INTELLIGENT FRAUD PREVENTION: IMPLEMENTING AML SOLUTIONS FOR MCCA REIMBURSEMENT CLAIMS PROTECTION

Abstract

This article examines the implementation of Advanced Machine Learning (AML) solutions for protecting MCCA reimbursement claims against fraud. The article explores the evolution of fraud detection systems within the Michigan Catastrophic Claims Association's framework, analyzing traditional detection methods' limitations and the transformative potential of machine learning approaches. The article delves into common fraud patterns, their impact on public funds, and the architectural components of modern fraud detection systems. It evaluates pattern recognition systems, risk assessment engines, and real-time detection implementations while examining the critical role of inter-agency data sharing frameworks. The article further investigates the benefits of implementation, including financial impacts, operational improvements, and deterrence effects. Additionally, the article addresses future developments in fraud detection, focusing on system scalability, enhancement opportunities, integration with emerging technologies, and regulatory alignment considerations. Through extensive analysis of current literature and industry practices, this article provides valuable insights into the effectiveness of AML solutions in combating insurance fraud while maintaining regulatory compliance and operational efficiency.