Self-Refining Language Models to Assess Insurance Claims

This research investigates the application of Large Language Models (LLMs) for automating the assessment of bicycle insurance claims. The study aims to determine the effectiveness of LLMs in interpreting insurance policy documents and evaluating the validity of claims. The ultimate objective being to enhance consistency and efficiency in claims processing by leveraging LLM capabilities to understand complex legal language. The methodology involves training and testing various models on a bicycle insurance policy and hypothetical claims against the policy. Key achievements include the introduction of an agent-based approach that significantly improves the acceptability of written claims handler responses. The findings indicate that, by using a combination of agents with crafted prompts, LLMs can produce acceptable claims handler responses to claim descriptions.