streaming-first companies already know this. They’re building intelligent data pipelines that give AI the context it needs to make decisions in real time.

    That’s how the next generation of innovators will pull ahead: not by having bigger models, but by having cleaner, faster, more connected data. Streaming is what will let AI move from reactive to predictive… and from predictive to autonomous.

    Too many organisations are cutting investment in data while pouring money into AI projects. But AI without quality data is just expensive guesswork. The companies doing this well understand that data has to be a product in its own right. And when business and technology teams design around that shared understanding, innovation follows naturally.

    Lesson 5: The Mindset of the Next Disruptors

    If I were starting a company tomorrow, I’d look closely at the critical events that run my business. I’d then make sure I had a way to capture those in the stream, make them reusable, and build every product and process around them. 

    When your business can see and act on what’s happening in the moment, you gain something no traditional architecture can give you: time. And in the next wave of disruption, that’s the only advantage that really matters.

    If we look to who we can learn from in the coming months, it’s financial services and healthcare that are moving the fastest. Real-time fraud detection, patient monitoring, and risk management are becoming operational necessities – and these industries will set the benchmark for real-time data excellence. 

    Looking Ahead to 2026

    By 2026, I don’t think we’ll talk about ‘real-time’ as a differentiator. It will simply be how modern businesses operate. Batch systems won’t disappear, but they’ll coexist within a single, streaming-first platform that delivers data whenever it’s needed.

    Once every process can react instantly, the question then becomes: can it anticipate? Can it learn? That’s where AI and streaming meet and where we move from reactive to autonomous enterprises that not only respond to the present but adapt to what’s coming next.

    Data, treated properly, compounds in value. The decisions you make with it become faster, sharper, and more confident. The companies that understand this will be the ones still leading when today’s titans look like yesterday’s news.

    Learn more at confluent.io

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Payments
    • Digital Strategy
    • Embedded Finance

    Author

    Jonny Combe

    Published

    19 February 2026

    Estimated Read time

    4Mins

    Jonny Combe, President and Chief Executive Officer, PayByPhone on how urban mobility is evolving from car-centric to multimodal and the opportunity the parking industry has to play a central role by integrating payment infrastructures that support a more connected, flexible mobility ecosystem

    The journey has changed. Over the past few years, the mobility industry has undergone seismic shifts toward more digital experiences. Cash payments continue to disappear and in the US made up only about 14% of all payments in 2024. Over half of the US adult population make use of mobile wallets and many companies provide payment opportunities via apps for their services. While this has made some processes more efficient and streamlined, it has also resulted in very fragmented data streams.

    Consider this scenario: a commuter drives an Electric Vehicle (EV) to a rural or suburban transit hub where they park and charge, then boards a train into the city. The final mile is completed on an e-scooter, shared bike or another mode of public transport to reach their destination. One journey, four separate payment interactions across four different apps.

    This is the daily reality for millions of commuters, and it exposes a fundamental challenge that not only the parking industry, but also the mobility industry as a whole must confront. Continuing to build payment infrastructure for journeys that end at the curb, is no longer enough; we should be facilitating one system for these evolved modern journeys.

    City Centres Reimagined

    A substantial amount of land in city centers has traditionally been dedicated to parking, but there is a growing trend where we see city centers worldwide redesigning their urban space. On-street parking is giving way to pedestrian zones and cycle lanes. Traditional car parks are transforming into multimodal hubs that are integrating EV charging, micro-mobility stations, and last-mile logistics. Technologies like automatic number plate recognition are helping to eliminate friction at entry and exit points. However, backend complexity of the redesign of urban mobility has grown exponentially.

    Local authorities now juggle relationships with cashless payment providers, meter operators, EV charging networks, micro-mobility vendors, and logistics partners. Each bring their own payment rails, reconciliation requirements, and data formats. For many municipalities, simply reconciling payments between a meter provider and a digital parking platform already strains finance teams. Adding multiple mobility partners brings a significant extra load to existing operational capacity and the operational burden is only part of the equation.

    The Hidden Cost of Fragmentation

    The more critical issue is strategic: fragmented payment systems can create fragmented data, and fragmented data can undermine intelligent policy.

    When payment information sits in siloed systems across multiple vendors, authorities lack the consolidated view needed to answer essential questions:

    • How does parking behavior correlate with public transit usage?
    • What pricing strategies would optimize utilization across the entire mobility network?
    • Where should we invest in EV infrastructure based on actual demand patterns?
    • How do we measure progress toward carbon reduction targets?

    Without integrated payment and usage data, cities are making significant capital infrastructure decisions with an incomplete picture.

    The Payment Layer as Strategic Infrastructure

    Forward-thinking cities are, however, beginning to recognize payment infrastructure not as back-office plumbing, but as strategic architecture for the mobility ecosystem.

    The solution lies in centralized payment platforms that serve as a unifying layer – ‘super apps’ as they are called in other industries. The backend of these apps should be able to consolidate transactions across multiple mobility services, automate complex multi-party reconciliations, and create unified data lakes that enable AI-driven insights.

    This approach can deliver immediate operational relief: finance teams spend less time manually reconciling disparate systems, and the strategic value compounds over time. With consolidated data, authorities can model the true economics of mobility transitions, identify underutilized assets, dynamically price services to manage demand, and measure environmental impact with precision.

    Building for What Comes Next

    The parking industry has always been about managing physical space, yet the future is about orchestrating mobility experiences. The question for industry leaders isn’t whether parking will integrate with broader mobility systems but whether parking operators will architect that integration intentionally.

    Doing so requires a fundamental rethink of the role parking payment providers play in the payment value chain, while investing and building the technology and the payment infrastructure that makes seamless, sustainable urban mobility possible.

    The infrastructure we build today will determine whether cities can deliver on their mobility and sustainability commitments tomorrow. For parking industry leaders, this is both a challenge and an opportunity: to evolve from transaction processors into the essential connective layer of urban mobility. Those with the vision, and the technological ability to rise to that challenge, have a real opportunity to lead the next generation of multimodal mobility payments.

    About PayByPhone                                                     

    PayByPhone is a global leader in mobile parking payments. We simplify journeys for millions of UK drivers with smart, intuitive technology and user-focused features. In addition to fast, secure parking payments, drivers can also locate nearby fuel stations and EV chargers – and pay for EV charging – all in the PayByPhone app. We work with over 1,300 cities and operators across the UK, North America, France, Germany, and Switzerland. More than 110 million drivers worldwide have downloaded the PayByPhone app to simplify their parking and vehicle payments to date. To discover how our products and services can elevate your driving experience.

    Learn more at paybyphone.co.uk

    • Digital Payments
    • Digital Strategy

    Author

    Trilliam Jeong

    Published

    12 February 2026

    Estimated Read time

    4Mins

    Trilliam Jeong, CEO at WealthBlock on why pairing credit discipline with real-time reporting will deliver a better position to hold onto investor confidence

    There’s no shortage of noise around the direct lending market right now. On one hand, deal activity remains strong, capital continues to flow in and investor appetite hasn’t wavered. On the other, competition is fierce, rates are edging down and macro conditions are less forgiving than they were a year ago.

    But strip out the headlines and the fundamentals still look solid. The demand is there, both from borrowers looking for speed and flexibility and from investors chasing yield and consistency. That puts direct lenders in a strong position, provided they’re prepared to adapt.

    Operational Shift

    One of the most significant shifts underway is operational. We’re seeing real adoption of technology across the mid-market from AI-assisted onboarding to fully digitised investor dashboards. This isn’t just cosmetic. Faster processes and clearer visibility mean capital can move more quickly, investors stay better informed and managers have more room to protect margins, even in a tightening spread environment.

    LP expectations are shifting too. Many now expect a consumer-grade digital experience from the platforms they commit capital to. They want real-time access to reports, frictionless communication and clarity around how their money is being deployed. That shift in expectations is accelerating the tech arms race across the mid-market. It’s no longer about who can show the best deck but rather can deliver the best infrastructure. And as investor sophistication grows, that infrastructure is becoming a non-negotiable.

    Digital Infrastructure

    That shift is also influencing how mandates are awarded. Institutional investors increasingly view digital infrastructure not as a bonus, but as a sign of long-term readiness. Questions that once focused solely on deal pipeline and past performance now extend to data availability, reporting cadence and system resilience. It’s not just about what a manager can deliver but how transparently and reliably they can do it. As more allocators run tighter operational due diligence processes, digital maturity is quietly becoming a competitive edge. Platforms that can demonstrate consistent, tech-enabled processes are better positioned to win, and keep, capital.

    That matters, because rates may not stay where they are. Increased competition is already putting pressure on pricing. But firms with strong digital infrastructure are better placed to absorb it. Operational leverage, not just headline yield, is becoming a key differentiator.

    Scaling Up

    There’s also the issue of scale. Consolidation is real and it’s reshaping the market. The biggest managers are only getting bigger and their resources are hard to match. But size alone isn’t the whole story. Technology is giving smaller and mid-sized players a way to compete on experience even if not on balance sheet. A seamless, professional, tech-forward investor journey can carry real weight with LPs, particularly those who value speed and clarity over brand.

    That’s especially relevant for new entrants. There’s no shortage of managers in direct lending and standing out requires more than just a different strategy. Yes, some are carving out a niche in NAV lending, venture debt or structured credit but what really earns attention is trust. That comes from clear communication, repeatable processes and a level of transparency that goes beyond the marketing deck.

    The Outlook for Lending

    The macro outlook is part of the equation too. With corporate defaults expected to rise, discipline is going to matter more than it has in recent years. Underwriting strength, sponsor alignment and proactive portfolio monitoring are back in focus. Investors will be watching for signals that managers are prepared for downside risk. The tougher the environment, the more exposed weaker systems become. Inconsistent reporting, vague valuation logic or delayed updates might have been tolerated in a bull market – but not now. Allocators want to know how a manager will behave under stress, not just how they perform when everything’s going to plan. That makes operational maturity as important as deal-level returns.

    Firms that pair credit discipline with real-time reporting will be in a better position to hold onto investor confidence. Allocators are already asking more pointed questions and looking for managers who can back up claims with data. There’s still plenty of room to grow in direct lending, but it won’t be enough to rely on past performance or broad market tailwinds. The firms that outperform from here will need to be efficient, responsive and trusted. In a more competitive, more transparent and more regulated market, those are the traits that will endure.

    Learn more at wealthblock.ai

    • Blockchain & Crypto
    • Embedded Finance
    • Fintech & Insurtech

    Author

    JP Cavanna

    Published

    11 February 2026

    Estimated Read time

    5Mins

    JP Cavanna, Director of Cybersecurity at Six Degrees, on balancing the risks and benefits of AI in cyber defence strategies

    Undeniably, AI is here to stay. Having become part of day-to-day life, it’s hard to remember what life was like without it. But when it comes to cybersecurity, is it causing more harm than good?

    Recent research outlines that 73% of organisations have already integrated AI into their security posture. The technology is clearly becoming a cornerstone of modern cybersecurity. Organisations are turning to AI not just as a tool, but as a partner in security operations, leveraging its capabilities to identify malicious activity faster, guide investigations, and automate repetitive tasks.

    For it to be truly effective, though, AI must be paired with human expertise – but this is where organisations are starting to become complacent. Given the growing sophistication of cyber-attacks, and even AI-powered attacks, many are removing the human element while expecting AI tools to do all the work for them, leaving them even more vulnerable to threats. This overreliance risks creating blind spots, where critical thinking, contextual understanding, and instinct are overlooked. Without the balance of human judgement, AI can amplify mistakes at scale, turning efficiency into exposure.

    The Cybersecurity Paradox

    This situation puts many organisations in a potentially difficult position. On the one hand, AI can significantly improve the efficiency of security operations. In the typical SOC, for example, AI technologies can process alerts in around 10-15 minutes. This represents a significant improvement over human analysts, who can easily require twice as long for the same task.

    Aside from the obvious efficiency gains, applying AI to these repetitive, time-pressured processes can also significantly reduce the scope for human error. And in turn, take considerable pressure off security analysts. Going some way to battling alert fatigue, an increasingly well-documented and persistent problem. In these circumstances, valuable human experience and specialist expertise can instead be more effectively applied to complex investigations, strategic decision-making, and other higher-value priorities.

    On the flipside, however, AI remains prone to generating inaccurate or misleading insights, and users may not realise they are applying the wrong information to potentially serious security issues. Similarly, habitual blind trust in AI outputs can easily erode performance levels and even introduce new vulnerabilities. There is also scope for sensitive data to enter public environments, with the potential to cause compliance issues. This kind of information can also reappear in future versions of the AI model in question, therefore resulting in further data exposure risks.

    Parallels with IoT Adoption

    The situation mirrors that seen in the early days of IoT adoption, where the rush to innovate would often override security considerations. In this current context, therefore, human oversight and vigilance are extremely important. Clear governance frameworks, defined accountability, and continuous monitoring must underpin any AI deployment. Therefore ensuring that innovation does not outpace risk management or compromise long-term resilience.

    A Growing Arms Race

    If that wasn’t challenging enough, threat actors are also in on the AI boom in what has already been described as an ‘arms race’. In practical terms, AI tools are already widely used to create more convincing phishing attacks free from some of the more obvious traditional tell-tale signs of criminal intent, such as imperfect grammar or a suspicious tone.

    Deepfake technology has also raised the stakes. We’ve all seen how convincing AI-generated video has already become. This is now finding its way into real-world examples, with one fake video reportedly causing a CFO to authorise a large financial transfer as a result.

    At the same time, technology infrastructure is constantly under attack by AI-powered tools. They can be used to analyse defensive systems and identify weaknesses faster than humans. The net result of these developments is that defenders constantly play catch-up, as they can only respond to new attack vectors once discovered. The underlying takeaway is that at present, AI cannot be trusted to operate autonomously. Instead, human intuition, scepticism and contextual understanding remain essential to spotting emerging tactics.

    As attackers refine their methods at machine speed, organisations need to resist the temptation to match automation with automation alone. They must double down on strategic thinking and continuous skills development.

    Balancing Benefits and Risk

    So, where does this leave security leaders who are looking to balance the benefits and risks? Firstly, and to underline a fundamental point, while AI offers scale and speed, it cannot replace critical human oversight. Organisations should view AI as an enhancer, not a replacer. Success lies in promoting partnership, not substitution.

    Strong governance is vital. This should start with clear AI usage policies that define what can and cannot be shared with AI tools, while proper data classification and access control ensure that sensitive information is protected. In addition, regular validation of AI outputs can help to prevent inaccurate or misleading results from being unnecessarily acted upon.

    Then there are the perennial challenges associated with employee awareness training, which is vital for avoiding complacency and understanding the limitations of generative AI tools. Cyber leaders should also monitor how AI is being used inside and outside the corporate environment, as staff often experiment with tools on personal devices.

    Get this all right, and security teams can put themselves in a very strong position to embrace AI, safe in the knowledge that they have the guardrails and processes in place to balance innovation and efficiency with effective human-led oversight. Ultimately, success will depend not on how much AI is deployed, but on how intelligently it is governed and refined alongside the people responsible for securing an organisation.

    Learn more at Six Degrees

    • Artificial Intelligence in FinTech
    • Cybersecurity
    • Cybersecurity in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Dan Nichols

    Published

    23 January 2026

    Estimated Read time

    8Mins

    Dan Nichols, Chief Technology Officer at virtualDCS, on why cloud resilience in the financial services sector hinges on shared accountability and an assume-breach philosophy

    A powerful catalyst for transformation, the cloud is reshaping how organisations compete in the financial services sector. Beyond significant cost savings and flexibility, leaders are eager to unlock the potential of AI-driven insights, intelligent automation, and real-time business modelling. And, in a space governed so strictly by data sovereignty and privacy policies, the cloud’s ability to localise, encrypt, and control data has made it a key enabler of compliance and customer confidence.

    But as threats become more frequent and sophisticated – with attackers now targeting shared platforms and partner supply chains – organisations can no longer rely on their own defences alone. For true digital resilience, shared accountability, collective readiness, and clear governance across every cloud touchpoint are equally non-negotiable.

    All Eyes on the Money

    The industry sits at a valuable intersection of data, technology, and finance. A combination that makes it uniquely attractive to attackers. It holds some of the world’s most sensitive data, directly underpins the flow of global capital, and operates through deeply complex and interconnected systems. With every integration increasing the risk of exposure. Ultimately, the attack motivation is as simple and relentless as it is in most sectors: monetary gain. Cybercriminals target institutions precisely because of the value at stake and the speed at which disruption translates to loss.

    How the Threat Landscape is Evolving

    Ransomware groups may see insurers and payment providers as high-yield targets. They understand even seconds of downtime can induce multi-million pound losses. Under pressure to protect customer trust and avoid regulatory penalties, some firms may choose to pay in order to restore their service quickly. This dangerous perception only encourages repeat targeting and paves the way for damage to spread even further. Yet it remains a common response tactic among many.

    At the same time, the rise of supply chain and third-party attacks has made it possible for criminals to bypass even the most well-defended cloud environments. By exploiting shared platforms, managed service providers, and cloud-hosted applications, perpetrators can move laterally across multiple organisations at once, amplifying both the reach and impact of their attacks. In other words, infiltrating one vendor’s weakness can cripple an entire network in one carefully coordinated strike. And, since some firms may overlook the cloud’s shared responsibility model – presuming end-to-end security sits solely with their cloud provider – multiple blind spots can inevitably emerge, creating easy openings to exploit.

    In an environment where boundaries blur and dependencies multiply, traditional perimeter-based defences are no longer enough. Hybrid and multi-cloud infrastructures demand continuous visibility, faster detection, and coordinated response across every partner and provider. The goal is not simply to prevent breaches, but to withstand and recover from them collectively. It’s about recognising that in today’s ecosystem, no financial institution is secure in isolation.

