AI in Healthcare Fraud Detection: Ensuring Integrity in Medical Billing

Abstract

Healthcare fraud poses a significant challenge, leading to financial losses and inefficiencies. AI offers innovative solutions by identifying anomalies in medical billing, insurance claims, and provider behavior. This paper explores the application of machine learning models in detecting fraudulent activities, such as upcoding, phantom billing, and duplicate claims. It also discusses the importance of maintaining patient privacy, ensuring algorithm transparency, and balancing automation with human oversight in fraud detection systems.