Agentic AI for Healthcare Fraud Detection and Claims Verification

Abstract: Due to the reasons like insurance frauds, duplicate billing, identity theft, and professional misconduct, fraud in the healthcare sector leads to billions of losses. Fraud detection mechanisms that use static parameters cannot detect the highly advanced fraud patterns. Due to the inclusion of the autonomous reasoning, constant surveillance, adaptation, and collaborative decisions, Agentic Artificial Intelligence increases the fraud detection process and claims verification. In this research work, we provide an Agentic AI framework which includes intelligent agents related to policy compliance, provider verification, anomaly detection, claims verification, and automated decision-making. Several AI agents can cooperate with each other in order to detect the suspicious health care transaction process without generating too many false positives through machine learning, knowledge graph, explainable AI, and reinforcement learning. The proposed approach will be beneficial in increasing the performance and fraud prevention in the healthcare ecosystems. Simulation results show that the proposed framework outperforms existing fraud detection systems in terms of accuracy, processing time, and claim verification process. The proposed technique will be helpful in designing future intelligent healthcare infrastructures with safe and reliable health insurance management.

Keywords: Agentic AI, Healthcare Fraud Detection, Claims Verification, Medical Insurance, Explainable AI, Multi- Agent Systems, Machine Learning, Healthcare Analytics.