    Inside the Ransomware Economy

    Evolving beyond the scattergun attacks of the past, ransomware now operates as a professionalised, profit-driven ecosystem, where malicious actors collaborate, trade intelligence, and lease attack tools much like legitimate software vendors. The rise of ransomware-as-a-service (RaaS) has even lowered the barrier to entry, giving less skilled affiliates access to ready-made payloads and automated encryption kits in exchange for a percentage of the ransom.

    What makes it especially destructive is the precision and psychology behind the attacks. Rather than randomly striking, attackers conduct weeks of reconnaissance – learning behaviours, studying employee hierarchies, and identifying systems most critical to operations. They often infiltrate through phishing emails or compromised credentials, quietly moving laterally through the network to gain elevated access. Once embedded, they disable defences, exfiltrate sensitive data, and target backup repositories before finally encrypting production systems.

    At that point, the goal shifts from technical control to financial coercion. Victims are locked out of their systems and presented with a ransom note demanding payment, sometimes in cryptocurrency, in exchange for a decryption key. Increasingly, the threat includes public exposure of stolen data – a tactic designed to pressure leadership into paying to protect their reputation and customer trust. Even when ransoms are paid, recovery is rarely clean: data may be incomplete, corrupted, or resold on the dark web, and repeat targeting is common once an organisation is identified as a payer.

    It’s this blend of stealth, strategy, and human manipulation that makes ransomware so difficult to defend against. By the time the encryption begins, attackers have already spent weeks ensuring recovery options are limited. This background isn’t designed to scaremonger, but to highlight why resilience must start long before an attack ever reaches the endpoint.

    The Foundations of Ransomware Resilience

    Ransomware resilience isn’t achieved through a single product or policy – it’s the outcome of strategic, technical, and cultural alignment. Financial institutions, in particular, must approach it as a continuous process of readiness: Anticipating compromise, containing impact, and restoring normality quickly and transparently:

    Assume-Breach Philosophy

    The first step is shifting from a defensive mindset to an assume-breach philosophy. In practice, this means recognising that even the most sophisticated systems can and will be breached – and building architectures and response strategies designed to limit damage when this happens. It’s a pragmatic approach, grounded in the reality that attackers are increasingly sector agnostic. No organisation is too small or too secure to be targeted, but the financial sector remains a favourite because it offers both high disruption value and potentially significant monetary reward.

    Building meaningful resilience, therefore, demands layered defence and disciplined execution. The goal is to slow attackers down at every stage – detecting them early, limiting lateral movement, and ensuring business continuity when systems are disrupted. Behavioural analytics and continuous monitoring can surface and neutralise subtle anomalies that would otherwise go unnoticed – such as phishing, spear phishing, and malware, with email still the number one entry point for ransomware.

    Zero Trust & MFA

    Meanwhile, zero trust policies and multi-factor authentication methods add a second layer of protection, blocking unauthorised access even if credentials are compromised.

    When incidents do occur, a well-practised response framework ensures action is fast and coordinated, minimising disruption across critical systems, with the ability to switch to secure replica environments to keep operations running while remediation takes place. Secure, immutable, air-gapped backups underpin it all, providing a safety net that guarantees recovery can begin from a clean and uncompromised state.

    Human readiness is equally critical. Technology can contain an attack, but only people can recover from one effectively. Regular simulation exercises, incident rehearsals, and cybersecurity awareness training help teams respond calmly and cohesively, transforming response from reactive to instinctive. This operational maturity is reinforced by strong governance. Frameworks such as DORA, NIST, and ISO 27001 provide the structure to align technical teams, compliance leads, and executive decision-makers around shared resilience goals. When combined with skilled practitioners and clear accountability, they embed security into ‘business as usual’ – moving resilience from a strategy to a sustained organisational capability.

    Why Multi-Layered Backup is Critical

    When ransomware strikes, the speed and integrity of data recovery determine whether disruption lasts minutes or days – and whether the impact cascades through wider global markets. As the last and most decisive line of defence when every other control fails, it’s also fundamental to customer trust and compliance. Yet too often, backup is treated as a static safeguard rather than a dynamic resilience layer.

    Since modern ransomware often seeks out and encrypts traditional backups first, a single backup copy or centralised repository is no longer sufficient. True resilience today depends on a multi-layered approach – combining offsite or cloud-diverse storage, immutable data copies that cannot be altered or deleted, and isolated environments to protect against lateral movement.

    How frequently these backups are tested is equally important. Too often, financial institutions only discover weaknesses when recovery is already underway, at which point strategies can’t be magically strengthened, and it becomes a race against the clock to minimise downtime and reputational fallout. Regular, automated recovery testing changes that dynamic. It not only confirms that files can be restored, but provides verifiable assurance that systems come back online in the correct order, data dependencies remain intact, and teams have the muscle memory to act quickly and confidently when the worst happens.

    The Power of Shared Accountability

    In a digital economy so deeply interconnected, no organisation operates in isolation. This is especially true in financial services, where supply chains and service providers form the backbone of day-to-day operations. While this interdependence is a strength in many ways, it also means resilience is no longer defined by how well a single institution can defend itself, but by how effectively every partner in its ecosystem upholds their part of the security chain.

    This is where shared accountability becomes critical. It recognises that cloud providers, managed service partners, and financial institutions each have distinct but complementary roles to play in securing data, systems, and infrastructure. When accountability is clearly defined – and when partners collaborate rather than operate in silos – visibility improves, incident response accelerates, and the risk of systemic failure decreases.

    Shared accountability also extends beyond contractual obligation. It’s about building a culture of collective readiness: sharing intelligence, rehearsing joint incident scenarios, and supporting smaller or less-resourced partners to raise their security baseline. The result is a unified entity capable of anticipating, absorbing, and recovering from disruption together.

    Looking Ahead

    To view cyberattacks as inevitable might seem pessimistic to some, but it’s an unfortunate truth that no amount of investment can eliminate risk entirely. In an era where threats are growing in both scale and sophistication, readiness becomes the true differentiator – particularly in such a high-stakes sector. For financial institutions, that means embedding security into culture, strengthening connections across supply chains, and continually testing their ability to withstand and recover as a united ecosystem. Only then can resilience become a strategic advantage rather than a defensive necessity, and unlock the cloud’s transformative potential with absolute confidence.

    Learn more at virtualcds.co.uk

    • Artificial Intelligence in FinTech
    • Cybersecurity
    • Cybersecurity in FinTech
    • Data & AI
    • InsurTech

    Author

    Dan Brightmore

    Published

    21 January 2026

    Estimated Read time

    2Mins

    New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving…

    New research from myPOS, the European payments provider for small and medium-sized businesses, reveals that Britain’s shift toward tap-to-pay is leaving traditional PIN codes behind. As contactless becomes the country’s top payment preference, almost a third of young adults now admit they can’t remember the four digits once central to everyday spending.  

    myPOS data reveals 29% of Gen Z struggle to remember, or have completely forgotten, their PIN. Highlighting how digital-first habits are shaping consumer behaviour. However, it isn’t just younger groups that are feeling the effects. One in five Boomers (20%) say they face the same issue as reliance on physical cards significantly declines. 

    Contactless Payments

    This shift has been driven largely by the dominance of contactless card and mobile payments. Over two-thirds of Brits (69%) say tapping, via card, mobile phone, or smartwatch, is now their primary method of payment. In contrast, just 16% rely mainly on chip and PIN, and only 14% primarily use cash. A further 10 % of Brits now live entirely wallet-free, using only their mobile or smartwatch for day-to-day spending. 

    Convenience-led behaviours are accelerating the decline of PIN usage across the UK. Nearly half of British consumers (47%) say they would happily go completely contactless if it meant shorter queues in shops and venues. Flexibility and convenience (42%) and speed (34%) remain the largest drivers behind the rise of tap-to-pay.  

    “As the UK embraces contactless and mobile payments, it’s clear that the traditional PIN is becoming less central to everyday transactions. Businesses and payment providers should ensure security and convenience go hand-in-hand, while recognising that consumer habits are evolving rapidly.”

    Michael Ault, Country Manager at myPOS UK

    • Digital Payments
    • Fintech & Insurtech
    • Neobanking

    Author

    Dan Brightmore

    Published

    19 January 2026

    Estimated Read time

    5Mins

    Michael Ault, Country Manager at integrated payments specialists myPOS, offers strategic advice for SMEs looking to scale through digital transformation and diversification

    Scaling a small business is one of the most rewarding, yet complex journeys for any entrepreneur. While growth brings opportunities for greater reach, higher revenue, and stronger market presence, it also demands foresight, discipline, and the ability to manage risk strategically. Securely integrating new technology is the main obstacle for 47% of SME’s, even though 76% of these businesses intend to expand their IT investment. This underscores a key point of tension, as many businesses want to grow through digital transformation but struggle to do so securely and sustainably.

    The business landscape continues to evolve with changing customer expectations, technology, and economic conditions. For UK SMEs, the key to long-term success lies in achieving growth but also in building resilience. Sustainable scaling comes down to three principles: embracing technology pragmatically, diversifying intelligently, and investing in people and partnerships that strengthen resilience.

    Leveraging Digital Transformation

    Digital transformation is the foundation of business growth, especially for small business. Cloud-based solutions, automation, and data analytics help to streamline operations, reduce inefficiencies, and create better customer experiences. However, transformation must be purposeful, not performative.

    The smartest approach is to scale technology investment incrementally, integrating flexible, modular systems that evolve with business needs. This approach not only lowers risk but also helps ensure digital maturity evolve over time. When SMEs use modular, cloud-based technology, operations run more smoothly and changes can be effectively analysed. Ultimately, resilience is not built through one-time upgrades but through a culture of continuous digital evolution.

    Diversifying Revenue Streams

    Depending on a single product, service, or market leaves a business vulnerable to sudden changes in demand. Diversification, when guided by customer insight and data can turn volatility into opportunity. Expanding into online sales, introducing subscription models, or targeting fresh customer segments can make income streams much more stable and sustainable.

    At myPOS, we know that even simple changes based on data, such as adding additional payment options or tapping into cross-border e-commerce, can help cash flow and protect against market shocks. The goal of technology is to mitigate specific challenges without adding layers of complexity.

    Investing in Employee Development

    Your people are pivotal to your ability to grow as a business; empowered teams are the engine of sustainable scale. A team that feels supported and motivated will bring fresh ideas, adapt to challenges, and push the business forward. Investing in training, mentoring, and development opportunities builds skills that pay back in the form of innovation and improved performance.

    In fast-changing industries, having employees who are confident in learning and adapting can make the difference between struggling through disruption and taking advantage of it. Equally, strong partnerships extend this resilience beyond the organisation. Building resilience at the team level creates resilience for the whole business, so fostering a culture of continuous learning and celebrating employee contributions is key to maintaining motivation.

    Focusing on Financial Health and Flexibility

    Financial resilience underpins sustainable growth. Scaling often requires upfront investment, and without healthy cash flow or reserves, opportunities can be lost. Monitoring income and expenses closely, cutting unnecessary costs, and preparing for seasonal fluctuations gives businesses more control.

    Having flexible financing options, like credit lines, small business loans, or even crowdfunding, provides a level of agility. Instead of being caught off guard by unexpected challenges, businesses with financial flexibility are positioned to respond quickly and strategically.

    Financial management software can make it easier to track performance, spot issues early, and forecast future needs. When you can see your finances in real time, you can make proactive, data-driven decisions instead of waiting for problems to happen. In markets that change quickly, this kind of financial management helps small firms plan with confidence, stay flexible, and establish a stronger base for long-term growth.

    Prioritising Customer Relationships and Feedback

    Your customers are not just buyers; they are advocates, sources of insight, and the foundation of repeat business and brand loyalty. Businesses that scale successfully often place customer relationships at the heart of their strategy by actively gathering feedback, responding quickly to issues, and personalising interactions, which shows customers they are valued.

    This loyalty becomes a form of resilience. In periods of uncertainty, a base of satisfied, returning customers provides more stability than constantly chasing new ones. Successful businesses use CRM tools to track customer preferences and automate follow-ups so no opportunity to strengthen a relationship is missed.

    Building Strategic Partnerships

    Partnerships can accelerate growth while also spreading risk. Working with other businesses, organisations, or influencers can provide access to new audiences, shared expertise, or additional resources. Collaboration can also create opportunities for joint marketing, co-branded initiatives, or innovative product and service offerings.

    In times of uncertainty, strong partnerships act as a support network. By aligning with others who share your values and vision, you create opportunities that are mutually beneficial and more resilient than going it alone. It is important to find partners whose goals and audiences complement your own for the best long-term impact.

    The next stage of small business success will be defined by resilience rather than speed, the ability to adapt, recover, and continue to create value in the fact of uncertainty. For SMEs, this means developing adaptable growth plans that include flexible technology, diverse models and empowered employees.

    Learn more at mypos.com

    • Data & AI
    • Digital Payments
    • Digital Strategy
    • Fintech & Insurtech

    Author

    Dan Brightmore

    Published

    16 January 2026

    Estimated Read time

    5Mins

    Ben Goldin, Founder and CEO of Plumery, explores the key banking trends for 2026 – from fraud and digital assets to stablecoins and AI applications

    As we head into the second half of the decade, several emerging trends will come to the fore in 2026. The interconnectedness among these trends is also noteworthy. Artificial intelligence (AI) and progressive modernisation act as common threads.

    A strong current throughout 2026 is the shift from customer-first banking to human-first banking. This relates to the concept of ethical banking. It focuses on creating financial services that have a positive social and environmental impact. 

    Human-first banking aims to get even closer to the customer by understanding their actual human needs, rather than just consumer needs. For example, a bank should be acting as a coach to improve a customer’s financial health, not solely as an advisor on which products they should buy. Banks can build trust in a digital world through tailored and empathetic interactions, effectively simulating the experience customers formerly had with their personal banker.

    To attain that level of hyper-personalisation, banks will need to be capable of processing vast amounts of transactional data, which can only be accomplished by deploying AI and big data tools. This requirement, in turn, will turbocharge progressive modernisation, another trend that has been bubbling under the surface for the past few years.

    Traditional banks are using progressive modernisation to deal with legacy infrastructure that is not fit for purpose in a digital-first, AI-driven world. Instead of a big bang replacement of core banking systems, which is risky and can take years, banks are creating change from within existing architecture. Banking is leveraging technologies that support a multi-core strategy. With this approach, banks can add new cores for specific products that require greater agility and innovation. Modern cores are necessary for deploying the latest AI and big data tools because they provide a unified, real-time data foundation to deliver hyper-personalisation.

    Fraud Threats

    Fraud will remain a top concern throughout 2026. Adversaries use AI to expand the range of techniques, such as impersonation scams and identity theft, as well as accelerate and scale fraudulent activity.

    According to the UK Finance Half Year Fraud Report 2025, £629.3 million was stolen by criminals in the first six months of this year, and there were 2.09 million confirmed cases across both authorised and unauthorised fraud. Card not present cases rose 22% to 1.65 million and accounted for 58% of all unauthorised fraud losses.

    However, the good news is that there was a 21% increase in prevented card fraud in the first half of 2025. The £682 million which was stopped from being stolen is the highest-ever figure reported.

    To combat fraud, new and improved tools to help banks identify, verify and onboard customers will come to market in 2026. The move away from paper-based identity (ID) and widespread adoption of digital ID will play a key role in the fight against fraud. Hence the UK government’s recently announced plans to roll out a new digital ID scheme.

    In addition, I expect to see a fundamental shift in fraud detection using real-time behavioural analytics, data analytics for proactive risk identification, and other applications of AI and machine learning in this space.

    Digital Assets and Stablecoins

    Digital ID verification is also essential for fighting fraud in the digital assets and stablecoins space. Another hot topic at several banking and payments industry conferences last year.   

    In 2026, digital assets and stablecoins will become much more mainstream. Banks have left the sidelines and are now actively engaged with running pilots. For example, in September a consortium of nine European banks, including CaixaBank, ING and UniCredit, announced an initiative to launch a euro-denominated stablecoin.

    Central banks and regulators are developing a comprehensive agenda for digital assets. Banks will need to blend traditional fiat currencies and assets with their digital counterparts. This trend is also driving a progressive modernisation approach, as legacy core banking systems weren’t designed to manage digital assets, nor do they support moving money via blockchain-based rails. I expect to see more banks looking to deploy a multi-core strategy where digital assets are managed and stored elsewhere, but they can still provide a seamless and unified experience to customers.

    AI

    Last year, I predicted that the industry would adopt a ‘meet-in-the-middle’ approach to AI, with banks beginning to uncover the real value that the technology can deliver. I also predicted consolidation, recalibration and stabilisation in the market.

    GenAI Banking Applications

    My predictions held true, by and large. In 2025, institutions explored what is possible, relevant and achievable within the banking context, then specifically for each individual institution within its legacy architectures and technological environments.

    This trend will evolve into more practical actions and initiatives over the next 12 months to provide greater clarity around where GenAI shines versus where it’s not applicable.

    To gain clarity, it’s important to understand the difference between AI and GenAI. The latter is built on stochastic principles, which uses probability to model systems that appear to vary in a random manner. This means that the same input could potentially generate different outputs – this isn’t acceptable for automated financial operations, which requires much more determinism. Hence, I believe that GenAI will be used chiefly in scenarios where there’s human intervention.

    One area where GenAI is applicable is in conversational applications. For example, banks will begin launching more interactive user interfaces. Customers will be able to interact with the bank as they would a human. Moving beyond simple, frequently asked questions to actual actions.

    GenAI in the Back Office

    Similarly in the back office, banks can leverage GenAI to provide guidance to their employees and accelerate certain tasks. Using the technology to improve efficiency and help staff do more will have a positive impact on customer experience. Processes will take much less time.

