Using Complex Network Analysis Techniques to Uncover Fraudulent Activity in Connected Healthcare Systems

Abstract

Complex network analysis is a powerful approach for finding fraud in a network. This is an application of graph theory that enables the depiction of relationships between entities as nodes and edges, which is one of the important elements of complex network analysis. Additionally, key players within the network who might be engaged in fraud might be found using advanced network analysis. Complex network analysis is an effective method for spotting fraud on a network because it provides comprehensive and systematic understanding of the links and interactions inside a network. This study describes Python NetworkX to analyze connected healthcare systems, focusing on fraud detection. Leveraging community detection algorithms, the research identifies cohesive groups within the network, revealing potential fraud clusters. Centrality measures assist in pinpointing influential nodes and detecting anomalous behavior. By integrating these techniques, the study aims to enhance fraud detection capabilities in healthcare networks, contributing to improved security and integrity within the system.