ADVANCING FINANCIAL INTEGRITY IN U.S. HEALTHCARE THROUGH MACHINE LEARNING AND DATA ANALYTICS FOR FRAUD DETECTION

Abstract

The U.S. government has long recognized healthcare fraud as a serious concern and has introduced various measures over several decades to prevent and control fraudulent practices. Despite these efforts, healthcare fraud has become increasingly sophisticated and complex, continuing to create substantial financial pressures on the healthcare system and the wider economy. Recent developments in artificial intelligence (AI), particularly machine learning (ML), natural language processing (NLP), and neural networks, have strengthened the ability to analyze large-scale healthcare datasets and identify concealed patterns, anomalies, and potentially fraudulent activities. Collectively, the implementation of these AI-driven approaches offers considerable potential to safeguard public healthcare resources, reduce financial losses, and strengthen public confidence in the integrity of the U.S. healthcare system.