Harnessing Artificial Intelligence to Combat Fraud, Waste, and Abuse in Healthcare

Suresh Babu Basanaboyina

Abstract: Fraud, Waste, and Abuse (FWA) present critical challenges across industries, leading to significant financial and reputational damage. Artificial Intelligence (AI) offers a sophisticated approach to mitigating these issues by enhancing the detection and prevention of FWA. Using machine learning algorithms, AI can process and analyze large volumes of data to uncover hidden patterns and detect irregularities indicative of FWA activities. These systems improve over time, adapting to new fraud tactics and reducing false positive rates. AI enables real-time monitoring and automated anomaly detection, providing organizations with immediate alerts on suspicious activities. With the integration of Natural Language Processing (NLP), AI can also scrutinize unstructured data sources, such as emails and documents, to identify fraudulent communications. Combining AI with existing FWA management systems enhances their efficacy, offering a comprehensive defense strategy that incorporates both technology and human insight. The implementation of AI-driven solutions results in more efficient resource utilization, better compliance with regulatory standards, and a stronger overall defense against FWA. By embracing AI, organizations can significantly reduce the incidence of FWA, leading to increased transparency, accountability, and operational integrity. Healthcare Fraud, Waste, and Abuse (FWA) costs are out of control. The National Health Care Anti-Fraud Association estimates that healthcare fraud costs the US approximately $68 billion each year. Further, the Centers for Medicare and Medicaid Services (CMS) reported that improper payments made by Medicare and Medicaid accounted for $31.46 billion in 2022. Other payers and industry organizations estimate the healthcare FWA cost to be more than $200 billion per year.

Keywords: Health care Fraud Detection, Waste and Abuse Prevention, AI in Fraud Prevention, Machine Learning for FWA, Anomaly Detection, Real-Time Monitoring, NLP in Fraud Detection, Healthcare Fraud Costs, Enhanced FWA, Management, Data Analysis in FWA