    It will also help to bring unbanked segments or non-standard customers, which are difficult and costly to onboard because they require a bespoke assessment, into regulated financial services. Applying GenAI can make the bespoke process much more efficient by providing data-driven insights to support faster and smarter decision-making. This will make it much cheaper to serve these segments. Including smaller and medium-sized enterprises, which will drive financial inclusion and improve customers’ financial health.

    Learn more at plumery.com

    • Artificial Intelligence in FinTech
    • Blockchain & Crypto
    • Cybersecurity in FinTech
    • Digital Strategy
    • Fintech & Insurtech
    • InsurTech

    Author

    Can Taner

    Published

    13 January 2026

    Estimated Read time

    3Mins

    Can Taner, Chief Product Officer at Bitpace, analyses the most important shifts in the crypto and payments landscape

    The crypto industry has entered a phase of unbundling. Instead of one-size-fits-all platforms that try to do everything, businesses are looking to specialised providers that solve real-world problems with focus and precision. This shift defines how leading firms now build products: client-first, agile, and compliance-ready by design.

    Solving Real Problems with Real Products

    The key to building effective crypto payment solutions is understanding what businesses actually need. Payments should help companies operate faster, more efficiently, and at lower cost. Rather than chasing every trend, the focus should be on creating tools that remove friction and add measurable value.

    That’s why many providers now offer modular solutions designed to work seamlessly across industries:

    • Payment gateway – enabling merchants to accept crypto securely, with instant conversion to fiat if needed, reducing volatility risk.
    • Global settlements – allowing businesses to move funds cross-border quickly and cost-effectively, bypassing traditional bottlenecks.
    • API integration –giving partners the tools to embed crypto payment functions directly into their platforms, delivering a frictionless experience for end-users.
    • OTC services –providing access to large-scale crypto trades, executed with discretion, high liquidity, and competitive pricing.

    Each product is tailored to solve a specific pain point. Instead of bundling everything into a rigid system, we focus on flexible modules that businesses can adopt individually or together.

    Agility and Expertise in Product Development

    For providers, being specialised also means being agile. Every client problem requires a different approach, and in-house expertise allows them to respond quickly without compromising quality. From compliance to sales to product development, teams must collaborate to find creative solutions that meet the highest regulatory and technical standards.

    This agility is only possible if they invest in deep domain knowledge. Product and engineering teams that understand the nuances of payments, crypto, and regulation can adapt quickly to market changes while keeping compliance at the core of every decision.

    How to Launch New Products Effectively

    Launching a new product in crypto, or any fast-evolving sector, demands structure and discipline. The most successful teams follow a process that balances creativity with rigour.

    • Start with ideation. Listen closely to client feedback, analyse emerging trends, and identify where the market still falls short. Great products don’t begin with technology, but with a clear problem to solve.
    • Do the research. Test assumptions early, model potential use cases, and validate compliance requirements before writing a single line of code. A strong evidence base prevents costly pivots later.
    • Plan collaboratively. Bring product, legal, compliance, sales, and technology teams together from the outset. Aligning goals across functions ensures that innovation doesn’t come at the expense of security or scalability.
    • Build with resilience in mind. Security, interoperability, and performance should be built into the product from day one, not retrofitted at the end.
    • Test thoroughly. Create safe environments to simulate real-world conditions and identify weaknesses before launch. Testing isn’t just a single step, but an ongoing cycle.
    • Launch deliberately. Roll out in phases, gather user feedback, and support early adopters closely. A careful launch builds trust and sets the stage for sustainable growth.

    Each of these stages is designed to reduce risk, accelerate learning, and maximise long-term value, principles that define successful product development in today’s crypto landscape.

    How Specialisation Wins

    Launching products in crypto is about precision and collaboration. The great unbundling of crypto is rewarding those who specialise, focusing on solutions that solve real business challenges. Specialised providers win because they put the client first. That focus on expertise and flexibility is what defines success in the new era of crypto payments.

    Learn more at bitpace.com

    • Blockchain & Crypto
    • Digital Payments
    • Fintech & Insurtech

    Author

    Ben Francis

    Published

    9 January 2026

    Estimated Read time

    4Mins

    Ben Francis, Insurance Lead at Risk Ledger, on navigating cyber threats by reinforcing security from the inside out

    Cyber insurance has evolved from a straightforward risk transfer mechanism into an integral component of enterprise risk strategy. As a result, the conversation has shifted beyond simply securing coverage to embracing three foundational elements: transparency in risk exposure, accountability for security measures, and active collaboration throughout the digital ecosystem.

    Rather than asking ‘are you covered?’, the more pertinent question has become ‘can you demonstrate measurable risk reduction?’. Insurers and insureds alike are recognising that what matters now is how well an organisation understands and manages its digital exposure, especially across its extended supply chain. Recent data reveals that 46% of organisations experienced at least two separate supply chain-related cyber incidents in the past year, a clear sign that exposure often lies beyond direct control. 

    From Risk Transfer to Risk Visibility 

    In recent years, the cyber insurance market has matured significantly. Once viewed as a reactive safety net to cushion the financial impact of attacks, it is now becoming a proactive tool for managing and mitigating risk. This shift is partly driven by insurers, who increasingly expect and work with organisations to demonstrate strong security practices and a nuanced understanding of their threat landscape, including risks deep within their digital supply chains; an area where many businesses still fall short.

    At the same time, the industry faces a growing challenge from systemic cyber risk within their portfolios, as many businesses rely on the same cloud providers, payment systems and digital platforms, increasing the chance of a single point of failure. Insurers must gain visibility into how policyholders are connected, not only to suppliers but to each other. Tools and frameworks that map and monitor these interconnections will be essential to avoid underestimating the wider impact of seemingly isolated cyber events.

    Mapping Beyond Third Parties

    It is no secret that cyber attackers often target the weakest link in a supply chain. These are not always direct suppliers, but fourth, fifth or even sixth-tier vendors that have indirect but critical access to systems and data. Unfortunately, many organisations lack visibility beyond their first tier, creating blind spots that attackers can easily exploit. From an insurance perspective, this presents a clear challenge. If an organisation cannot account for who it is connected to, it cannot adequately quantify its risk and neither can its insurer. Mapping these extended connections is more than just a technical exercise; it means actively practiced risk governance and responsibility. Insurers increasingly want to know how their policyholders are identifying and managing indirect dependencies, particularly in sectors like financial services and retail where disruption can ripple across entire markets.

    Collaboration as a Risk Strategy 

    One of the more underappreciated aspects of cyber resilience is the role of peer collaboration. Unlike physical incidents, cyber threats rarely exist in isolation. A single compromised vendor can impact multiple organisations simultaneously, a fact that has been highlighted by high-profile supply chain attacks such as SolarWinds and MOVEit. 

    As a result, businesses need to think beyond their own perimeters and adopt a more collective mindset. This includes building relationships with industry peers, sharing threat intelligence and participating in sector-wide initiatives aimed at improving visibility and preparedness. 

    In highly regulated sectors, such as insurance, this collaboration is increasingly being encouraged by oversight bodies. Frameworks like the Digital Operational Resilience Act (DORA) in the EU and initiatives from the Prudential Regulation Authority (PRA) and the Financial Conduct Authority (FCA) in the UK are pushing for more transparency around third-party risk. In this context, openness is no longer optional; it will be a regulatory expectation. 

    For insurance providers, greater collaboration between policyholders also means better data on emerging threats and more accurate portfolio management. For businesses, it offers a chance to anticipate vulnerabilities that may not yet have hit their own networks but are affecting others in their industry. 

    Proactive Transparency Builds Trust 

    Organisations that take a proactive, transparent approach to cyber risk management are more likely to secure cover and potentially favourable terms, not just in terms of premiums, but also in access to additional services such as forensic support, incident response sources and legal counsel. 

    Demonstrating a mature cyber posture is not about claiming perfection. No organisation is immune to breaches. What insurers are looking for is evidence of a structured approach: the existence of incident response plans, robust governance, effective supply chain risk management, and above all, an honest view of risk. 

    A Shift in Mindset 

    Ultimately, our understanding of cyber insurance must keep evolving. It should not be treated as a simple checkbox exercise, but as a collaborative relationship between insurers and the organisations they support – one built on shared insight, clear communication, and a drive for continuous improvement.

    The organisations best equipped to navigate today’s threats will be those that prioritise transparency. Not only does it lead to stronger protection, but it also builds a culture of accountability that reinforces security from the inside out.

    Learn more at riskledger.com

    • Cybersecurity
    • Cybersecurity in FinTech
    • Digital Strategy
    • Fintech & Insurtech
    • InsurTech

    Author

    Jan Van Hoecke

    Published

    5 January 2026

    Estimated Read time

    5Mins

    Jan Van Hoecke, VP AI Services at iManage and a highly experienced computer scientist with a passion for technology and problem-solving. on navigating the AI landscape for success in 2026

    The AI landscape faces a number of big shifts in 2026. Agentic AI will undergo a reality check as enterprises discover the gap between marketing hype and actual capabilities, while organisations will go through a mindset change from treating AI hallucinations as crises to managing them, acknowledging the inherent limitations of the technology. There will also be a shift in how data will be structured in AI systems, to help the move from just finding facts (“what”) to understanding reasons (“why”).  Middleware application providers will face new challenges, as those vendors controlling both platforms and data will become more influential. Finally, standardised AI chat interfaces will evolve into smarter, dynamically generated, task-specific user experiences that adapt to immediate needs.  

    Agentic AI Reality Check  

    2026 is the year when agentic AI will get a reality check, as the gap between marketing promises made in 2025 and their actual competencies will become starkly visible. As enterprise adopters share the mixed successes of agentic AI, the market will begin to differentiate between true autonomous agents and the clever workflow wrappers.

    Currently, many products promoted as AI agents are, in reality, rigidly programmed systems that simply follow predefined paths. They cannot independently plan or adapt in real-time to accomplish tasks. The current evolution of AI agents closely resembles the development of autonomous vehicles: early self-driving cars could only maintain lane position by relying strictly on preset instructions, and likewise, today’s AI agents are limited to executing narrowly defined tasks within established workflows. True autonomy, where AI agents can dynamically perform and solve complex problems better than humans and without human intervention, remains, for now, an aspirational goal.

    AI Hallucination Goes from Crisis to Management

    In 2026, the AI hallucination crisis will reach a critical juncture as organisations realise they must learn to coexist with the current fundamentally imperfect technology – until a new technology comes into play that can effectively address the issue. The focus will shift from AI hallucination ‘crisis’ to management.

    As the industry deliberates who carries the liability for AI’s mistakes and inaccuracies – the tool makers or the users – enterprises will stop waiting for vendors to solve the problem and take matters into their own hands. They will adopt a variety of pragmatic risk mitigation strategies – from double and triple-checking work, and enforcing human oversight for high-stakes decisions, to taking hallucination insurance policies.

    Major model builders acknowledge that current foundational LLM technology cannot eliminate hallucinations and ambiguity through incremental improvements alone. New technology is needed. Until then, and perhaps with the realisation that a technological breakthrough is years away, users will start driving the hallucination conversation – both by building systematic defenses within how they use AI, and forcing vendors to accept shared responsibility through better documentation and clearer model limitations.  

    The Next Evolution in AI Data Architecture Lies in a Shift from “What” to “Why”

    There will be a fundamental shift in how data is structured for AI systems, driven by the limitations of current approaches in answering complex questions. While Retrieval Augmented Generation (RAG) has proven effective at locating information and answering “what” questions, it struggles with the deeper “why” and “how” inquiries.

    This limitation stems from RAG’s flat-file architecture, which excels at locating information but fails to capture the complex interconnections and relationships that underpin meaningful understanding and knowledge, especially in specialised domains like legal and professional services information.

    The solution lies in AI-driven autonomous structuring of data. These systems will be better placed (than humans) to reveal critical relationships across multiple data points at scale, also highlighting the contextual dependencies essential for answering the “why” and “how” questions effectively.

    Consequently, in 2026, with machines taking the lead, the method of structuring data will undergo a complete transformation, gradually eliminating the human role in creating structure, to reveal the business-critical interconnections across multiple data points.

    Middleware AI Apps Squeeze

    Given the essential link between data and AI, middleware companies that specialise in building custom applications layered on top of data platforms will begin to get pushed to the margins, forced to compete on niche features – while the core value of data and insight is captured by the platform owners. The true leaders will be those organisations that both own and manage their data, while also offering an AI-powered interface that enables users to interact with their data securely and efficiently, fully leveraging the capabilities of modern AI technology.

    Shift to AI-generated, Task-Oriented User Interfaces

    In 2026, the current traditional vendor-designed, standard AI chat-based user interfaces will transition to dynamically AI-generated task-specific user interfaces that adapt to users’ immediate needs. This represents a fundamental shift from standardised software – for example, where everyone uses identical Microsoft Word or SharePoint interfaces – to personalised, short-term user interfaces that exist only as long as the user requires them for a specific task.

    This transformation will also address the critical pain point that users typically have – i.e, the crushing cognitive load of navigating bloated, feature-rich software. Instead of searching through endless menus in an overstuffed application like Excel, the user will simply state their goal – “Compare the Q3 and Q4 sales figures for our top 5 products and show me a chart” – and the AI will instantly generate a temporary, purpose-built interface – a “micro-app” – solely designed for that one single task.

    In the context of dynamically generated user interfaces, both data storage and the creation of bespoke interfaces will be managed by AI. The AI organisations that will truly lead in providing such bespoke user interface-generating capability are those that possess and control their own data.

    About iManage

    iManage is dedicated to Making Knowledge Work™. Our cloud-native platform is at the centre of the knowledge economy, enabling every organisation to work more productively, collaboratively, and securely. Built on more than 20 years of industry experience, iManage helps leading organisations manage documents and emails more efficiently, protect vital information assets, and leverage knowledge to drive better business outcomes. As your strategic business partner, we employ our award-winning AI-enabled technology, an extensive partner ecosystem, and a customer-centric approach to provide support and guidance you can trust to make knowledge work for you. iManage is relied on by more than one million professionals at 4,000 organisations around the world.

    Learn more at imanage.com

    • Artificial Intelligence in FinTech
    • Data & AI
    • Digital Strategy

    Author

    Dan Brightmore

    Published

    26 September 2025

    Estimated Read time

    2Mins

    Join 3,000+ industry decision makers and influencers at Smart Retail Tech Show for your opportunity to gain the tools to stay ahead in a competitive market

    If you’re in retail and looking to stay ahead in a fast-changing market, the Smart Retail Tech Expo is a must-attend event. With thousands of industry professionals, the show is a hub for innovation, showcasing the latest technologies to enhance the customer journey, streamline operations, and drive growth. Whether it’s improving operations, enhancing safety, enabling contactless payments, or elevating the customer experience, it’s all on the show floor.

    Regardless if you’re an independent retailer or part of a global chain, this is your chance to explore cutting-edge solutions!

    Why Attend Smart Retail Tech Expo?

    With only pre-qualified decision-makers and key influencers in attendance, it’s the perfect place to network, learn, and invest in the future of retail.

    Visitors include Key Decision-Makers: CTO | Director of Retail Experience | Digital Transformation Director | Director of Innovation | Head of Customer Experience | Head of Digital & E-commerce

    • 3,200 visitors in attendance
    • 86% have purchasing authority
    • 76% are looking to source new products & services
    • 95% are senior management or above

    Smart Retail Tech Expo is where retail innovation happens! Small business or global, discover cutting-edge solutions and in one place and shape retail’s future.

    “Thanks @smartretailexpo! Packed with innovation, connected with lots of great problem solving startups doing amazing work in the space!”

    Daniel Himsworth, Marks & Spencer

    Keynote speakers include experts from e-commerce, retail, and tech backgrounds, alongside many more. They will be sharing insights from their personal journey and future-proofed strategies on customer engagement, globalising your business, social media commerce, and lots more. Come and hear from the industry’s biggest voices and learn about how to keep ahead in the white and private-label sector. Keynote speakers include expert insights from Pinterest, Tik Tok, Uber Eats, Alibaba and many more…

    Register now for free tickets and gain insider knowledge… Beyond networking, Smart Retail Tech Expo offers expert-led sessions and insights into emerging trends, sourcing strategies, and retail technology—giving you the tools to stay ahead in a competitive market.

    • Event Newsroom
    • Events

    Author

    Dan Brightmore

    Published

    Estimated Read time

    2Mins

    Join over 25,000 entrepreneurs, SME owners, and senior professionals at Excel London for The Business Show London 2025

    The world’s largest award-winning business event, The Business Show London 2025, is returning to Excel London on the 12th and 13th of November 2025. Join over 25,000 SMEs and startups at this premier London business expo, designed to provide the support and resources you need to start, grow, or scale your business.

    As always, the event offers free expert advice and insights from some of the biggest names in the industry. Building on last year’s impactful keynotes, this year’s business conference features fresh faces—business leaders who have thrived in recent years. In today’s digital landscape, this is a rare opportunity to gain face-to-face experience, advice, and inspiration from those who have been in your position and succeeded.

    Whether you’re looking to network at one of the best business networking events in London or seeking new business partnerships, this event is your gateway to unlocking growth. For enquiries, registration, or to book a stand, contact the team today and secure your place at the UK’s leading SME business event.

    Why Attend The Business Show London?

    This flagship London business expo offers unparalleled opportunities to connect with industry leaders, discover cutting-edge solutions, and gain practical insights to accelerate your business.

    “Vibrant, electric and inclusive ….the atmosphere I felt today at The Business Show, London excel as a keynote speaker representing Google. Such an incredible turn out, engaged listeners and wonderful to also have 121’s with many entrepreneurs on business growth utilising AI!”

    Harmony Murphy, Google

    With thousands of exhibitors, inspiring keynote speakers, and interactive show features, the show caters to startups, established businesses, and everyone in between. Whether you’re looking to connect with startups, explore small business exhibitions, or attend the UK’s leading business growth conference, this event will equip you with fresh ideas and practical strategies to help your business succeed.

