AI-Driven Machine Learning for Fraud Detection and Risk Management in U.S. Healthcare Billing and Insurance
Raktim Dey, Ashutosh Roy, Jasmin Akter, Aashish Mishra, M. Sarkar
Abstract
Healthcare fraud in the United States results in billions of dollars in financial losses annually, necessitating advanced technological solutions for fraud detection and risk management. Machine learning (ML) has emerged as a powerful tool in identifying fraudulent claims, mitigating risks, and enhancing financial security in healthcare billing and insurance (Anderson & Kim, 2023). This study examines the application of supervised and unsupervised ML techniques, such as decision trees, neural networks, and anomaly detection models, to detect fraudulent patterns in insurance claims (Wang et al., 2022). By analyzing large-scale electronic health records (EHRs) and claims datasets, ML algorithms can identify suspicious activities and reduce false positives, improving fraud detection accuracy (Garcia & Lee, 2023). Additionally, predictive analytics aids in risk assessment, enabling insurers and healthcare providers to proactively manage financial fraud risks (Brown et al., 2023). Despite its advantages, ML-based fraud detection systems face challenges, including data privacy concerns, interpretability issues, and regulatory compliance (Nguyen & Patel, 2023). This research highlights the effectiveness of AI-driven fraud detection models in minimizing financial losses and enhancing operational efficiency in the U.S. healthcare sector, with future implications for explainable AI and privacy-preserving ML solutions.