Machine Learning-Driven Healthcare Fraud Detection: A Comprehensive Analysis of FAMS Implementation and Outcomes

Abstract

This article examines the implementation and effectiveness of the Fraud and Abuse Management System (FAMS) in healthcare claims processing, addressing the critical challenge of fraudulent claims in the healthcare industry. The article presents a comprehensive analysis of FAMS, a machine learning-driven solution designed to detect and prevent healthcare fraud through automated pattern recognition and predictive modeling. Through systematic evaluation of implementation data across multiple healthcare organizations, this article demonstrates FAMS's capability in identifying various fraud types, including medical identity theft, upcoding, and unauthorized billing. The findings indicate significant improvements in fraud detection accuracy and reduction in false positives compared to traditional methods, while simultaneously decreasing the manual review workload. The article also reveals key implementation challenges and provides strategic recommendations for healthcare organizations considering FAMS adoption. This article contributes to the growing body of literature on automated healthcare fraud detection and offers practical insights for healthcare administrators and policymakers in their efforts to combat fraudulent claims processing.