Real-Time Fraud Detection in Health Insurance Using AI: Opportunities and Challenges

Abstract: Health insurance fraud has been one of the biggest financial and operational headaches in recent years, running into billions annually, which creates upward pressure on the premium paid by the policyholder. Artificial intelligence and machine learning can create new avenues for combating fraud by employing real-time detection systems to identify and react to suspicious claims with unprecedented accuracy and speed. This paper explores the opportunities AI presents in transforming fraud detection within health insurance, focusing on both technical advancements and potential roadblocks. Real-time AI systems bring opportunities for automated and continuous monitoring, allowing insurers to assess fraud risk more efficiently and enabling proactive fraud prevention measures that ultimately reduce operational costs. An insurer requires sophisticated computing architecture, rapid processing capabilities of data, and powerful integration frameworks of data for the effective application of such systems. There are many computational challenges where high-speed processing is crucial, along with efficient handling of data and not losing model transparency. Moreover, compliance to HIPAA makes insurers undertake strict security measures for preventing unauthorized disclosure of health data. Findings suggest that real-time AI fraud detection could facilitate the prevention of fraud while accelerating the process of examining claims and significantly reducing costs. In fact, ongoing challenges that include regulatory compliance, computations, and keeping pace with fraud tactics in evolution argue for a balanced approach for the deployment of AI by health insurance.

Keywords: Fraud-detection, health insurance, AI, compliance, data-driven decision making