Leveraging AI Algorithms to Combat Financial Fraud in the United States Healthcare Sector

Abstract

Financial fraud is a major problem in the healthcare industry because it causes large financial losses and compromises the integrity and trust of healthcare systems. The intricacy and sophistication of contemporary fraudulent operations make conventional fraud detection techniques which rely on manual audits and rule-based systems increasingly inadequate. AI algorithms have become a viable way to improve financial fraud detection and prevention. Hence, this paper examines how AI algorithms can be used to detect and stop fraud in the healthcare industry, emphasizing how these algorithms could revolutionize fraud control procedures. This study suggests that AI algorithms greatly improve the identification of financial fraud in the healthcare industry by spotting intricate patterns and abnormalities frequently overlooked by already existing techniques. Machine learning models have proven to be highly accurate in predicting fraudulent claims and transactions. However, while AI provides numerous opportunities to improve fraud detection skills, its effective application necessitates resolving important issues, including ethical considerations, data governance, and model interpretability.