Healthcare Fraud Detection Using Machine Learning Techniques

Abstract

Healthcare fraud is a significant global issue that undermines the integrity of healthcare systems and results in billions of dollars in losses annually. Fraudulent activities can take numerous forms, including billing for services not rendered, upcoding, and falsifying medical claims. Detecting such activities is critical to ensure efficient allocation of resources and to maintain trust in healthcare systems. With the advent of machine learning techniques, there has been a paradigm shift in how fraud detection systems are designed and implemented. This paper explores the application of machine learning techniques in healthcare fraud detection. We provide an overview of the problem, review existing literature, outline a proposed methodology using advanced machine learning models, and present results derived from simulated datasets. Finally, we discuss the implications of our findings and suggest avenues for future research.

DOI - https://doi.org/10.65525/SVUP.9788199963351.2026.158-164