How insurers are using GenAI Insurers are currently in the understanding and experimentation phase of GenAI use. Two of their biggest concerns are data security and hallucination (a tendency to create outputs that seem correct when compared to factual data but are simply made up). While open-source LLMs can drastically reduce data security risks, GenAI’s ability to confidently provide incorrect or biased outputs must be mitigated through validation before this data is used to take informed decisions. Even so, in its current form, GenAI currently works well as a knowledge assistant to insurance professionals across the value chain and can help to improve efficiencies internally (i.e., in low-risk, high-impact use cases). For instance, Zurich is currently conducting experiments involving applications that aim to extract data from claims descriptions and various other documents. To enhance its underwriting process, the company is inputting the most recent six years' worth of claims data. In another example from RGA Data Science, VP Jeff Heaton has said, “RGA is currently in the early stages of evaluating ChatGPT primarily to assist insurance professionals with routine tasks.”
Although insurance providers might not experience an instantaneous impact on their financial performance or customer interaction, a phased value measurement framework assists us in charting the path toward profitable expansion.
The framework below (Fig. 1) is helpful for measuring value in implementing GenAI within the insurance and helps us evaluate the advantages it delivers to customers and the business alike.
© 2023 Fractal Analytics Inc. All rights reserved
02
Made with FlippingBook - PDF hosting