Fraud Detection and Prevention in Healthcare Insurance Claims Using Machine Learning Regression Models

Health insurance fraud is a serious problem that upsets much of the financial stability among insurance companies and policyholders in general. It's estimated that healthcare fraud costs the U.S. health care system around $68 billion each year, as it drives up premiums and limits access to care for legitimate patients in search of care. Traditional methods such as manual review and also rule-based systems have shown to be very weak in regard to the sophisticated natures of illicit schemes that have dynamic characteristics. This research paper discusses how machine learning regression models aid in fraud detection and prevention applications in healthcare insurance claims. By minimizing advanced use in real-time data processing and advanced analytics, patterns of behavior that reflect fraud are recognizably detected, thus providing more accuracy in fraud detection. The paper goes further to cover the theoretical framework of machine learning in fraud detection, which includes data preprocessing, engineering the features, and evaluating the models. It emphasizes the merging of historical claims data with external sources to build a holistic dataset that will train a more powerful machine learning model. It discusses the need for continuous adaptability of models due to fraud changes over time. Besides, the paper gives some industry statistics that help illustrate healthcare fraud's financial effects and its incremental benefits from using machine learning technology. The research contributes to the continuing conversation regarding innovative solutions to fraud detection in health care, creating pathways for future research and development in this important domain.