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The paper introduces ARQuest, a framework that leverages LLMs and RAG to generate personalized and adaptive insurance questionnaires using alternative data sources like social media images and geographic data. Experiments with a life insurance mobile app showed that while traditional questionnaires had slightly higher risk assessment accuracy, the adaptive questionnaires significantly reduced the number of questions needed and improved user satisfaction. This suggests a promising path toward more efficient and user-friendly insurance underwriting.
Personalized insurance questionnaires powered by LLMs can slash the number of questions needed while boosting user satisfaction, even if current accuracy lags behind traditional methods.
Insurance application processes often rely on lengthy and standardized questionnaires that struggle to capture individual differences. Moreover, insurers must blindly trust users'responses, increasing the chances of fraud. The ARQuest framework introduces a new approach to underwriting by using Large Language Models (LLMs) and alternative data sources to create personalized and adaptive questionnaires. Techniques such as social media image analysis, geographic data categorization, and Retrieval Augmented Generation (RAG) are used to extract meaningful user insights and guide targeted follow-up questions. A life insurance system integrated into an industry partner mobile app was tested in two experiments. While traditional questionnaires yielded slightly higher accuracy in risk assessment, adaptive versions powered by GPT models required fewer questions and were preferred by users for their more fluid and engaging experience. ARQuest shows great potential to improve user satisfaction and streamline insurance processes. With further development, this approach may exceed traditional methods regarding risk accuracy and help drive innovation in the insurance industry.