AI in Insurance: Adaptive Questionnaires for Improved Risk Profiling

TL;DR

The paper presents ARQuest, an adaptive questionnaire system using LLMs and alternative data to enhance insurance risk profiling, reducing questions and improving user experience.

Contribution

It introduces a novel framework combining LLMs, social media analysis, and RAG for personalized, adaptive insurance questionnaires.

Findings

Adaptive questionnaires required fewer questions than traditional ones.

Users preferred the adaptive questionnaires for their engaging experience.

Traditional questionnaires achieved slightly higher risk assessment accuracy.

Abstract

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.