Machine Learning for Medical Billing Fraud and Insurance Risk Detection: Trends and Challenges in the US Healthcare System

Abstract

The monetary costs of healthcare fraud in the United States surpass billions of dollars annually, and to address and prevent fraud and insurance risk loss, effective data-driven methods of fraud detection and risk mitigation are required. The paper examines how Machine Learning (ML) and Artificial Intelligence (AI) hold the potential to transform current challenges. Marketable fraudulent billing practices and using ML models to improve premium risk scoring through the discovery of non-intuitive patterns in large-scale healthcare data is achievable by the means of employing supervised, unsupervised, and reinforcement learning. Recent developments are discussed and compared in various detection frameworks, and specifically, the involvement in the new technologies, Blockchain to secure data sharing and biometrics to identify. The researchers stress that scalable and explainable ML models that are consistent with the changing trends of frauds and healthcare demands are vital. In addition, it recommends cooperation between technology developers, insurers, and policymakers in order to gain ethical use of AI and adherence to data privacy policies. The results demonstrate the importance of AI as a way of improving the efficiency of the operations, financial stability, and individual care in terms of service delivery to healthcare insurance systems.