AI-powered life insurance claims adjudication using LLMs and RAG Architectures

The accurate assessment and adjudication of life insurance claims is a core competency of the insurance industry and the business of insurance, affecting financial stability and operational efficiency of the company and the relationship with the customer. The traditional methods of claims processing that heavily rely on rule-based systems and manual assessment have problems of inefficiencies, errors, and long time to resolution. However, as Artificial Intelligence (AI), Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) architectures are becoming capable, they open the opportunity to revolutionize again claims adjudication, improving the accuracy, automating and also detecting fraud. Laying out the use of LLMs and RAG architectures in life insurance claims management, this paper illustrates the ability of these two architectures to automate decision-making, boost risk assessment, but also to optimize the operational workflow. Also, calls attention to the use of predictive modeling in addition to hosting robotic process automation (RPA) in the development of AI driven claims processing.