Optimize Fraud Detection in Health Insurance Claims by Integrating Graph Analytics and Machine Learning Models

Healthcare fraud involves submitting false claims or misrepresenting facts to obtain improper payments. Fraud in health insurance claims causes billions of dollars in annual losses. Advanced machine learning algorithms can efficiently extract critical features from data, recognize common patterns, and generate highly accurate predictions when adequately configured and trained. However, detecting fraud in healthcare is challenging as it sometimes involves coordinated actions among affiliated providers, physicians, and beneficiaries to submit fraudulent claims. This paper uses graph analytics and machine learning techniques to detect fraudulent claims accurately. The approach represents the data in its graphical form, computes network features, and uses this enriched information to inform the machine learning algorithm. This research aims to comprehensively analyze how integrating graph-based and machine-learning methods can optimize fraud detection in the health insurance claims process by offering more precise and scalable solutions while acknowledging the need for ongoing refinement.