AI- and Data Science–Driven Healthcare Revenue Optimization and Payment Integrity Modeling in U.S. Public and Private Insurance Systems

Abstract

The increasing complexity of healthcare reimbursement systems in the United States has intensified financial inefficiencies, claim denials, fraud exposure, and payment inaccuracies across both public and private insurance markets. This study examines the transformative role of artificial intelligence (AI) in healthcare revenue optimization and payment integrity within Medicare, Medicaid, and commercial insurance systems. By integrating machine learning algorithms, predictive analytics, natural language processing (NLP), and anomaly detection frameworks, AI-enabled systems can proactively identify billing irregularities, reduce improper payments, optimize reimbursement cycles, and enhance regulatory compliance. The research proposes a multi-layered AI framework combining fraud detection models, claims prediction systems, automated coding validation, and risk-adjusted payment analytics to strengthen financial sustainability across healthcare institutions and insurance providers. The study further evaluates the economic impact of AI adoption on revenue cycle management (RCM), cost containment, and administrative burden reduction. Additionally, ethical considerations, data governance challenges, and explainability requirements are examined to ensure transparency and compliance with federal healthcare regulations. Findings suggest that AI-driven payment integrity systems can significantly reduce fraud, waste, and abuse (FWA), improve claim acceptance rates, and enhance long-term fiscal stability in both public and private healthcare insurance programs. The paper concludes by outlining policy recommendations and implementation strategies to support scalable, secure, and equitable AI integration in U.S. healthcare financial ecosystems.

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