    • 500+ exhibitors
    • 86% attendee satisfaction rate
    • 75% attendees plan to return
    • 6 show features

    Don’t miss your chance to participate in one of the top business networking events in London.

    Register now for free tickets and join the UK’s most ambitious business minds to gain new partnerships, expert advice, and business development opportunities.

    • Event Newsroom
    • Events

    Author

    Andy Swift

    Published

    8 September 2025

    Estimated Read time

    4Mins

    Andy Swift, Cyber Security Assurance Technical Director at Six Degrees on

    According to AV-TEST, the independent IT security institute, every day sees at least 450,000 new malware variants added to its database. In June this year, for example, cybercriminals are thought to have used malware to steal over 16 billion login credentials across various major platforms in what is thought to have been the largest breach of its kind in history. For security teams, this represents a relentless challenge that demands constant attention and consumes significant resources.

    Malware-Free Attacks

    As if that wasn’t enough, malware-free attacks are increasingly favoured by cybercriminals as a way to circumvent organisational security. Typically using legitimate programs and tools, these stealth attacks are particularly complex to detect. And they are invisible to most automated security protection options that are available to buy.

    With no obvious malware signatures to detect, automated defences are often powerless to respond. And without robust security foundations, even advanced detection tools offer limited protection once an attacker gains a foothold. When that happens, the consequences can be significant.

    At the heart of the matter are the limitations of many traditional security tools, which are simply not designed to stop what they cannot see. Malware-free attacks do not rely on external payloads or binaries with known malicious signatures. This renders many automated detection systems, including standard antivirus solutions, effectively useless. As a result, the burden falls elsewhere.

    For most organisations, that means having the right expertise in place to recognise unusual behaviour, supported by technologies that can identify behavioural anomalies quickly. Endpoint detection and response (EDR) platforms offer some of these capabilities. But even the most advanced solutions rely on proper configuration and human oversight to be effective. In an ideal world, every business would have round-the-clock monitoring in place, but in reality, very few do.

    Challenging Assumptions Around Risk

    So, how can organisations fill the gap? When assessing how to protect against malware-free attacks, many organisations begin with the assumption that they will need to buy new tools or licenses. This can form part of a rounded solution. However, leading with this mindset often overlooks a more fundamental and cost-effective question: What can be improved with the tools already in place?

    Reviewing existing capabilities should be the first step. For example, most environments already have some level of EDR, behavioural monitoring or identity protection deployed. Yet these are often underutilised or misconfigured. This can result from a lack of understanding around tool capabilities (and limitations), paying for the wrong level of license coverage, and failing to ensure configurations support behavioural analysis rather than just malware scanning. In many cases, even minor adjustments can significantly increase effectiveness without any additional spend.

    Cost vs Risk

    Organisations should also reconsider how they approach the question of investment. The cost vs risk conversation needs to shift from what they should buy to what they should fix. Even the most expensive detection tools can be rendered ineffective if attackers can exploit basic oversights such as poor configuration, excessive access rights or the absence of multi-factor authentication. In contrast, identifying and addressing these gaps in existing systems is not only more cost-effective but also more impactful in stopping attacks before they gain momentum.

    This kind of review process is also an opportunity to identify gaps and prioritise actions that reduce risk without escalating costs. For example, many organisations find that network segmentation, strict privilege controls and enforcing least-access policies can help prevent lateral movement and minimise credential misuse – two of the most common techniques used in malware-free attacks. Putting these capabilities in place are security fundamentals that often determine whether an attack is stopped early or is able to spread.

    In this context, a best practice approach matters more than ever. Not as a one-off initiative, but as a continuous effort to close the windows of opportunity that attackers rely on. This includes reducing privilege levels, adopting MFA by default, limiting binary access and educating users on social engineering techniques. All of which are good examples of cost-effective steps that can limit the opportunity for malware-free attacks to take hold. These are not headline-grabbing technologies, but they remain the strongest defence against attacks that thrive on poor hygiene and overlooked gaps.

    So, rather than investing in yet another layer of detection, organisations should focus on strengthening what they already have. This approach not only helps avoid unnecessary expense but also delivers a stronger, more sustainable defence posture in an environment where threat actors continue to be extremely effective.

    • Cybersecurity
    • Cybersecurity in FinTech
    • Infrastructure & Cloud

    Author

    Dan Brightmore

    Published

    4 September 2025

    Estimated Read time

    5Mins

    The Financial Transformation Summit (FTS), presented by MoneyNext, took place June 18-19 2025 at London’s ExCeL Centre, Royal Victoria Dock. With over 2,000 attendees, 300+ speakers, and 400 roundtables, it stood out as one of the most immersive and interactive events in the financial services calendar.

    FinTech Strategy hit the conference floor at the heart of the action delivering insights from experts across Banking, Insurance, Wealth, and Lending at Financial Transformation Summit (FTS).

    Financial Transformation Summit attendees from banking, insurance, wealth, lending, fintech, consultancy, and regulatory sectors convened for two days packed with keynotes, panel talks, immersive demos, and networking among 60+ exhibitors and startups.

    Co-located streams – Banking, Insurance, Wealth, and Lending part of themed zones – meant that ticket-holders could explore adjacent sectors fluidly across a guiding theme: culture, collaboration, and customer centricity driving tech adoption and transformation.

    Programme Highlights

    Keynotes & Panels

    1. Data Silos & Cross‑Institutional Collaboration

    A panel featuring senior leaders from EVLO, Aon, Schroders, and Brit Insurance tackled how institutions – despite collectively spending over $33 billion annually on data – still struggle to collaborate due to privacy concerns and regulation. Innovative solutions included federated learning, anonymised client IDs and consent-backed APIs.

    2. Digital Insurance via Wallets

    Anna Bojic (Miss Moneypenny Technologies) unveiled a fresh take on insurance – embedding policy and claim data into Apple/Google Wallets. The idea: dynamic customer interaction directly from smartphone wallets, enhancing real‑time engagement and retention.

    3. ESG Economics & Market Reality

    Marc Kahn (Investec) challenged ESG orthodoxy, urging firms to emphasise human and planetary wellbeing – beyond purely financial returns – to capture stakeholder trust and sustainable growth.

    4. People & Psychological Safety

    Kirsty Watson (Aberdeen Group) and Vikki Allgood (Fidelity International) underlined that technological investments are futile without organisational design and psychological safety. Allgood cited a McKinsey study revealing only 26% of leaders build teams with a sense of safety – a critical step toward innovation.

    5. Human‑Centred AI

    Monica Kalia (Planda AI) championed AI that models individual financial contexts – recognising diversity within demographic cohorts and personalizing services accordingly.

    Roundtable Experiences at FTS

    At the event’s heart were the TableTalk roundtables – 400+ small-group sessions, each led by a subject-matter expert. These were limited to six participants each, enabling deep, peer-led discussions on themes like:

    • AI in risk and compliance
    • Open banking integration
    • ESG data standards
    • Cyber resilience
    • Change management and culture adaptation

    Attendees consistently praised their interactive nature – far removed from the stage‑focused “listening” format often critiqued at other conferences.

    Demonstrations & Exhibitor Showcase

    Over 60 exhibitors presented tech-driven innovations: Generative AI, open‑banking APIs, ESG reporting tools, embedded finance solutions, and more. A few standouts were:

    • CRIF highlighted AI-powered credit scoring with ESG overlays – promising dynamic risk assessments backed by sustainability data
    • Emerging FinTechs demoing AI compliance engines, digital wallet insurance packaging, and data-sharing platforms
    • Hyland demonstrated the intuitive end-user experience of its Hyland Content Innovation Cloud™ and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business

    The demo zone allowed engaging, hands-on exploration and real-time Q&As; it complemented the content with practical insights.

    Standout Themes & Strategic Insights

    1. Tech is Not Enough Without Culture

    Recurrent messaging emphasised that culture, trust, governance, and psychological safety are foundational – not secondary – to digital initiatives. Technology alone won’t deliver transformation without a people-first mindset.

    2. Cross‑Sector Data Collaboration

    Despite heavy investment, institutions still operate in silos. Shared, secure infrastructure and regulatory-aligned frameworks are being prototyped, but broad adoption remains a work in progress.

    3. AI-as-a-Personalisation Backbone

    AI is shifting from automation to empathy. Organisations showcased tools to hyper-personalise offers yet maintain privacy and inclusion – moving beyond outdated demographic frameworks into genuine behavioural understanding.

    4. Embedded Finance & Digital Wallets

    Insurance via wallet applications and embedded finance models point to seamless customer journeys – less app hopping, more value delivered at the point of need.

    5. Rebalancing ESG & Profit Metrics

    Speakers emphasised integrating ESG factors into performance metrics – not just for compliance, but as an operative advantage anchored in long-term stability and stakeholder trust.

    Who Should Attend FTS Next Year?

    Ideal for:

    • Transformation and change leaders
    • CTOs, CIOs, and Heads of Innovation
    • Data and AI strategists
    • Operational and HR leaders focused on culture
    • FinTech innovators and solution providers

    If you’re crafting digital transformation strategies, an attuned leader in financial services, or a consultant embedding tech in legacy environments, this summit provides rich, actionable content.

    Expect next year’s event to build on this foundation:

    • More AI-specific tracks, possibly Generative AI streams
    • ESG deep-dives with case studies on implementation
    • Expanded regulator involvement around data governance and cross-border compliance

    FTS: Final Verdict

    Overall, the FTS 2025 delivered on its brand promise:

    • Interactive and inclusive: 400 roundtables empowered voices across levels.
    • Cross‑sector learning: Banking, Insurance, Wealth, and Lending streams offered both breadth and depth.
    • Insightful keynotes: Big ideas on AI, ESG, data-sharing, and culture were well-explored.
    • Real-world relevance: Exhibitor demos connected theory with practice.
    • Networking with purpose: Opportunities to engage, learn, and collaborate were abundant.

    The Financial Transformation Summit struck a compelling balance between big-picture vision and granular, execution-level insight. It emphasised that while technology enables; culture, customer centricity and collaboration drive real progress. The format – with its roundtables, demos, and keynotes – offered a dynamic platform for knowledge exchange.

    If you attended, chances are you left with practical next steps. If you didn’t, you missed one of the most interactive, future-focused events shaping financial services transformation today.

    • Artificial Intelligence in FinTech
    • Digital Payments
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    • Host Perspectives
    • InsurTech

    Author

    Dan Brightmore

    Published

    12 August 2025

    Estimated Read time

    8Mins

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    FinTech Strategy meets Eastern Horizon Founder & CEO Christine Le to discuss client expectations and the changing landscape of wealth management

    Financial Transformation Summit 2025 EXCLUSIVE

    At Financial Transformation Summit, Christine Le, a Chartered Financial Planner and Founder & CEO of Eastern Horizon Wealth Management, spoke on an investment panel – “Generational Wealth Transfer: Meeting the Expectation of Younger Clients”. Appearing with industry colleagued representing Citi Global Wealth, HFMC Wealth and Lightbox Wealth, Le considered: What trends and technologies are shaping NextGen investment decisions, and how can WMs stay ahead? Can digital wealth platforms meet the demand for hyper-personalised, user-friendly experiences? How does social responsibility & ESG investing influence younger investors, and how can advisors align with these priorities? How can wealth managers build and maintain trust with NextGen investors?

    Following the panel, we spoke with Christine to find out more…

    Hi Christine, tell us about your role at Eastern Horizon?

    “I’m a Chartered Financial Planner and the Founder & CEO of Eastern Horizon Wealth Management. We are a financial advisory firm and also a partner practice of St. James’s Place. They are among the biggest wealth management firms in the UK based on assets under management. We get a lot of support from St. James’s Place in terms of technology compliance and investment solutions. At my practice, we focus on a diverse range of clients including ethnic minorities, especially British Asians in the UK. I’m also the president of the Vietnam Investment and Finance Association in the United Kingdom (VIFA). We aim to provide useful financial information for Vietnamese people in the UK and become a bridge between Vietnam and the UK.”

    You were part of a panel at this Summit focused on Generational Wealth Transfer. Can you give us an overview of your thoughts?

    ‘’Having worked in the financial services industry for over 15 years, I’ve observed a persistent gap in how the industry serves diverse client segments – particularly ethnic minority communities in the UK. This gap is especially pronounced when it comes to financial education and long-term planning, including wealth transfer across generations. When I speak to members of my own Vietnamese community, I often find that there’s a limited understanding of how to navigate financial systems effectively – from managing investments and pensions to planning for intergenerational wealth. It’s not due to a lack of interest or ambition, but rather a lack of access to culturally relevant and accessible financial advice.

    “This is where I believe I can make a meaningful difference. I not only bring professional expertise and technical knowledge to the table, but also a deep understanding of the cultural values, family dynamics, and communication styles that shape financial decision-making in the community. That cultural insight is key to building trust, something that is essential when discussing personal finances and planning for the future. My goal is to help bridge that gap – to empower families with the knowledge and tools they need to make informed financial decisions, preserve their wealth, and pass it on confidently to the next generation.’’

    Why is this an exciting time for the business?

    “At the moment the world is so integrated, and many people can benefit. A lot of people want to go to the UK, invest into the UK. I think with that in mind this is an exciting time to run my business and to be able to bridge that gap, providing sufficient knowledge for people as a trusted source when they come to the UK and need to understand the financial regulations. We can give people solid support to understand the financial processes of settling and building wealth in the UK.”

    “Right now, everyone is talking about AI, and for good reason. In my business, we rely heavily on digital tools to streamline administrative tasks. It’s truly a game-changer. Compared to starting a business 15 years ago, when I would have needed a full-time assistant just to take meeting notes and summarise action points, many of those processes can now be automated, saving both time and cost. Another advantage is in how we communicate. Many of my clients are British Vietnamese. While they understand and speak English, they often feel more comfortable communicating in Vietnamese. We use AI-powered translation tools to make this process faster and more seamless. These technologies are allowing us to broaden the range of services we offer and tailor our support to each client’s needs.”

    What pain points are your clients experiencing that you need to address?  How are you meeting the challenge?

    “It’s about meeting the client’s highest priority. When people come to me, they maybe want to support their children to get onto the property ladder or plan for their retirement. They might be looking to buy a new car or move home. So, as a regulated financial advisor, I can sit with a client and talk them through key priorities and tailor the solutions best for them and help them overcome the pain points of decision-making.

    “Additionally, the UK’s financial regulations are complex and changing all the time. It’s very difficult for people to follow. It’s my job as a financial advisor to follow up those changes and stay up to date with the regulations to assess how it can impact our clients and then give them the best recommendations. Allied to this, many of our clients will need support with cross-border services as they move freely between different countries they need somebody they can trust, an expert that knows what they’re doing and who can provide the right financial services for them.”

    Tell us about a recent success story…

    “Success for Eastern Horizon is to know that our clients feel they have somebody to rely on. For example, I have an old friend who came to me as a client. She was based in Vietnam but wanted to relocate to the UK. She had assets across Europe and in Vietnam and needed to understand the big picture of financial planning in the UK. We examined her assets across different countries to bring them into the UK and find the best solution for her to utilise tax efficient savings, pensions and investments to support her family and her business in the long term.”

    What’s next for Eastern Horizon when it comes to wealth management? What future launches and initiatives are you particularly excited about?

    “Over the next few months, we are keen to collaborate with different associations and communities across the UK – whether that’s related to Vietnam or British Asian communities and offer useful information and workshops and webinars tailored to different audiences. Also, with my work for the Vietnam Investment and Finance Association I want to organise workshops for those keen to invest in the UK but don’t know where to start. They often don’t have anyone to support them so I would like to focus on building a network to offer that bridge to investment in the UK.”

    Why do you think the evolution of collaboration between traditional institutions and FinTechs is set to continue? What are you excited about?

    “I spent five years working at the intersection of FinTech and WealthTech – where wealth management meets technology. During that time, I witnessed firsthand how the financial services landscape is evolving. Large incumbent banks bring undeniable strengths: scale, regulatory rigour, and long-standing client trust. However, they often struggle with agility. Their legacy infrastructures, many of which still aren’t cloud-based, make digital transformation slow and complex. On the other hand, FinTechs are born digital. They’re nimble, innovative, and quick to adapt to changing customer needs. But without the reputation and stability that traditional institutions have built over decades, they can face challenges in gaining consumer trust or navigating regulatory environments alone. What became clear to me is that banks and FinTechs cannot operate in silos.

    “Collaboration is not just beneficial, it’s essential. When they work together, they combine the best of both worlds: the reliability and compliance of traditional finance with the innovation and customer-centric design of new technology. With my own practice, we apply this mindset. We actively look for ways to streamline administrative processes using digital tools – reducing costs, improving efficiency, and freeing up more time to focus on what matters most: building strong, human relationships with our clients. The goal is to use technology not to replace that human connection, but to enhance it. By doing so, we can deliver modern, efficient, and deeply personalised financial services that clients trust.”

    Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Eastern Horizon?

    “I’ve attended several events this year, and this has truly been one of the most enjoyable and well-organised in the UK. What stood out was the impressive mix of voices – from established financial institutions to bold, forward-thinking startups. Engaging with such a diverse group of speakers has been both insightful and thought-provoking. I’ve come away with fresh perspectives, challenged some of my own assumptions, and found new ideas to explore as we continue building meaningful partnerships for Eastern Horizon Wealth Management.”

    Find out more at easternhorizonwealth.co.uk

    About Christine Le and Eastern Horizon Wealth Management

    As an Appointed Representative of St. James’s Place, Practice Lead, and business owner, Christine leverages over 15 years of experience in financial services and wealth tech to serve our clients, acquired through extensive work in multinational financial services firms in the UK. This rich background has equipped Christine with the skills and knowledge necessary to effectively oversee the business, ensuring that every facet is managed with the highest level of professionalism.

    Christine founded and built this Practice to help clients prosper, build financial security, and attain peace of mind while overcoming financial obstacles. 

