Fraud and Social Network Analysis

Abdelrahim Al Aqqad ()

Chapter Chapter 17 in Fraud Analytics in Action, 2026, pp 421-441 from Springer

Abstract: Abstract Chapter 17 examines the intersection of fraud detection and social network analysis (SNA), establishing that fraud is not merely an individual act but a fundamentally social phenomenon that propagates through networks in structured, detectable ways. The chapter introduces homophily—the tendency of similar individuals to associate—as a core principle for identifying fraud clusters, and demonstrates how ego networks can dramatically reduce investigative complexity by focusing attention on high-probability suspects. Two key network metrics are introduced and worked through with numerical examples: dyadicity, which measures the degree to which same-labeled nodes cluster together, and heterophilicity, which evaluates cross-group connectivity. Both metrics are supported by Python code using the NetworkX library. The chapter then applies these concepts to real-world fraud scenarios, including identity theft detection in telecom networks, where behavioral shifts in call patterns and degree-centrality metrics serve as early warning signals. Finally, the chapter explores graph algorithms—particularly breadth-first search—as tools for tracing money flows and uncovering hidden relationships in complex financial networks. Together, these techniques provide a robust, layered framework for fraud detection that complements traditional machine learning approaches.

Date: 2026
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DOI: 10.1007/978-3-032-16023-2_17

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