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• The ARQuest framework uses Large Language Models (LLMs) and alternative data sources to create personalized and adaptive questionnaires for insurance underwriting.method 95%
• Techniques such as social media image analysis, geographic data categorization, and Retrieval Augmented Generation (RAG) are used to extract user insights and guide follow-up questions.technical 90%
• Traditional questionnaires yielded slightly higher accuracy in risk assessment than the adaptive versions powered by GPT models in the tested experiments.result 85%
• Adaptive questionnaires powered by GPT models required fewer questions than traditional questionnaires.result 85%
• Users preferred the adaptive questionnaires for their more fluid and engaging experience compared to traditional ones.result 85%
• The ARQuest framework has the potential to improve user satisfaction and streamline insurance processes.market 80%
• With further development, the adaptive approach may exceed traditional methods regarding risk accuracy.limitation 70%
• Standardized insurance questionnaires struggle to capture individual differences, and insurers must blindly trust user responses, increasing fraud risk.limitation 90%

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