Generative artificial intelligence is on track to be the defining advancement of the decade. Since the launch of generative AI-enabled chatbots and image generators at the tail end of 2022, the technology has dominated the conversation.
Provoking both excitement and fervent criticism, generative AI’s potential to disrupt and transform the economic landscape cannot be understated. As a result, investment into the technology increased fivefold in 2023, with generative AI startups attracting $21.8 billion of investment.
However, despite attracting considerable financial capital backing, it’s still not entirely clear what the concrete business use cases for generative AI actually are. One sector where generative AI may be able to deliver significant benefits is insurance, where we’ve identified the following applications for the technology.
1. Personalised policies and products
Large language models (LLMs) like ChatGPT are very good at using patterns in large datasets to generate specific results quickly.
The technology (when given the right data) has a great deal of potential for writing personalised insurance products and policies tailored to individual customers. AI could customise the price, coverage options, and terms of policies based on customer traits and previous successful (and unsuccessful) interactions between the insurer and previous clients. For example, generative AI could weigh up a customer’s accident history and vehicle details in order to create a customised car insurance policy.
2. Anomaly detection and fraud prevention
Generative AI is also very good at combing through large amounts of unstructured data for things that don’t look right. Anomalies and irregularities in customer behaviour like claims processing can be an early warning for wider trends in population health and safety.
It can also be a key indicator of fraud. When trained on patterns that indicate fraudulent behaviour or other types of suspicious activity, generative AI can be a valuable tool in the hands of insurance threat management teams.
3. Customer experience enrichment
Increasingly, companies offering similar services are turning to customer experience as a key differentiator between them and their competitors. A growing part of the CX journey in recent years has been personalisation and organisations working to provide a more individualised service.
Generative AI has the potential to support activities like customer segmentation, behavioural analysis, and creating more unique customer experiences.
It can also generate synthetic customer models (fake people, essentially) to train AI and human workers on activities like segmentation and behavioural predictions.
Lastly, generative AI is already seeing widespread adoption as a first-touch customer relationship management tool. Several organisations, having implemented a customer service chatbot, found users preferred talking to an AI when it came to answering simple queries, allowing human agents more time to handle more complex requests further up the chain.
4. Regulatory compliance
In an industry as heavily regulated as insurance, generative AI has the potential to be a useful tool for insurers. The technology could streamline the process of navigating an ever-changing compliance landscape by automating compliance checks.
Generative AI has the potential to automate the validation and updating of policies in response to evolving regulatory changes. This would not only reduce the risk of a breach in compliance, but alleviates the manual workload placed on regulatory teams.
5. Content summary, synthesis, and creation
Large amounts of insurers’ time is taken up by intaking large amounts of information from an array of unstructured sources. Sometimes, this information is poorly managed and disorganised when it reaches the insurer, consuming valuable time and potentially leading to errors or subpar decision making.
Generative AI’s ability to scan and summarise large amounts of information could make it very good at summarising policies, documents, and other large, unstructured content. It could then synthesise effective summaries to reduce insurer workload, even answering questions about the contents of the documents in natural language.
- Data & AI
- Fintech & Insurtech
