Personalized Health Insurance Recommendation with Retrieval-Augmented Generation: A Study of Lexical, Dense, and Hybrid Retrieval

Health insurance information in Thailand is available on many company websites and is presented in different formats, making it difficult for users to compare coverage, conditions, and benefits. In this study, we propose a personalized health insurance recommendation system using Retrieval-Augmented Generation (RAG). The system retrieves information using three methods: BM25, dense retrieval, and hybrid retrieval with Reciprocal Rank Fusion. The language model analyzes the retrieved insurance plans and recommends suitable options based on the user query. Experimental results show that dense retrieval provides the best overall performance, while hybrid retrieval performs better than lexical search. The proposed RAG system also maintains practical response latency, making it suitable for interactive applications.