    Her primary focus is on nurturing enduring relationships with her clients, offering them trusted guidance as their financial requirements evolve over time. Throughout her advisory process, clarity remains paramount. By closely collaborating with her clients, Christine strives to identify the most efficient and tax-effective strategies to help them achieve their objectives. Specialising in tailored solutions, Christine is dedicated to understanding her clients’ financial goals and crafting strategies that align with their vision for the future.

    • Artificial Intelligence in FinTech
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    Author

    Dan Brightmore

    Published

    11 August 2025

    Estimated Read time

    8Mins

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    FinTech Strategy meets with Citigroup’s Head of ESG Credit Management, Mauricio Masondo, to discover the future for ESG and sustainable finance

    Financial Transformation Summit 2025 EXCLUSIVE

    At Financial Transformation Summit, Mauricio Masondo, Head of ESG Credit Management at Citigroup, featured on a sustainability panel – ‘The Future of ESG and Sustainable Finance: Balancing Profit and Purpose’. Alongside peers fromGenerali AM, Gallagher Re and Arma Karma, Masondo considered: What key metrics should FIs use to track ESG progress, and how can they ensure authenticity in their sustainability efforts? Developing a holistic ESG strategy amid evolving regulations – key challenges and solutions. How can FIs leverage technology to meet sustainability goals and drive long-term profitability? How can FIs move beyond offering ESG products to embedding sustainability into their core business models?

    Following the panel, we spoke with Mauricio to find out more…

    Hi Mauricio, tell us about your role at Citigroup?

    “In my 32 years with Citi my career has primarily focused on wholesale credit, and in recent years I built out our portfolio management function. For the past year specifically, I’ve been leading the integration of ESG and climate considerations into our credit processes. As Head of ESG Credit Management, my role is to embed ESG requirements into our credit processes in a way that’s consistently and efficiently applied through technology, policies, training, and governance frameworks. Our strategic approach was not to create an ESG silo that replicates existing processes, but rather to integrate ESG considerations seamlessly into our current workflows. This means any credit analyst can now underwrite ESG credits, sustainable loans, or green loans, rather than requiring dedicated specialists. We’ve equipped our entire team with the knowledge and tools they need to handle these transactions effectively.”

    You were part of a panel at this Summit focused on the future for ESG and sustainable finance. Can you give us an overview of your thoughts?

    “Data standardisation is absolutely critical, especially as we advance into the AI era. I often reference Moody’s as an excellent example of strategic foresight. Moody’s operates two key businesses – credit ratings and data analytics – and early in their AI journey, they made the strategic decision to structure and normalise all their credit research data. This proved to be transformational because it enabled them to deploy AI solutions much more rapidly with clean, structured datasets. We’re working to apply this same principle at Citi. We’re developing processes to structure climate-related data in a way that will be usable across multiple applications. For example, we’re working on integrating emissions data and climate risk assessments into our credit risk rating models. We’re also exploring how this structured approach could support underwriting processes and securitisations, where comprehensive data packages could facilitate risk transfer transactions with institutional investors. The goal is to build normalised, structured data as the foundation for various applications, from portfolio management to AI-driven solutions. While we’re still in the early stages of many of these initiatives, the potential is significant.”

    Why is this an exciting time for the business?

    “We’re witnessing the convergence of several transformative trends. However, one of our biggest challenges is policy divergence across jurisdictions. Countries are taking vastly different approaches to ESG requirements, and for a global bank like Citi, this creates significant complexity in standardising processes across multiple regulatory environments. While challenging, this divergence also creates opportunities to develop scalable, cost-effective solutions that can adapt to various regulatory frameworks. Second, AI is revolutionising how we approach ESG challenges. It’s helping us structure data more effectively, enhance reporting capabilities, contextualise information, and identify trends that would have been impossible to detect manually.

    “Previously, comprehensive ESG analysis required significant time, resources, and personnel. AI has made these processes more accessible and cost-effective. Most importantly, there’s been a fundamental shift in how the industry, and governments, view ESG. It’s evolved beyond compliance and emissions reporting to become a significant business opportunity. We need to capitalise on this transition – moving from reactive reporting to proactive opportunity capture. The capital is there, and if traditional banks don’t seize these opportunities, asset managers, private credit firms, and private equity will. We’re partnering strategically with reinsurance companies and asset managers to develop innovative solutions that unlock transition capital and help companies fund decarbonisation projects.”

    “Trade flows are experiencing significant disruption due to current tariff policies. This creates both challenges and opportunities for our clients. Companies are reassessing their supply chain vulnerabilities and seeking greater resilience in their operations. I anticipate we’ll see a regionalisation of trade flows rather than a complete deglobalisation. European companies will likely increase intra-regional trade while reducing intercontinental transactions. We’re seeing similar patterns emerging in Asia and the Middle East. This shift requires banks to be more agile in how we structure trade finance and working capital solutions to meet these evolving needs.”

    What pain points are you experiencing that you need to address?  How are you meeting the challenge?

    “Working capital finance requires increasingly creative solutions that leverage advanced technology. Banks are recognising that FinTechs often have greater agility in developing and implementing these technologies. There’s significant efficiency in having one FinTech serve multiple banks rather than each institution developing independent solutions. This collaborative approach allows us to move faster while reducing development costs and time-to-market.”

    Tell us about a recent success story…

    “I designed and led the implementation of an early warning monitoring system for Citi’s credit portfolio. The project began with a fundamental concept: create a data lake, develop meaningful metrics, and engage data scientists to interpret the insights. We collaborated with trade officers and partnered with external specialists to enhance our capabilities.Initially, there was scepticism about the system’s value, particularly because we built it as an independent function within our portfolio management organisation, separate from traditional banking and risk management structures. However, this positioning allowed us to collect unique client data and develop insights that weren’t available elsewhere in the organisation. A critical component of our success was establishing a dedicated credit expert team that oversees the entire process.

    “This team leads the engagement and communication of alerts, ensuring that insights are properly interpreted and actionable recommendations reach the right stakeholders. The evolution was remarkable. We progressed from generating a few alerts daily to dozens per day, and eventually to hundreds of alerts weekly. More importantly, we developed sophisticated processes for interpreting and acting on these alerts, with our expert team serving as the bridge between data insights and business action. Bankers and risk managers began to recognise the value, and today, three years later, the system is integral to how we conduct annual reviews and client presentations. It’s incredibly rewarding to provide our bankers with comprehensive data and insights that strengthen their client relationships.”

    What’s next for Citigroup when it comes to ESG? What future launches and initiatives are you particularly excited about?

    “While it may sound clichéd, AI truly is transformative for our industry. The breadth of use cases and the rapid pace of learning make it essential to our strategic direction. We’ve established a strategic partnership with Google and are investing significantly in AI use case development and implementation across our operations. From an operational perspective, AI will undoubtedly increase our efficiency as an industry. More importantly, it’s enabling us to evolve our business models and create client solutions that weren’t previously feasible. This opens entirely new avenues for innovative product development. Additionally, since CEO Jane Fraser joined, we’ve embarked on a comprehensive transformation program that’s delivering strong results in terms of financial performance and returns. We’ve restructured and simplified our operations, which positions us more competitively as we refresh our leadership teams and attract new talent. The trajectory is very promising.”

    Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

    “The current tariff environment is creating opportunities for FinTechs that facilitate connections between banks, investors, and corporations. It’s also presenting consolidation opportunities for private equity firms within the rapidly expanding FinTech ecosystem.”

    Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Citigroup?

    “The panel brought together diverse perspectives from FinTech, asset management, insurance, and banking – all addressing common challenges that span our sectors. This cross-industry dialogue creates tremendous opportunities for collaboration and mutual understanding. The key now is translating these conversations into action. We need to maintain these connections, expand the dialogue, and avoid making decisions in isolation. FinTechs possess the agility to implement changes in their operating models far more quickly than large incumbents like us. However, our procurement systems and processes aren’t always conducive to collaborating with smaller, innovative companies. Events like this highlight the need to streamline how institutions like Citi can collaborate with and learn from FinTechs. We must accelerate our ability to adapt to a rapidly changing world.”

    Learn more at citigroup.com/global/our-impact

    About Citgroup

    A human bank…

    We’re helping build more sustainable, economically vibrant communities around the world.

    At Citi, helping our clients navigate the challenges and embrace the opportunities of our rapidly changing world is fundamental to our mission of enabling growth and economic progress.

    • Artificial Intelligence in FinTech
    • Events
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    Author

    Dan Brightmore

    Published

    8 August 2025

    Estimated Read time

    7Mins

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    FinTech Strategy spoke with Veritran’s CMO, Jorge Sanchez Barcelo, at Money20/20 Europe to find out more about the tech firm’s partnership with Manchester City reimagining CX to create a frictionless digital experience for fans

    Money20/20 Europe Exclusive

    In an era where technology defines the customer journey, Jorge Sanchez Barcelo, Chief Marketing Officer at Veritran, is leading a bold charge into a new frontier: one where financial technology fuses with fandom, and CX becomes both frictionless and deeply personal.

    Jorge’s professional journey has always followed the arc of digital transformation. From his earlier roles at AT&T and Banorte to now helming marketing at Veritran, a global technology company, his mission is clear: make life easier, better, and more secure for end users – whether they’re banking customers or football fans.

    “Our technology without a purpose is nothing. It’s just code,” Jorge says. “We build for people. And that purpose has taken us far beyond banking.”

    From Buenos Aires to Global Ambitions

    Founded in Buenos Aires almost 20 years ago, Veritran started building mobile applications before the iPhone even existed – when, as Jorge jokes, “phones were just for calls, texts, and the occasional game of Snake”.

    “Our guys were visionaries,” he continues. “They were talking about applications when we didn’t even have smartphones. Back then, you had to build a separate app for every phone model because we didn’t have iOS or Android,” he recalls.

    Despite those early technical hurdles, the company maintained a singular focus: democratising access to financial services. “Once a person starts managing their own finances, they gain control,” reasons Jorge. “And control is the first step toward growth.”

    That mission has proven timeless, and borderless. Today, Veritran has a solid footprint across Latin America and has expanded into the US and Europe.

    Why Experience Matters More Than Ever

    Jorge is acutely aware that in financial services, trust is everything. A slick PowerPoint is not enough to win over banks.

    “When I meet with a financial institution, they don’t want theory. They want proof. They want to see our tech working in the real world. But many banks are reluctant to share their strategies, even with non-competitors.”

    This desire to demonstrate capability led Veritran to seek a bold new marketing approach – one that would provide a visible, secure, and non-competitive environment to showcase its tech.

    Enter Manchester City: A Blueprint for CX Innovation

    The solution arrived via the pitch, not the boardroom. Veritran entered into a partnership with Manchester City, one of the best football teams in the world.

    “Manchester City is digitally five to seven years ahead of most clubs,” says Jorge.

    Veritran’s technology now supports key digital operations at Manchester City, helping the Club streamline processes such as user registration, membership management, and ticketing. This collaboration reflects a shared commitment to innovation and operational excellence.

    What began as a strategic partnership has evolved into a strong example of how financial technology can reinforce digital infrastructure in the sports sector. As more organisations seek reliable and scalable solutions, the model developed with Manchester City demonstrates the value of secure, efficient platforms designed to support long-term digital growth.

    Breaking the Sponsorship Mold

    Unlike traditional sports sponsorships, which often come with hefty price tags and limited strategic collaboration, Veritran’s deal with City was rooted in partnership.

    “Our partnership is beneficial for both companies, we share value,” explains Jorge.  “With the brand reach of Manchester City’s clubs we have been able to promote our company worldwide.”

    This model has opened the door to future collaborations, not only with sports clubs, but also with entertainment companies in the US who are eyeing similar digital transformations.

    Applying FinTech Learnings in New Territories

    As Veritran enters new markets, they carry the lessons of regulated finance into less restricted sectors.

    “In banking, every innovation has to pass through layers of regulation,” notes Jorge. “But in entertainment or sports, you can think outside the box and start with the experience, not the compliance checklist.”

    That freedom has allowed Veritran to experiment with new ideas, such as smile-based stadium access or face-based payments.

    “We call it ‘mouthful access’ – just smile, and you’re in. You can’t do that in banking… yet.”

    Blending Brand and Utility: A New Era for Embedded Finance

    What sets Veritran apart isn’t just its technology stack – it’s the way it applies that stack to create emotional resonance and operational value in new settings. For Jorge and his team, the convergence of financial services and lifestyle touchpoints is the most exciting, and underexplored, frontier.

    “When we embed finance into a stadium or a music festival, we’re not just processing payments,” he explains. “We’re creating seamless, branded experiences that extend customer relationships beyond the bank branch or app.”

    This philosophy echoes a wider FinTech trend: the shift from siloed services to contextual, embedded finance – delivered where customers already are, not where institutions want them to be.

    As financial brands seek new ways to engage digitally-native consumers, Jorge believes partnerships with lifestyle, sports, and entertainment brands offer huge untapped potential.

    Jorge notes that younger generations expect everything to be digital, instant, and intuitive. They don’t separate banking from shopping or attending an event, it’s all part of one journey. “If we can integrate services invisibly into those moments, that’s where the magic happens.”

    He’s quick to add that the financial industry still has work to do in aligning with this shift – both culturally and technologically.

    “It’s not just about APIs or infrastructure. It’s about mindset. The organisations that embrace this new way of thinking – who see CX as a shared responsibility across ecosystems – will lead the next decade.”

    With Veritran’s cross-industry collaborations accelerating, Jorge is confident they’re not just shaping financial journeys – they’re reshaping everyday experiences.

    Embedding Finance in the Fan Journey

    Jorge sees a massive opportunity to embed financial services into sports and entertainment ecosystems, particularly in underbanked regions like Latin America.

    “In the UK, stadiums are already cashless. In Latin America, we still have guys walking around selling Coca-Cola for cash from their pockets. We want to change that.”

    By introducing digital wallets, biometric payments, and embedded insurance services (e.g., ticket protection at the point of sale), Veritran enables clubs to become financial service providers.

    “Imagine buying a match ticket and adding travel insurance in one click. That’s the level of seamless we’re aiming for.”

    Pain Points Driving Demand

    So what are clients asking for?

    Jorge says it comes down to three priorities:

    1. Integrated Payments Ecosystems
      Clients want unified platforms that support seamless payments across channels and partners
    2. Digital Onboarding & Identity
      Reducing friction while enhancing security is top of mind – especially in customer acquisition
    3. End-to-End Security Suites
      With AI-driven fraud and evolving regulations, security isn’t optional; it’s a strategic asset

    Veritran’s flexibility as a tech partner, not just a vendor, allows it to co-create with clients. This often means integrating with their existing partners, such as banks, card networks, or insurers.

    What’s Next for Veritran?

    According to Jorge, the company is at a pivotal moment. Its technology is gaining traction in new verticals with strong investment appetite – such as entertainment and live events.

    “These sectors have the budget and the ambition. No one’s serving them with the kind of Fintech-grade CX we provide.”

    The company is also exploring opportunities in public transportation and other infrastructure-heavy sectors where transactions are frequent and still inefficient.

    “Everywhere there’s a transaction, there’s an opportunity to simplify.”

    FinTech is set to play an expanding role in everyday life whereJorge believes the very definition of FinTech is evolving.

    “It’s not just about banks anymore. If you buy a coffee, book a train, or enter a concert – those are all transactions. And if we can simplify them, that’s FinTech too.”

    That’s why Veritran sees future growth in collaborative ecosystems where banks, brands, and non-traditional players converge to serve the customer journey holistically.

    Why Money20/20?

    Jorge credits the annual Money20/20 Europe conference with helping shape Veritran’s partnerships – including the initial connection with Manchester City.

    “It’s one of our top five global trade shows. We don’t just send a team – we send our top execs, including our CEO. It’s where deals happen.”

    Building with Purpose for the Future

    In an industry flooded with features and hype Veritran differentiates by staying grounded in user value.

    “Tech for tech’s sake is meaningless. But tech that improves how someone lives, spends, or connects – that’s everything,” says Jorge.

    From its Argentine roots to a global stage, Veritran’s journey underscores one enduring truth: In customer experience, the future belongs to those who build it with purpose.

    Veritran: A CX FinTech Trailblazer

    • Embedded Finance
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    Author

    Dan Brightmore

    Published

    7 August 2025

    Estimated Read time

    7Mins

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    FinTech Strategy meets with Seema Desai, COO at iwoca, to hear how customer experience is being redefined in a digital lending era

    Financial Transformation Summit 2025 EXCLUSIVE

    At the Financial Transformation Summit, Seema Desai, COO at iwoca, spoke on a panel (alongside representatives from Zopa Bank and Citibank) about the shifting needs for customer experience in digital lending. How can lenders create hyper-personalised loan products to meet diverse customer needs? What are the best practices for maintaining a human touch in automated lending processes? How can lenders build and maintain customer loyalty in a competitive market? What role does omnichannel strategy play in delivering a seamless lending experience?

    Following the panel, we spoke with Seema to find out more…

    Hi Seema, tell us about your role at iwoca?

    “I am the Chief Operating Officer at iwoca. We provide fast and flexible finance to small businesses across the UK and Germany. In my role as COO, I’m responsible for all of our UK operations teams. So, all of our agents that engage with customers throughout the customer journey. And I make sure that we’re offering a really high quality service that is also highly efficient.”

    You were part of a panel at this Summit focused on redefining CX in the era of digital lending. Can you give us an overview of your thoughts?

