Optimizing efficiency and accuracy in medicare and medicaid fraud detection through artificial intelligence and machine learning

Abstract

Healthcare fraudulent activities encompassing errors, abuse, and waste constitute a significant challenge to healthcare systems and insurance organizations. Healthcare fraud in the US is estimated to be up to 10% of all healthcare expenditures, translating to a loss of over $100 billion annually by the General Accounting Office. Medicare and Medicaid-related fraud is a significant source behind the financial losses in the US healthcare system as it inflates healthcare costs, misuses taxpayers' money, and places patients at risk of needless and unsafe treatments or potential identity theft. The traditional approaches to detecting Medicare and Medicaid fraud are frequently inefficient and labor-intensive, highlighting the need for more effective strategies to improve this pressing issue.This thesis focused on the relatively underexplored intersection of Artificial Intelligence and Machine Learning in healthcare fraud detection, specifically in Medicare and Medicaid fraud. The existing research often overlooks a focused exploration of these technologies in enhancing operational efficiency, accuracy, and economic value, specifically in fraud detection. However, it is crucial to recognize that detection is only a part of the broader challenge of stopping fraud, which involves complex processes such as prosecuting fraudsters, recovering lost funds, and changing provider behaviors. The aim is to understand how these technologies can improve the efficiency and accuracy of healthcare fraud detection. Also, this thesis will concentrate on how AI and ML can improve fraud detection efficiency and create economic value.--Author's abstract

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