Graph-Based Social Network Analysis for Uncovering Fraudulent Patterns in Health Insurance

This article introduces a novel strategy for addressing health insurance fraud through the implementation of Graph-Based Social Network Analysis (SNA). By capitalizing on the interconnectedness of coverage holders, healthcare practitioners, and relevant entities, our approach generates an all-encompassing graph structure that encapsulates the complex network dynamics inherent in the health insurance ecosystem. An anomaly detection algorithm and the meticulous construction of node and edge attributes to identify minor patterns are employed to detect fraudulent activity. The outcomes illustrate the effectiveness of the suggested methodology and establish its superiority in comparison to the available alternatives. The contributions of the study to approaches for detecting health insurance fraud enhance the cost-effectiveness and integrity of the system. In light of the persistent gravity of health insurance fraud, this study offers practitioners and regulators in the industry a comprehensive and pertinent response.