Applying Machine Learning Fraud Detection to Healthcare Payment Systems: An Adaptation Study

Healthcare payment fraud represents a significant financial burden on healthcare systems worldwide, with estimated losses reaching billions annually. While machine learning techniques have shown remarkable success in detecting fraud in financial and credit card transactions, their application to healthcare payment systems presents unique challenges due to the complexity of medical billing, diverse stakeholder involvement, and regulatory requirements. This study presents a comprehensive adaptation framework for applying machine learning fraud detection techniques to healthcare payment systems. We systematically analyze existing fraud detection methodologies from financial domains and propose adaptations specific to healthcare contexts, including ensemble learning approaches, neural network architectures, and hybrid models. Our framework addresses key challenges such as class imbalance, temporal patterns in healthcare fraud, and the need for interpretable models in regulated environments. Through extensive analysis of healthcare fraud patterns and comparison with established financial fraud detection techniques, we demonstrate the potential for significant improvements in fraud detection accuracy while maintaining compliance with healthcare regulations. The proposed framework achieves promising results in identifying various types of healthcare payment fraud including billing irregularities, phantom services, and provider collusion schemes.