Leveraging Graph Databases for Fraud Detection in Medical Insurance

Abstract

The healthcare sector, particularly medical insurance, is one of the largest and most essential industries globally. However, it also faces significant challenges related to fraud, which not only jeopardizes financial resources but also undermines trust in the system. This paper explores the potential of graph databases in detecting fraud within the medical insurance domain. By leveraging the interconnected nature of medical data and utilizing the advanced capabilities of graph databases, insurers can uncover complex patterns and anomalies indicative of fraudulent activity. This research discusses the application of graph databases, their advantages over traditional relational databases, and presents a case study demonstrating their effectiveness in fraud detection.