The financial services industry is highly technology-driven and organisations around the world are scrambling to take advantage of developments in Artificial Intelligence (AI) to enhance the customer journey. By harnessing AI, firms gain the ability to provide personalised services that ensure each customer’s journey is uniquely tailored to their individual preferences.
We live in a consumer-centric world, which means most innovations are led by the customer’s demands, and that is apparent across many different sectors. The shift towards a more personalised journey not only enhances stakeholders’ trust in organisations in the financial services space, but it also positions companies that are doing this well at the forefront of innovation in an extremely competitive and fast-moving sector.
For banks, there are three key business areas in which AI technology is being particularly explored, utilised and integrated to take advantage of the efficiencies it can bring. Customer operations are the first, especially Know Your Customer (KYC) and Customer Due Diligence (CDD) processes. Marketing and sales departments use AI to drive customer engagement through hyper-personalised, data-driven campaigns that adapt in real-time based on customer interactions. Moreover, AI can analyse customer sentiment through natural language processing (NLP) of customer feedback, social media, and service interactions. This allows financial institutions to identify at-risk customers and implement targeted retention strategies. Meanwhile, software engineers are using AI to speed up and streamline the construction and integration of complex IT frameworks and tools, increasing the perceived business value.
How will AI support this transition?
AI has opened the door to a wealth of customer data that helps financial institutions shape the journey. The analytical capabilities are dramatically enhanced, which allows assessment of much greater volumes of data on customer behaviour trends, such as financial history, spending patterns, and real-time transactions. For example, if a customer is approaching retirement age, their banking app might proactively offer retirement planning services. Similarly, AI can identify customers at risk of financial distress and provide them with personalised financial management advice, thereby preventing potential issues before they arise.
Most banks we work with use simple forms of AI, such as chatbots, and approximately 70 per cent use an advanced form of AI. This number skyrockets in the fintech market, with these organisations using AI wherever possible as they operate in a less regulated environment. However, they will be subject to compliance with the EU AI Act moving forward. Some financial institutions invest in more elaborate AI assistants tailored to specific corporate knowledge and documents including policies, offerings and terms and conditions. Tapping into the benefits of automated machine learning means organisations can continuously improve their responses to customer enquiries and tailor interactions for an immediate, more convenient service.
When it comes to compliance and protecting customers from fraud, AI can enhance KYC processes by providing advanced identity authentication and anomaly detection, ensuring robust compliance and heightened security. AI and ML technologies can help detect fraud patterns or suspicious activities in risk profiles, automatically flagging high-risk profiles for enhanced due diligence and pre-emptive measures.
What is the advice for financial services firms?
When it comes to building AI functions, the safest route is to prioritise consistency. Many firms are creating the same architecture in an agile environment across multiple departments to ensure security and data governance are at the core of all applications.
Regarding data, banks need to develop sustainable data engineering capabilities. Also, there needs to be a key focus on compliance and governance to ensure no privacy concerns. For example, banks are exploring the use of AI within facial recognition systems specifically to enhance security measures and improve customer authentication processes. To make facial recognition AI successful, there needs to be a comprehensive audit trail of all facial recognition attempts, including timestamps, user identifiers, and the outcome of each authentication attempt. Logging this information ensures transparency, accountability, and compliance with data protection regulations.
In addition, as financial institutions increasingly rely on AI, it is crucial to address ethical considerations such as algorithmic bias and transparency. Implementing fairness and accountability measures in AI systems helps maintain customer trust and regulatory compliance.
Businesses in the financial services space have the opportunity to gain a competitive edge through AI and the opportunities are boundless. Still, they must also remain aware and in control of the potential risks of leveraging much greater volumes of personal data and increased data sharing.
- Fintech & Insurtech
