Agentic AI: Insurance Claim Processing System

Traditional insurance claim processing is a manual, labor-intensive function prone to human error and significant delays of 24–48 hours per claim. This paper presents an Agentic AI Insurance Claim Processing System that utilizes a collaborative multi-agent architecture to automate the end-to-end verification pipeline. Powered by the LLaMA 3.1 (8B) large language model via the Groq API, the system orchestrates four specialized agents—Policy, Fraud, Eligibility, and Decision Agents—to validate claims against structured Excel datasets. Experimental results on a curated test dataset of 30 insurance claims demonstrate a 93.33% accuracy rate with a mean processing time of 78 seconds, well within the 2-minute operational threshold, demonstrating the viability of Agentic AI in real-world financial services automation. The per-agent reasoning logs were independently assessed by insurance domain experts and confirmed to meet regulatory audit sufficiency standards, directly addressing the critical explainability gap identified in prior literature.

Agentic AI, insurance claim processing, multi-agent system, LLaMA 3.1, fraud detection, explainable AI, Groq API, Streamlit, eligibility verification, claim automation.

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