    “So, maintaining that personal touch is really important because that personal touch helps us to build trust with our customers. We all know that when dealing with money, that trust element is super important. There’s lots of things that iwoca does to maintain that. For example, every customer has a dedicated account manager. They can get through to them via a direct number. We also respond to emails fast, every email on the same day. And then we commit to answering at least 80% of calls in less than 60 seconds. We’ve got 10,000 new applications every month and about 30,000 customers making repayments currently. We’re doing all of this with an account management team of just 30 people. So, to maintain that level of personal touch whilst also being able to deal with that volume of customers, we absolutely have to leverage digital technology to be able to do that really efficiently. And there’s many ways that we do that…

    “First of all, we make sure that our account pages and our signup flow is as clear and seamless as possible so that customers can self-serve if they want to. But we also make sure that with our operations activities, we’ve broken down every step of every operational process into a task that is visible on our in-house built CRM system. And then what we can do is run tests on every single step of those to see where having human interaction really adds the most value. So, we are constantly upgrading where we apply human interaction in a really forensic way to make sure that it’s optimised as much as possible.”

    Why is this an exciting time for the business?

    “It’s really exciting right now. We’ve been having some record months recently and broken some big milestones. We are now approving around 10,000 new business loans every month, which is huge. Our loan book across the UK is almost £1 billion. And then a bit closer to home, we’ve also just moved offices. We’ve got more space and we’re still able to attract exceptional talent into iwoca and it’s great to have a new home in central London to do that.”

    “Embedded finance is a big trend right now. It’s important for us to make sure that customers can access lending when and where they need it. We’re integrating lots of partners through our open API – around a third of our applications come through partner channels. So, that’s a very important trend and growing for us in the future. We’re also seeing a lot of hyper-personalisation. We know that customers want to be able to tailor loan products exactly to their needs, and we want our products to be able to provide that flexibility to them. We’re looking at increasing loan amounts, changing durations and offering different types of repayment schedules with interest only options. And that’s hugely exciting. And one of the big trends that I’ve heard about here at FTS, and which we are working on at iwoca, is how we leverage AI and what we might be able to do with AI to make us even more efficient, but still maintain an excellent customer service.”

    What pain points are your customers experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

    “So, it’s important to remember that iwoca exists in order to solve pain points for customers because customers were just relying on traditional lenders. Those traditional lenders, the big banks, have much longer application processes, typically taking weeks and sometimes just aren’t able to lend to those customers at all because it’s not within their risk appetite. Whereas at iwoca you can get a loan within minutes. We can also lend to customers that banks couldn’t lend to because we’re able to use data and data science to be able to understand the risk level and different customers much better.”

    Tell us about a recent success story…

    “We are operational in the UK and Germany, and a success story for us is the fact that we are now working with a loan book of almost a £1 billion and we are profitable. And we have been for quite a while now, since early 2023. So, it’s a real success story for us that we’re able to use that profitability to fund our core business growth but also use it to invest in solving other pain points for customers beyond lending.”

    What’s next for iwoca? What future launches and initiatives are you particularly excited about?

    “Yeah, there’s a lot of things that we’re working on right now. I’m excited about some of the AI tools that we are trialling to make our service even more efficient. There’s a number of exciting applications out there, so there’s a lot of people at iwoca exploring and exploiting different AI technologies. It’s going to be very exciting to see how that rolls out across our business in the rest of this year. And then also looking at new ventures that are beyond lending, which we may be launching later this year or early next.”

    Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

    “Collaboration is hugely important to us and our business model. Traditional banks are able to access capital more cheaply than we can, but they’re able to provide us with access to their balance sheet so that they provide financing to us so that we can then lend to our customers. So, with their financing, we are able to use our data and our technology to reach customers that they wouldn’t be able to reach directly. At the moment, something like 80% of our funding comes from banks such as Barclays and Citi. So, they’re hugely important to us and we are continuously reviewing with them the performance of our own book and finding ways that we’d be able to lend to more of our customers.”

    Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for iwoca?

    “This is my first time at this event, and I’ve been really impressed. It’s been really well organised and the panels have been insightful with some great speakers. I’ve learned quite a lot. I’ve met some really interesting people and I’m really impressed by the diversity of people that are coming here. So, I was just on a panel with somebody from Zopa, which is where I used to work. I also met somebody in the audience who came from Lloyd’s, which is where I worked about 15 years ago. So, it’s great to see that this ecosystem being brought together at FTS.”

    Learn more at iwoca.co.uk

    About iwoca

    Fast, flexible finance empowers small businesses to manage their cash flow better and seize opportunities – making their business and the economy stronger as a whole. At iwoca, we do just that. We help businesses get the funds they need, when they need it, often within minutes. We’ve already made several billion pounds in funding available to over 100,000 businesses since we launched in 2012 and positioned ourselves as a leading Fintech in Europe. Our mission is to finance one million businesses. We’ll get there by continuing to make our finance ever more relevant and accessible to more businesses by combining cutting-edge technology, data science and a 5-star customer service.

    • Events
    • Neobanking
    • Together in Events

    Author

    Dan Brightmore

    Published

    6 August 2025

    Estimated Read time

    6Mins

    Read in brochure format

    FinTech Strategy speaks with Jonas von Oldenskiöld, Head of Partnerships at Qover, about the future for the insurance industry

    Financial Transformation Summit 2025 EXCLUSIVE

    At Financial Transformation Summit, Jonas von Oldenskiöld, Head of Partnerships at Qover, spoke on a panel (alongside peers from Davies Group, Accenture, Superscript and YuLife) entitled ‘Bridging the Gap: How InsurTech is Reinventing Traditional Insurance Processes’.

    Following the panel, we spoke to Jonas to find out more…

    Hi Jonas, tell us about your role at Qover?

    “I’m the Head of Partnerships at Qover. We are focused on embedded insurance. We try to enable that for a lot of different players in the markets. Everything from motor insurance, SMEs, going the whole way down to simple things like classes[1]  such as travel, trying to be the enabler between the typical risk carrier and the distribution platform.”

    You spoke on a panel at the Summit about InsurTech innovation. Give us an overview of your thoughts…

    “It was a very interesting group of people on the panel coming from different angles across the industry. And the key things for me were around where InsurTech needs to go now and how it enables insurance companies at this point in time. The common understanding was that we, the InsurTechs, come from being disruptors to being more of a force into them where we can plug in and help them to change a little bit the behaviours that are currently going on. Being that catalyst in the organisation and helping them to drive innovation. Because I think a lot of large organisations have realized that innovation cannot be driven by a single hidden team somewhere, it needs to be driven from a business perspective.”

    Why is this an exciting time for Qover?

    “I think there are many reasons. Of course, you cannot be at an event like this without speaking about AI and the opportunity that gives to us. Also, we’re seeing a generational shift. The industry needs to get ready to service a completely different type of customers going forward and that will drive a lot of exchanges we’ll see in the next couple years.”

    “I think a key one is to be able to navigate the future role of AI regulation. That will be very interesting to see what opportunities are there and what opportunities would be possible to use. More importantly, I think it is taking data from something, using data from something that is good to have, to really put it in the forefront of the operation to start planning your business process from a data perspective. This is the data that we need to have in order to deliver a good product rather than having data as the outcome of the whole process. You have set up and try to do something from that perspective. So, we need to turn the table on that.”

    What other pain points your customers are experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

    “They particularly need help with the UX and how to deliver the product. I think the underlying product itself doesn’t change so much, but it’s a lot about the delivery, making sure that it actually does get delivered at the point in time that we like to call events driven. So, for us it is distributing insurance when you have a life event, if that is having a child, buying a car, buying a house or whatever it might be, data can help us to drive that. So, for us it’s very much around the delivery rather than the product underneath.”

    Tell us about a recent success story…

    “We’re very proud that we now have several new motor programmes in place where we have been working with large motor organisations that have realized that they’re not only selling a car, they’re selling a means of transportation and convenience, which also then includes insurance across that whole journey. We recently announced partnerships with both Volvo and BMW. And we have more in the pipeline. So, I think that has been a great success where large established industries have realised they need to go further in order to have that UX design.”

    What’s next for Qover? What future launches and initiatives are you particularly excited about?

    “In 2025, our focus is on expanding into more new verticals. We are involved in driving that engagement to see where we can expand. We started traditionally with a lot of the travel organisation and bike providers. We’re now working with neobanks[2] , traditional banks and the motor industry. I also see more opportunities in areas like utilities, in SME supporting functions, everything from accountancy to data provision and being a software provider. These expansions will be the goal over the next 24 months.”

    Why do you think the evolution of collaboration between industries and InsurTechs is set to continue? What are you excited about?

    Partnerships is one of the key things changing the insurance industry. We still have some very large players around. They’re fulfilling their function, and they do it very well. But in order for them to adapt into the new situation, partnerships are important. You always need to be able to work at scale, which is important for them. Of course, with a partnership you lose a little bit of control compared to acquiring something or developing it yourself. But on the other hand you win on the speed to market and potentially also on the cost side. So, for me, the winners will be the ones that can handle partnerships in the right way. And at the end of the day, a partnership is a relationship. You can have as many contracts as you want, but it comes down to people.”

    Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Qover?

    “We get a lot of good feedback and the great thing with events like this is that you have the chance to do networking both informal and formal. You’re having a formal agenda but also have a chance to rotate around. I always make sure to join the sessions and round tables. It has been interesting to speak to peers across the industry. It’s a good way of getting away from the desk and finding some new inspiration.”

    Learn more at qover.com

    About Qover

    Embedded insurance orchestrators… We’re creating a global safety net with insurance,

    empowering people to live life to the fullest.

    Qover was founded in 2016 by Quentin Colmant and Jean-Charles Velge. From the very beginning, our co-founders had a clear vision of the future of insurance: a simple, transparent and accessible service across borders.

    Through embedded insurance, we can create a global safety net that protects everyone, everywhere. To that end, our embedded insurance orchestration platform enables any company to harness the power of technology to embed insurance as a native component of or add-on to their core product or service.

    In doing so, embedded insurance becomes a powerful tool for businesses to enrich their value proposition, enable their success and care for their community.

    • Events
    • InsurTech
    • Together in Events

    Author

    Dan Brightmore

    Published

    5 August 2025

    Estimated Read time

    6Mins

    Read in brochure format

    FinTech Strategy meets Vikki Allgood, Director of Technology Strategy at Fidelity, to discuss the fundamental importance of culture in driving a successful business transformation

    Financial Transformation Summit 2025 EXCLUSIVE

    At Financial Transformation Summit, Vikki Allgood, Director of Technology Strategy at Fidelity International, gave a keynote speech entitled ‘Psychological Safety – The Hidden Key to Transforming Your Business’. Following her appearance, we spoke to Vikki to learn more…

    Hi Vikki, tell us about your role at Fidelity?

    “I am Director of Technology Strategy for Fidelity. We’re looking at how we can ensure we can adapt our response to our business’ needs through our technology to meet whatever demand is coming over the horizons tomorrow. And in the years to come.”

    You spoke at this Summit about psychological safety driving business transformation. Tell us more…

    “At Fidelity, our strategy for our technology has culture as our foundational pillar. Talking with our leaders over the last 18 months, we looked to understand how we can create a brilliant culture, recognising that psychological safety is a fundamental element in that.

    “Transformations often stumble because the business plan forgets its most volatile, and most valuable component, the people asked to deliver it. Without psychological safety, even well‑funded and organised programmes stall. Teams focus more on protecting themselves instead of challenging ideas. That’s when the risks remain hidden until it’s costly, and the collective new ideas to solve the biggest challenges are never formed. That’s why we ask leaders to invest time and energy in building a culture where it’s safe to question, experiment, challenge the status quo and admit what’s not working. In that environment the behaviours every transformation depends on (curiosity, creativity, problem‑solving, healthy challenge) all naturally emerge.

    Psychological safety isn’t some new trendy HR slogan, it’s a timeless basic human need wired into our biology through millennia of evolution. When people sense social threat, the amygdala floods the body with cortisol and the prefrontal cortex (the part of our brain we rely on for reasoning, innovation, etc.) literally dims. Remove the threat, and the brain’s chemistry flips, dopamine and oxytocin rise, and teams move from cautious compliance to bold collaboration. Leaders must ask themselves if their teams can lean in and challenge effectively or if they are staying quiet to protect themselves. The hidden key is simple, but non‑negotiable, leaders must consciously, relentlessly and courageously build psychological safety through everything they do and say. If they do that, then your technology and transformation plans will have the human engine they need to succeed.”

    Why is this an exciting time for Fidelity?

    “I think that within the industry, all the opportunities that are coming along, and our ability to adapt to our customers’ needs, is what makes it exciting. We are all on an exponential curve of change. Technical possibilities, customer expectations, regulatory demand, industry landscapes, are all going to keep moving, with new challenges and opportunities presenting themselves. We are ensuring that we can meet those needs of our customers both today and tomorrow. Finding new ways to do that is pretty exciting.”

    “So, from a technology perspective, I would say that we are making sure that all our foundational elements are there so that we can respond and adapt. One of Fidelity’s differentiators is that we have historic long running relationships with our customers. We are reintegrating our data strategy to allow us to better leverage this, in addition to market data, allowing us to provide personalised solutions to our customers.

    “AI is absolutely generating a buzz for us right now as well, and not just Generative AI. We’re seeing a push towards Agentic AI and how we can look to provide faster, quicker, more cost-effective services for our business partners who can then provide better outcomes for our customers. This in combination with our long-standing history gives us a unique opportunity.”

    What pain points are your customers experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

    “We need to understand the new generations entering the wealth space and what their expectations are and how they engage with us. We’re looking to ensure we can keep pace with their demands. For example, we’ve just launched Pay by Bank allowing our customers to pay money into their accounts in a faster more secure way. This feature leverages the Open Banking Technology that is now available to financial institutions.”

    Tell us about a recent success story for Fidelity…

    “Across the technology landscape, we have been amplifying our existing cloud strategy by removing complexity in our hybrid setup, reducing the number of dependencies back to on-premises. This is a well-known challenge for financial institutions who have regulatory reasons to have highly confidential systems in house. This will allow us to respond at pace to what customers need. Looking a couple of years down the line nobody can be sure what the next big opportunities are going to be, so ensuring we’re building that foundation to respond to what comes over the horizon is fundamental.”

    What’s next for Fidelity? What future launches and initiatives are you particularly excited about?

    “Security is incredibly important to us. With that in mind, we are exploring Quantum to understand both the opportunities and risks that it could present in the future and how we can stay at the forefront of it. Ensuring a secure and reliable service for our customers is an absolute non-negotiable part of our strategy.”

    Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

    “I think the reality is that we need the collective mindsets to come together to create the best outcomes. We’re never going to have all the answers all by ourselves. So, starting to engage and work with people and collaborate means that we get to have a better, wider perspective. Coming to events like this, we get to learn, understand what other industries are doing, what other areas are looking at, and it helps to widen our perspectives and have more opportunities to find those out of the box ideas that are going to then help our customers.”

    Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Fidelity?

    “I was particularly keen to attend this conference because I think transformation and how we can do this successfully is so important at the moment. The reality is, sadly, and I covered this in my talk, a staggeringly large number of transformations miss the mark or fall short. And so, learning and embracing how you can ensure that you go after it and you get the value that you’re aiming for, that is for me what’s important. As I said, getting that learning, talking to each other, understanding what’s worked, what hasn’t worked and sharing tips and techniques is actually incredibly powerful and something you can then take back and use at your organisation.”

    Learn more at fidelity.co.uk

    About Fidelity

    It has been more than 50 years since we were founded. We’ve seen many market cycles – bull and bear, boom and bust. We have stayed the course through different investment environments regardless of market performance.

    The needs of our customers have always steered our decisions, which is why we’ve stuck to our core activity of investing. We believe this is what allows us to excel – and, even more importantly, to repay the trust placed in us by our customers.

    Whether you’re investing for the first time, or have a wealth of experience, it’s essential to be informed and to be comfortable with your decisions. Through Trustpilot, you can read up-to-the-minute, real-world reviews and see for yourself how Fidelity aims to put the customer first and make investing a bit easier.

    Our do-it-yourself online services give you 24/7 access to our investment guidance, handy tools, and range of accounts from your computer, tablet or phone. Transfer your existing investments to us, or open a new account online and begin investing in just a few steps.

    • Artificial Intelligence in FinTech
    • Events
    • Together in Events

    Author

    Dan Brightmore

    Published

    4 August 2025

    Estimated Read time

    8Mins

    Read in brochure format

    FinTech Strategy met with Standard Chartered’s Head of Digital Assets – Financing & Securities Services, Waqar Chaudry, at Money20/20 Europe to discuss how the bank is connecting traditional with digital, collaborating with FinTechs directly and via SC Ventures, and taking a measured approach to entering the crypto market

    Money20/20 Europe Exclusive

    There is a buzz in the air at Money20/20 Europe. Waqar Chaudry, Head of Digital Assets – Financing & Securities Services at Standard Chartered, has just spoken on Mastercard’s Horizon Stage about the great digital assets opportunity. We meet up with him at his bank’s stand in the heart of the action at the Amsterdam RAI Arena.

    Waqar works in custody to secure digital assets at Standard Chartered. It also has a fund accounting business and offers transfer agent services. “The financing in the Financing & Securities Services elements are in our FX Prime offering,” he explains. “At the moment my sole focus is on crypto custody, tokenisation and building an ecosystem around those products.”

    The Rise of Digital Assets

    It’s an exciting time for Standard Chartered with crypto custody and the rise of stablecoins and tokenisation… Whether the asset is Bitcoin, a tokenised money market, or anything tokenisable, there have been a lot of conversations with the bank’s partners in terms of the technology quest.

    “Most of the conversations historically have been led by the fact that technology does give you the capability to do 24/7 trading and settlement. Risk management from the technology side is much better. The blockchain dream is sold to everyone, which remains true,” notes Waqar. “The issue has been that on the business side, tackling the areas that actually can work with this technology. You have your near instant settlement availability on blockchains. On the other side you have a T+1 or T+3 cash settlement time – that doesn’t gel very well.

