AI-Based Predictive Analytics for Identifying Fraudulent Health Insurance Claims

Abstract

An increasing problem, health insurance fraud causes significant financial losses for policyholders & also increases premiums for insurance companies. Often focused on human supervision & rule-based, conventional fraud detection methods find it difficult to change with the evolving methods of fraudsters. By use of ML, deep learning & data-informed insights, AI- driven predictive analytics offers a novel approach to detect their fraudulent claims with increased accuracy & their efficiency. By means of huge scale data analysis, trend recognition & actual time flagging of anomalies, AI may significantly lower faulty positives & improve fraud detection efficiency. This work investigates many approaches including NLP for claim analysis, anomaly detection methods & supervised and unsupervised learning models. Using historical claims data, a case study shows how better AI-driven models are at spotting dishonest behavior than more conventional methods. The findings show that AI increases detection accuracy & simplifies the claims validation procedure, thereby reducing running expenses & supporting equity for rightful policyholders. Still, for general use problems like data privacy, model interpretability & their potential biases must be addressed. The development of AI might change fraud detection systems in the insurance & healthcare industries, therefore allowing proactive fraud avoidance & maintaining the integrity of legitimate claims