A Case Study on Enhancing Inquiry Response in a Non-Life Insurance Company Using Generative AI.

Abstract

In Japan, non-life insurance companies deliver products through agencies. Major insurance companies provide support through phone calls, emails, etc., at locations nationwide to ensure that their tens of thousands of agents can accurately handle customers, taking into account the characteristics and underwriting rules of a wide variety of insurance products. The documents to be referred to cover a vast amount of complex rules, and as financial products, precise and courteous responses are always needed according to individual cases. In this study, we developed and operated an inquiry response support system using the RAG architecture of LLMs with the aim of improving the inquiry response operations of casualty insurance companies. In addition, we conducted evaluation experiments on the optimal combinations of conditions related to response performance, such as the chunk division units of the target manuals for searching and the number of tokens input into the LLM.