    “Entrenched in the day-to-day business of these really large institutions is to be able to inject a new piece of technology. And then suddenly say, hey, all these things are solved. For all the inefficiencies in the system it doesn’t work that quickly. We’re actually taking one step at a time. That’s why it’s exciting that we can see in five or ten years from now what the world will look like. Basically, in our vernacular that means we have near instant settlements and near instant international transfer of value. So, that’s the kind of stuff that we are really interested in for the future.”

    Meeting the Blockchain Challenge

    Waqar explains that when something like a blockchain comes into a traditional bank, and especially blockchains like the ones that support an asset like Bitcoin, you don’t know who the counterparties are (which are clear on the SWIFT network).

    “You have to build capability from a technology side, operations side, risk management side,” he continues. “You need to develop the governance of all those functions to be able to get the value of the asset in the ecosystem. And then be able to add value to that to transact on it. We don’t yet have those ingredients, so it becomes very challenging for us to accept the assets. A lot of the work that the bank has done over the past five years has been around embedding those elements into our day-to-day operations. It’s about understanding the risk profile of the coins and understanding the risk profile of the blockchains.”

    Waqar’s team works on how to protect the ecosystem from risks from both an AML and KYC point of view. “We’re also making sure that by doing that we don’t create such a burden to the client that the service becomes useless,” he adds. “We’re trying to balance that out and that’s where the challenges lie at the moment. The next stage is to also be able to integrate all of our traditional cash and assets rails into this. And that’s where the next level of risks will come in… Where people are not used to seeing things on the blockchain… They are used to seeing things on the SWIFT network or a CSD. But when the blockchains come in, profiles will change and that’s where we have to meet the challenges.”

    Traditional Meets Digital

    For an asset manager with a variety of equities and bonds, but keen to start in crypto and other digital assets, the rails are very different… “The liquidity venues and the way you settle the instrument are very different. And they don’t naturally talk to each other,” confirms Waqar. “It’s a big challenge. But to be able to go with the provider that has all the capabilities, which includes the cash side, the asset side, the crypto side and the blockchain side, is something people are looking for now. Without having the end-to-end picture, it would be very difficult for our clients to have an equitable strategy for their clients. We need to be able to service them appropriately based on the rails they operate in.”

    For Standard Chartered’s clients it’s increasingly important for payments to facilitate activity on-chain regardless of the use case of digital assets. “There is a key challenge with payments at the moment. If you do transfer value across geographies or between B2B and B2C, what do you do with that value afterwards?” asks Waqar.

    “Are you going to keep it on the books for your treasury or account purposes or are you going to find a way to liquidate the position to pay your employees or pay your service provider? Without the capability to store the asset appropriately and then convert it into a usable form, you can’t do much with it. The only thing you can do is actually transfer value. So, for us what’s important in payments is that we get the transfer value happening immediately. Or as quickly as possible. And then also connect our payment infrastructure and the banking behind. We aim to support the transfer of value from a digital asset into an actual cash asset.”

    Building on Success

    Standard Chartered’s work with OKX in Dubai has spurred demand the bank didn’t expect. “The key ingredient is that a really large crypto exchange has come together with a really large bank,” reasons Waqar. “When you combine the product features of a large bank like ours with the liquidity of OKX it creates a unique proposition in the market. The traditional players have started to show interest in that because now they can buy diverse assets, pledge them as collateral and start trading while the assets remain safe in a genuine large institutional bank. And at the same time, they also have access to a highly regarded institutional exchange. That story is for us quite important and we’re fostering these relationships more and more…”

    It’s been a real success story for Standard Chartered on the money market fund side which is also connected to what the bank is doing on the collateral side. “Money market funds are used to gain value and have an asset that does generate yield on the one side, but also the capability to use the asset as collateral is important,” adds Waqar.

    “The money market fund that we launched for China Asset Management in Hong Kong, albeit it’s a retail use case for a start, but then the ambitions are big. The next thing is how do we start using that same asset for pledging for trading purposes and then how do we inject that into a portfolio basket of assets that people buy? At Standard Chartered, we aim to create a supermarket of tokens in a centralised ecosystem. So, our collateral story and the tokenised money market funds is connected, and we want to continue building around it. We’re thinking about other assets now too… We’re looking at equities, bonds and enabling more cryptocurrencies in the same ecosystem as well. It’s just the start of all the things we need to build in the future.”

    Why Money20/20?

    “This is my first time coming to Money20/20 Europe. Digital asset companies are here alongside financial services and related FinTechs. It’s great that they’re able to talk to each other and it’s quite evident there are lots of great meetings happening. There are many companies here we are either supporting or we’re working with. We’ve also had meetings with UK government representatives geared to attracting talent into the country. They’re trying to make sure that their FinTech ecosystem grows quite significantly for us in the UK and for other footprint markets in Asia; Middle East and Africa are also quite important in how we do that and continue to grow.”

    The Evolution of Collaboration between Banks and FinTechs

    Standard Chartered is also working in harmony with its ventures partner SC Ventures. The bank is working closely with Libeara for tokenisation and with Zodia Custody as Saas. “Our core institutional bank and our Ventures business are quite tightly coupled from that point of view,” says Waqar. “And it’s quite obvious that the reason for that is how we’ve made significant investments into them. We’ve given part of our DNA into this ecosystem and now, at the bank, they’re building the ecosystem around these capabilities, so we’re keen to bring them in and use their solutions for our services as well.”

    Standard Chartered may be a traditional bank but it is a seasoned collaborator with innovative FinTechs. “They need traditional services too,” reasons Waqar. “Once they get to a critical mass, a FinTech may not have the bandwidth to manage certain client sizes. By partnering with some of the FinTechs, we’re seeing that once a certain size of a client comes in, they prefer to work with a large institution like ours. So, that partnership is proactively managed as well from our side. From our ventures side, bringing their innovative approach to product development and technology into the bank, building the ecosystem around risk management and governance from the bank side and then connecting into the FinTechs outside of that ecosystem is something I think is quite an interesting proposition for us. We’re going to keep building on top of that.”

    Standard Chartered – Financing & Securities Services

    Promoting your future in global securities

    We’re ready to help you flourish in emerging and frontier securities services markets

    In today’s fast-moving markets, especially  across Asia, Africa and Middle East, success isn’t just about the solutions you choose – it’s about the partnerships you build.

    Standard Chartered has been committed to these regions for decades. We understand both the promise and challenges. That’s why we go beyond delivering end-to-end custody, fund, and fiduciary  solutions – we actively help shape the markets themselves.

    By working with local governments and industry associations, we bring you early insights and access to new opportunities. Partnering with leading asset managers, fintechs, and infrastructure providers, we connect you to the best of the industry, via a single partner. Because in a world of complexity, collaboration is your greatest advantage.

    Learn more at sc.com/en/corporate-investment-banking/financial-markets/financing-and-securities-services/

    • Blockchain & Crypto
    • Events
    • Together in Events

    Author

    Dan Brightmore

    Published

    1 August 2025

    Estimated Read time

    5Mins

    Read in brochure format

    FinTech Strategy meets Ishtiaq M Ahmed, Senior Product Manager – Emerging Tech, Innovation & Ventures at HSBC, to learn more about the future of payments – real-time, cross-border and beyond

    Financial Transformation Summit 2025 EXCLUSIVE

    At the Financial Transformation Summit 2025, Ishtiaq M Ahmed, HSBC’s Senior Product Manager, for Emerging Technology, Innovation & Ventures, joined a panel with J.P. Morgan, Revolut, Lloyds and EY to explore how real-time payments, embedded finance and global collaboration are shaping the future of financial services. How are real-time payments reshaping banking infrastructure? What are the regulatory challenges for cross-border payments? How can banks compete with FinTechs in the rapidly evolving payments space? How are digital wallets and mobile payment platforms changing consumer spending behaviours?

    We spoke with Ishtiaq after the session to explore what drives HSBC’s approach to innovation, how customer expectations are evolving, and why trust remains at the core of transformation.

    Hi Ishtiaq, tell us about your role at HSBC?

    “I work on Global Product within HSBC’s Emerging Technology, Innovation & Ventures team. Our focus is to deliver next-generation propositions, particularly across payments, embedded finance and frontier technologies. We work on horizon 2 and 3 initiatives, with a view to turning emerging ideas into viable, scalable solutions. The goal isn’t just to experiment. It’s to test, validate and shape innovations that will help us serve customers better and redefine how financial services operate in the years ahead.”

    It’s a transformational time for payments with the rise of open banking and a national vision for the UK. Give us your overview…

    “Payments is possibly the most loved area by both FinTechs and banks. A lot of what is happening in payments, it’s where a lot of meaningful innovation is already landing. It’s no longer theory or ideation, its practical and accelerating. The UK’s National Payments Vision is ambitious, and rightly so. But ambition needs alignment. We need stronger collaboration between Banks, FinTechs, Regulators and infrastructure service providers. This journey will take time and coordination. It’s more a marathon than a sprint, and we’re only just getting started.”

    Why is this an exciting time for HSBC?

    “Simply because the way technology has penetrated our lives and the influence of technology on how banking is evolving are very closely knitted. Technology is no longer on the edges of banking; it’s embedded in every customer interaction.”

    “The shift towards alternative payment methods is one I feel strongly about. For decades, the path was linear: cash to cheque to card. Now, we’re entering a new chapter. Pay by Bank, or direct account-to-account payment, is gaining traction. Some regions have already scaled it. In the UK, it’s about to accelerate. This trend will unlock lower costs, faster movement of money and better control for users. It’s not just about technology. It’s about user experience and future-ready infrastructure.”

    What other pain points are your customers experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

    “I think for customers it’s very simple. As a customer myself, I look for speed, ease, and simplicity in everything that I do. That’s universal. But what makes it complex today is the influence of AI, automation and data. People want innovation, but not at the expense of trust. So, while we innovate, we keep trust as the anchor. The real test is whether customers can do more, faster and easier, while still feeling their money is protected and their experience is safe. That’s the balance we aim to strike.”

    Tell us about a recent success story…

    “We’re particularly proud of the work we’re doing on embedded payments. The goal is to make payments feel invisible – integrated into the environment the customer is already in. Whether that’s a retail website, a social app or a business platform, customers shouldn’t have to toggle across apps to complete a payment. We have already launched products in this space, and we’re continuing to build. It’s about making banking ambient – present where the customer is, not where the bank wants them to be.”

    Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

    “FinTechs bring urgency and imagination. Banks bring trust, infrastructure and scale. The opportunity is not in competing, but in co-creating. We have seen some encouraging partnerships, and we’re still working at the surface level. There’s a much deeper layer of value if we can move beyond tactical deals into genuine joint innovation.”

    Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for HSBC?

    “Events like this are important because they bring together different voices with a shared interest in shaping the future. What stood out to me is how open the audience and panellists are to challenging ideas and exploring new perspectives. These are places where real conversations happen; where you meet regulators, banks, FinTechs and enablers all under one roof. It’s these intersections that move the industry forward.”

    Learn more at ventures.hsbc.com

    About HSBC Emerging Technology, Innovation & Ventures

    HSBC Emerging Technology, Innovation & Ventures team is a global group of technologists, data scientists and venture specialist dedicated to shaping the banks future capabilities. Our goal is to deliver world class digital-first banking across HSBC’s global footprint.

    Our mission is to drive meaningful innovation across the organisation by identifying and unlocking opportunities that enhance customer experience, improve operational efficiency and embrace disruptive technologies.

    Our approach is rooted in experimentation, rapid prototyping, continuous iteration. By working closely with both internal and internal partners and external collaborators, we test and refine new ideas, prioritising solution that are scalable, impactful and aligned with the needs of our customers.

    We actively partner with leading technology firms, FintTechs, academic institutions and policy makers to stay at the forefront of digital innovation and accelerate time to market.

    By combining the scale, trust and resilience of HSBC with agility and mindset of a tech start-up, we aim to nurture transformative ideas, drive strategic innovation and shape the future of banking.

    • Digital Payments
    • Events
    • Together in Events

    Author

    Dan Brightmore

    Published

    31 July 2025

    Estimated Read time

    7Mins

    Read in brochure format

    FinTech Strategy speaks with Matt Bazley, Account Executive at Hyland, to explore how the content intelligence and process automation specialists are helping to drive operational efficiencies for their financial services clients

    Financial Transformation Summit 2025 EXCLUSIVE

    Hyland empowers organisations with unified content, process and applications intelligence solutions, unlocking the profound insights that fuel innovation. The Hyland team was at Financial Transformation Summit to reveal the ways organisations can transform their processes with the Hyland Content Innovation Cloud™. By combining AI-powered automation with built-in integrations to productivity tools and business applications, Hyland streamlines workflows across multiple channels, accelerating response times, boosting productivity and improving customer satisfaction.

    At the event, Neil Rayment, Sales Solution Engineer, demonstrated the intuitive end-user experience and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business. We spoke to Hyland’s Matt Bazley, Account Executive for Financial Services, to find out more…

    Hi Matt, tell us about your role at Hyland?

    “I’m the Account Executive responsible for banking across the UK and Ireland. I’ve been with the company for just over 18 months. Across my career, I’ve been helping financial services institutions for over 15 years with digital transformations and various programmes.”

    What are the key digital transformation solutions Hyland offers Financial Services organisations? How are they making a difference? What are some of the use cases you’re exploring?

    “Hyland is at the cutting edge of the content space. We have what we call our Content Innovation Cloud, which is delivering content intelligence, process intelligence and application intelligence. What that means in reality is that we’re helping organisations get access to their content that they don’t currently have access to because it’s spread over many siloed systems and sat in an unstructured format. So, with our content and intelligence, we’re able to get access to that unstructured data, which is around about 80% of an organisation’s data in the financial services sector. And we’re able to then provide knowledge and insight on that content, which helps organisations to make better strategic decisions. Allied to that, with this process intelligence, we’re able to help automate processes across the business. Whether it be orchestrating use cases and workflows or integrating with other systems to deliver application intelligence, we’re able to manage that whole end-to-end life cycle of information across an organisation.”

    Why is this an exciting time for the business?

    “We’re excited because our strategy is really leading the way. We’re leveraging large language models (LLMs) and AI to be able to deliver these real-life use cases that solve actual challenges. A lot of the time AI projects fail because businesses are trying to implement AI that isn’t actually a solution solving a problem. Whereas the AI we’re using is to actually solve a real-life challenge that businesses face because they want to be hyper-personalised for customers and more customer-centric. And you can’t really do that if you’re only leveraging 20% of the data you hold about your customers. And that’s why getting access and insight around this unstructured data is really vital for financial services organisations right now. We are able to help them leverage that unstructured data and meet them where their data is at. So, it’s not a case of having to migrate all of that data into different platforms or into our platform. We confederate across your information wherever it’s held as a financial services organisation; and that’s really a game-changing position for us and for the industry.”

    “AI is the big one. Although it is a bit of a buzzword that everyone’s mentioning nowadays, we’re actually delivering AI solutions to solve problems that businesses face. And that’s one of the real trends in the industries. Most AI projects fail, and companies want AI projects that succeed and deliver real value. The other thing we’re seeing is the rise of hyper-personalisation as part of being really customer-focused and customer-centric. Again, by helping businesses leverage that 80% of information around their customers that they don’t currently have access to, and provide insights on that information, we’re helping those organisations to become really specific and personalised in their dealings with their customers.

    “The final piece is around data and governance. So, security around our data as customers, because we’re all consumers at heart and want to know that our information is secure. Using best-in-class processes around security and governance is what we’re really focused on. And that’s a real trend in the market as well. We’re making sure that while we’re leveraging that information about customers, we’re keeping it safe and only using it for what it’s intended for and making sure the processes and governance around that information are really robust.”

    What other pain points are clients in the FS space experiencing that you need to address? What are they asking you for help with? How are you meeting the challenge?

    “The one big one is the siloed information across multiple systems as part of digital transformation strategies. Over the years, I’ve seen many businesses implement point solutions. They might be best-in-class point solutions… But that means you end up with information and data and processes across 10, 15 or 20 systems. How do you then unify that data and leverage it to make the user journeys more effective? And also the customer journeys better, whatever channel those customers are using?

    “What we see is that while trying to be omnichannel for their customers, organisations end up with multiple solutions. One for their mobile app, a solution for their website, a solution for in-branch banking… So, you end up with omnichannel processes that are actually siloed processes. What we are trying to help businesses do is to unify those processes. We can break down those silos and make it a really seamless, integrated journey internally and externally for colleagues and customers.”

    Tell us about a recent success story …

    “A great example is our work with ABN AMRO – a bank that is one of our longstanding and valued customers. They were looking for a solution because of this very challenge. The bank had multiple siloed systems holding a lot of information and a very complex architecture. They went to market and Hyland was able to prove our solution was able to manage the sheer volume and complexity of the information and content that they had. And most importantly we were able to help them integrate with their line-of-business systems very easily to create that seamless internal/external journey for both users and customers.”

    What’s next for Hyland? What future launches and initiatives are you particularly excited about?

    “It’s all about continuing to grow for us. With the Content Innovation Cloud, the reception we’ve received from the market, from our customers, has been absolutely tremendous. Businesses are so excited to see the ability and capability of what we’re able to do. And what we’re able to deliver for them in terms of real value through the Content Innovation Cloud. We’ve got customers onboarded already. It’s now about expanding that list of customers who are going to see real value from leveraging the cloud, our AI solutions and driving efficiencies with our content process and application intelligence across their businesses.”

    Why do you think the evolution of collaboration between banks and FinTechs is set to continue? What are you excited about?

    “Across the market over the last 15-20 years the banks are starting to see FinTechs more as allies than competitors. And they’re leveraging these technologies rather than trying to challenge them. I think that’s going to continue because FinTechs are far more agile. And as customer expectations continue to evolve and become more demanding, banks need to evolve and deal with these demands more effectively and more fluidly. And that’s why leveraging FinTechs is going to be a key differentiator over the next 10 years. That trend is going to continue where banks and FinTechs work together and collaborate rather than challenge each other.”

