AI-Driven Fraud Detection in Healthcare Payments: Reducing Financial Risks in Claims and Billing

Abstract

Comprising billions of yearly expenses and heavily stressing healthcare systems financially, healthcare fraud is a major issue. Apart from increasing expenses, false claims, billing mistakes, and identity theft compromise the quality of therapy received. Dependent on human audit & the rule-based systems, traditional fraud detection techniques sometimes finds it difficult to adapt with the times to meet the advancing strategies of the fraudsters. Artificial intelligence (AI) & the machine learning (ML) greatly change the recognition & avoidance of the healthcare fraud. AI-powered fraud detection solutions using big datasets highlight trends and anomalies human auditors would overlook. All, supervised and unsupervised algorithms, predictive analytics, natural language processing, and machine learning models provide very accurate real-time fraud detection. Methods including network analysis, deep learning, and anomaly detection help to uncover dubious claims and raise attention to variances before financial damages are compensated for. Recent developments indicate that AI-powered systems could greatly reduce false positives while simultaneously increasing fraud detection rates, therefore saving billions of unlawful payments. By means of AI-driven fraud prevention, not only accomplishes financial resource protection but also enhances confidence of healthcare systems. Still, long-term success challenges including data privacy concerns, model interpretability, and the need of constant model revisions demand attention. The results show that artificial intelligence changes payment security for healthcare not only as a tool but also as a transforming agent. Since its versatility ensures that detection systems grow proportionately as fraudsters create ever more sophisticated strategies, artificial intelligence is an essential tool in avoiding healthcare fraud.

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