AI-Based Intelligent Insurance Agent Using Retrieval-Augmented Generation

Abstract

The insurance industry faces significant challenges in providing efficient, accurate, and personalized customer support due to the complexity of policies and reliance on human agents. This research presents an AI-Based Intelligent Insurance Agent that utilizes Retrieval-Augmented Generation (RAG) to deliver context-aware and reliable responses. The system integrates natural language processing, machine learning, and database-driven retrieval to assist users in understanding insurance policies, recommending suitable plans, and supporting claim-related queries. It offers both text and voice interaction, ensuring 24/7 accessibility and improved user engagement. The methodology involves retrieving relevant data from a structured database and generating responses using an AI model. The proposed system reduces operational costs, enhances accuracy, and improves customer satisfaction. The results demonstrate that the system provides faster and more consistent support compared to traditional methods. This research highlights the potential of AI in transforming the insurance sector through automation and intelligent decision-making.