    Why Financial Transformation Summit? What is it about this particular event that makes it the perfect place to embrace innovation? What’s the response been like for Hyland?

    “It’s my fourth year coming here with a couple of different companies and I always find this event really valuable. Not only to obviously promote our products and our brand… But to speak to key decision-makers and peers across financial services. We aim to learn from them about whether the challenges we perceive as a vendor are seen by them as a customer. We will continue to learn and evolve our business around key market challenges. Hyland can then focus our solutions around the real-world problems our peers are seeing across financial services. Coming to this event is a great way to meet as many people as possible. And just really enjoy having those meaningful conversations with leaders in the financial services sector.”

    Learn more at hyland.com

    About Hyland

    Hyland puts your content to work, making it smarter and more accessible in the moment of need.

    Hyland’s content, process and application intelligence solutions empower customers to deliver exceptional experiences to those they serve. The solutions capture, process and manage high volumes of diverse content, helping you improve, accelerate and automate operational decisions and workflows.

    3 Core enterprise content management solutions

    20+ Distinct product offerings

    1,000s of ways to transform the way you work

    • Artificial Intelligence in FinTech
    • Events
    • Together in Events

    Author

    Dan Brightmore

    Published

    5 June 2025

    Estimated Read time

    3Mins

    The final day at Money20/20 Europe 2025 was packed with more insights on the future of FinTech, from banks to borderless innovation.

    Money20/20 Conference Themes & Tracks

    Money20/20 Europe 2025 is structured around four thematic content tracks:

    • Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
    • Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
    • Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
    • Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.

    Day three featured more impactful sessions across all four pillars, offering attendees more valuable insights and strategies for innovation.

    Highlights from Key Sessions at Money20/20 Europe:

    How to Create and Leverage FinBank Partnerships

    The discussion focused on the evolution and success of FinTech partnerships with banks. Key points included the shift from transactional partnerships to more collaborative, value-driven relationships, emphasizing joint KPIs and product creation. 

    Alex Johnson, Chief Payments Officer, Nium

    “You really have to differentiate. You really have to stand out for a bank to say, ‘Yeah, I like what you offer enough to go through, six months of onboarding.’ Dare I say, maybe more.”

    John Power, SVP, Head of JVs & AQaaS, Fiserv

    “The legacy system, it’s a fact of life. They’re there. They’re pervasive. They’re going to be here for a long time, and banks historically have made huge investments in those platforms and systems. So I think both the challenge for the for the bank and the opportunity for the FinTech is, how do you at the front end of those legacy systems develop new products that can scale and that you can bring cross border easily and readily.”

    Cecilia Tamez, Chief Strategy Officer, Dandelion Payments

     “It really is cutting the line to be able to deliver opportunity for customers and to be able to expand propositions for new customers.”

    “The economic development supply chains shifting to low to middle income countries are incredibly important right now, and cross border payment rails have not been good in low middle income countries.”

    Where Fintech goes Next: Tapping into Platforms and Verticals 

    The discussion centred on the democratisation of financial services through embedded finance. The panel emphasised the importance of data quality, personalisation, and strategic partnerships in delivering seamless financial experiences – ultimately enhancing customer satisfaction and improving business efficiency.

    Hiba Chamas, Growth Strategy Consultant – Independent

    “Embedded finance is going to be defined by region and use cases.”

    Amy Loh, Chief Marketing Officer – Pipe

    “Small businesses don’t want to manage their business through a bunch of different tools that are stitched together. They’re looking to platforms to do everything for them and keep high end services.”

    Zack Powers, VP Commercial & Operations – Mangopay

    “Most platforms or merchants out there trying to diversify revenue, and they will get auxiliary revenue, or maybe get primary revenue through FinTech activity.”

    The Neobanks Strike Back

    ​​In a dynamic exploration of neobanking’s evolution, Ali Niknam revealed bunq’s remarkable journey from a tech-driven startup to a sustainably profitable digital bank. By leveraging AI across every aspect of their operations, bunq has transformed traditional banking, reducing support times to mere seconds and creating a hyper-personalised user experience. Niknam emphasised the power of user-centricity, showing how innovative features like simple stock trading and multi-language support can democratise financial services.

    The bank’s strategic approach – focusing on user needs rather than investor expectations – has enabled them to expand thoughtfully, with plans to enter the UK and US markets. By embracing technological change and maintaining a relentless commitment to solving real customer problems, bunq exemplifies the next generation of banking.

    Ali Niknam, Founder & CEO, bunq


    “Somewhere in the 70s, we let go of the gold standard, and now currencies are basically floating. The only reason why a dollar or a euro is worth what it’s worth is because of trust and perception. Philosophically, it’s very logical that we have found another abstraction layer by introducing stablecoin, which is not much else than a byte number that has a denomination currency as a backing asset that itself doesn’t have anything as a backing asset. A lot of people might ask, ‘Why would you need a stablecoin? We have euros. I go get a coffee, pay with Apple Pay or cash.’ But there are many countries on this planet where the local currency is not stable. If your country has an inflation rate of 30,000% like Zimbabwe, you would really love to use a different currency. The US dollar has been the currency of choice, but as a normal person, you cannot access the US dollar. A US dollar stablecoin that you can access by simply having a mobile phone – that’s going to be transformational for large groups of people.”

    Innovating When Regulation Can’t Keep Up: Lessons from NASA 

    Lisa Valencia covered an array of topics, from her 35 year career at NASA and Guinness World Record to the rise of private entities like SpaceX, which has launched 180 missions this year, and the increasing role of public-private partnerships in space exploration. The speaker also touched on international collaborations, particularly with the European Space Agency and the Italian Space Agency, and the potential for space tourism and colonization of the moon.

    Lisa Valencia, Programme Manager/Electrical Engineer – Pioneering Space, LC (ex NASA)

    “Back in the day, NASA got 4% of the national budget. Now it’s down to just 0.1%, so we’ve had to get creative with private partnerships. SpaceX is the perfect success story. They came to us in 2007 needing money after some rocket mishaps, and look at them now! From my balcony, I see their launches every other day. They’re planning 180 launches this year alone.Talk about a return on investment!” 

    “We’re planning to colonise the South Pole on the moon. The idea is to extract water and hydrogen from the regolith—both for living there and for fuel.”

    Scaling Internationally in 2025: Funding, Innovating, and Breaking into New Markets

    The conversation focused on the growth and strategy of fintech companies, particularly those with a strong presence in Europe and the US. The panel featured Ingo Uytdehaage, CEO and co-founder of Adyen, and Alexandre Prot, CEO of Qonto. Both leaders expressed a preference for organic growth over acquisitions, emphasizing the importance of scaling efficiently before pursuing an IPO.

    Ingo Uytdehaage, CEO and co-founder of Adyen

    “I think an important part of scaling a company is not just thinking about your product, but also considering the markets you want to address, and how you ensure you become local in each country.”

    “We realised over time that if we really want to bring the customers, we need to have the best licenses to operate. A banking license gives you a lot of flexibility.” 

    “Being independent from other companies, other financial institutions, that gives you flexibility to build what your customers really want.”

    “I think it’s very important, also in Europe, that we continue to be competitive. If you think about regulations and AI, we shouldn’t try to do things completely differently compared to the US.”

    Alexandre Prot, CEO of Qonto

    “We need to be very strict about tech integration and avoiding legacy which slows us down.”

    “We still need to scale a lot before we have a successful IPO. A few team members are working on it and getting the company ready for it. But, the most important thing is just scaling efficiently in the business, and maybe an IPO would be welcome in a couple of years.”

    Putting The F in Fintech

    The panel discussion focused on the role of women in FinTech based on personal experiences.

    Iana Dimitrova, CEO, OpenPayd

    “At times, being underestimated is helpful, because if you’re seen as the competition, driving an agenda is becoming more difficult. So what I found, actually, over a period, is that bringing your emotional intelligence, leaving the ego outside of the outside of the room, and just focusing on execution is is incredibly helpful.” 

    Megan Cooper, CEO & Founder, Caywood

    “The moment we start defining ourselves as like a female leader or a female entrepreneur, you almost kind of put yourself in a bit of a box. And so I think just seeing yourself on an equal playing field and then operating it on an equal playing field and interacting in that way is quite advantageous.”

    “We can’t just want diversity and hope it happens. We actually have to be intentional about creating it.”

    Valerie Kontor, Founder, Black in Fintech

    “Black women make up 1.6% over the FinTech workforce, but when we look at the financial reality of black women by the age of 60, only 53% of black women have enough money in their bank account to retire. We need to start marrying people in FinTech and the people that we need to serve.”

    Money20/20 Europe 2025 closed its doors but the next edition of the conference will return to Amsterdam from June 2–4, 2026, promising to continue the tradition of shaping the future of financial services…

    • Artificial Intelligence in FinTech
    • Blockchain & Crypto
    • Cybersecurity in FinTech
    • Digital Payments
    • Embedded Finance
    • Host Perspectives
    • InsurTech
    • Neobanking

    Author

    Dan Brightmore

    Published

    30 May 2025

    Estimated Read time

    3Mins

    From June 9-13, London Tech Week gathers investors, enterprises, and startups from around the world to network, learn, and solve the most pressing challenges facing the IT sector.

    London Tech Week 2025 is coming. The event will take place from June 9–13 at Olympia London, and is one of the world’s largest tech events, drawing over 45,000 attendees from across 90 countries. Designed to bring together the innovators creating the technologies of the future, the investors who fund them, and the enterprise tech leaders who adopt them, the event is one of the most impactful gatherings of tech professionals in the industry. 

    “Innovators. Investors. Tech giants. The visionaries applying new tech to solve the world’s biggest problems. Enterprise tech leaders who are creating solutions to make work easier and life more fun,” according to the event website. “They all come to London Tech Week to see where tech will take them next.”

    This year, London Tech Week is expanding, occupying double the space at Olympia, new features and a whole new experience. Keynote and expert speakers at this year’s event include: Dame Melanie Dawes, Chief Executive at Ofcom; Darren Hardman, Corporate VP & CEO at Microsoft UK; Dr Jean Innes, CEO of the Alan Turing Institute; Sir Tim Berners-Lee, inventor of the World Wide Web; renowned science educator and broadcaster, Professor Brian Cox; and many, many more. 

    This year’s event targets key demographics across the tech space, including… 

    Startups 

    Attending this year’s event are future unicorns, top investors and the tech leaders of tomorrow. Attendees have the opportunity to connect with visionary founders from some of the UK and Europe’s most exciting startups, and learn how they’re approaching funding, scaling, and solving some of the world’s most pressing challenges.

    Enterprise 

    Attendees will also have the opportunity to learn how large corporates are pushing the boundaries of innovation by embracing emerging technologies. This year’s London Tech Week will feature insights from top industry leaders about how they are driving productivity, efficiency, and competitiveness across various sectors.

    Investors 

    London is home to a world class investment ecosystem, with VCs, CVCs and angel investors. Many will be attending this year’s event — on the lookout for their next venture. The London Tech Week 2025 enhanced app is designed to help startups and other investment-seekers find people with the right profile in order to maximise their time at the event.

    “London Tech Week is THE gathering spot, not even in London or in the UK, but in Europe. You can meet wonderful tech companies here.” – Canva
    Image courtesy of London Tech Week 2025.
    Image courtesy of London Tech Week 2025

    The Fringe 

    The London Tech Week Fringe Event programme takes place from 9 – 13 June across London, featuring smaller organisations and niche topics you won’t find on the more mainstream technology conference circuits. The event’s partners cover a wide range of topics from emerging areas to established industry trends. This year the event it featuring fringe events covering SpaceTech, Healthcare, Areospace & Automotive, Investment, AI, Entrepreneurship, and more. 

    Learning Labs 

    Back for its second year at London Tech Week, the Learning Labs offer diverse content and learning opportunities. These sessions, presented by our leading event sponsors, cater to all experience levels. Learn about The Tech Lifecycle, AI and Data Integration, Natural Intelligence, Building a Strong Digital Core, and more.
    Learn more about attending London Tech Week 2025 here.

    • Digital Strategy
    • Event Newsroom

    Author

    Dan Brightmore

    Published

    21 March 2025

    Estimated Read time

    5Mins

    InsurTech Insights Europe 2025: A Transformational Gathering for the Future of Insurance

    InsurTech Insights Europe 2025, held on March 19-20 at the InterContinental London – the O2, reaffirmed its status as the premier conference for insurance technology professionals across the continent. Drawing more than 6,000 attendees from over 80 countries, the event brought together C-level executives, startup founders, investors, and tech leaders. They explored the evolving future of insurance powered by innovation and digital transformation.

    Key Themes

    With seven stages and over 400 speakers, the conference agenda was packed with compelling keynotes, forward-looking panel discussions, fireside chats, and practical workshops.

    The overarching theme of the 2025 edition was crystal clear: artificial intelligence (AI) is no longer a futuristic concept, it’s the driving force behind today’s insurance innovation. Topics like automation, generative AI, claims transformation, underwriting analytics, embedded insurance, cyber security, and ESG all reflected a dynamic industry poised for rapid acceleration.

    A Focus on Leadership & Diversity

    One of the standout sessions was the panel discussion titled “The ROI of Gender Diversity: Breaking the Glass Ceiling for Women in Leadership”, held on the Purple Stage. Featuring high-level voices from Solera, unlock VC, and AXA XL, the panel addressed the often-overlooked yet crucial importance of gender diversity in executive roles. The discussion didn’t stop at raising awareness; it presented measurable business outcomes tied to diverse leadership and called for action to foster inclusivity across all levels of the industry.

    Complementing this session was “The Women in Insurance Power Group Meet-up”, a networking event held at the Sky Bar on the 18th floor. Attendees not only connected over lunch but were also invited into an exclusive WhatsApp group, encouraging long-term collaboration and support among female leaders and allies in the space.

    The Innovators Hub and the ITI Marquee: Where the Future Was Born

    A major addition to this year’s conference was the debut of the ITI Marquee. A vibrant, purpose-built zone dedicated to showcasing bold ideas and startup brilliance. This space housed the Innovators Hub, which included its own dedicated Innovator’s Stage. Here, early-stage ventures and InsurTech pioneers pitched their solutions to panels of VCs, corporate innovation leads, and fellow founders.

    This setting offered more than exposure, It cultivated real-time connections between startups and investors, giving many smaller players their first shot at meaningful partnerships or funding opportunities. The diversity of ideas, from AI-powered claims processors to data-driven risk models for climate insurance, reflected the industry’s hunger for next-gen solutions.

    Keynote InsurTech Highlights

    One of the most talked-about moments of the event came from Daniel Schreiber, CEO and Co-Founder of Lemonade, whose opening keynote explored how AI can dramatically enhance customer experience in insurance. He challenged the audience to rethink not just how insurance is sold or serviced, but why it’s offered. And how technology can transform its social impact.

    Another crowd favourite was the session on “The Path to Embedded Insurance”, which unpacked how insurance products are increasingly being bundled into digital ecosystems like ecommerce platforms, mobility apps, and smart home technologies. This wasn’t just a hype piece. Real-world case studies from European neobanks and auto insurers illustrated how embedded models are already driving customer growth and retention.

    Among the compelling keynotes on the Main Stage, Sofia Kyriakopoulou, a Fintech Strategy AI Champion and Group Chief Data & Analytics Officer at SCOR, revealed how GenAI innovation at one of the world’s largest reinsurers is transcending the realm of proof of concepts to become fully productive.

    InsurTech Deep Dives: AI, Data & Digital Claims

    Sessions throughout the week made it clear that AI is at the forefront of virtually every area of insurance operations. Whether it was applied in predictive underwriting, fraud detection, or personalised customer engagement, companies are looking to AI not just for marginal gains but foundational transformation.

    A standout workshop on AI in Claims Automation included live demos from startups using computer vision and NLP to automate damage assessment. Meanwhile, a session on Data-Driven Underwriting shared how insurers are replacing traditional risk proxies with real-time data streams, from wearables to smart meters.

    Cybersecurity was another hot topic, with insurers discussing how to build resilient cyber products in the face of increasing digital threats and regulatory complexity.

    Global Meets Local: The Power of Diversity

    Although a European event at heart, the conference had a distinctly global flair. Speakers came from the U.S., Singapore, Brazil, South Africa, and the Middle East. They brought diverse perspectives on shared challenges such as climate change, digital regulation, and consumer trust.

    Simultaneously, European startups shone on stage. Companies from the UK, Nordics, DACH, and Benelux presented innovative, often niche solutions for localised market challenges—from parametric crop insurance to real-time mobility coverage.

    Trade Exhibition & Brand Visibility

    The exhibition floor was a hive of activity, featuring booths from established players like Munich Re, Swiss Re, Guidewire, Duck Creek, and Cognizant, alongside vibrant startup showcases. Product demos, swag giveaways, and live challenges kept engagement high and made it easy for brands to stand out.

    The conference proved to be a golden opportunity for brand elevation, allowing companies to position themselves as thought leaders or rising disruptors in front of an incredibly curated audience.

    InsurTech Insights Europe: The Verdict

    The closing remarks from Kristoffer Lundberg, CEO of InsurTech Insights, captured the spirit of the event:

    “It’s a privilege for us to gather together the sharpest minds in the industry to discuss the role of AI in insurance. The direction and impact of these technologies will shape the space for decades to come.”

    Indeed, InsurTech Insights Europe 2025 wasn’t just a conference, it was a strategic gathering. A melting pot of ideas and a launchpad for the next generation of insurance products and platforms. Attendees walked away not just with new business cards, but with fresh ideas, collaborative leads, and the motivation to drive innovation within their own organisations.

    As the insurance industry continues to evolve amid mounting global challenges and rapidly advancing tech, this event served as a timely and energising reminder… The future is not something to wait for—it’s something to build, together.

    • Artificial Intelligence in FinTech
    • Host Perspectives
    • InsurTech