USING GRAPH CENTRALITY METRICS FOR DETECTION OF SUSPICIOUS TRANSACTIONS
DOI:
https://doi.org/10.37943/22ZSKI6025Keywords:
social network analysis, centrality measures, financial fraud detection, betweenness centrality, anti-money laundering, transaction networks, graph-based anomaly detection, explainable AIAbstract
This study addresses the critical challenge of detecting suspicious transactions in modern financial networks, focusing on the persistent threat of money laundering and related fraudulent activities. We propose a graph-based approach where each financial participant—whether an individual or an institution—is modeled as a node, and directed edges represent the flow of transactions. Using a dataset of anonymized banking records, we construct a directed graph and then calculate centrality measures, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. These metrics quantify how actively each node participates in or controls the circulation of funds across the network. Nodes characterized by particularly high values for betweenness or degree centrality emerge as potential “bridge” entities, acting as conduits for the majority of transaction paths. Our results indicate that these high-centrality participants may be key to understanding illicit financial flows, because they facilitate significant volumes of transactions or exert disproportionate influence by connecting otherwise separate sub-networks. Furthermore, a visualization of subgraphs around these nodes reveals tightly knit structures, suggesting the presence of possible hidden clusters that could be orchestrating complex money-laundering schemes. Overall, the proposed network-driven approach provides an efficient lens for early detection of suspicious accounts and transaction routes, especially when integrated with contemporary machine learning technologies for real-time analytics. The study concludes that centrality-based screening can enhance both the speed and accuracy of anti-fraud interventions, thereby strengthening the resilience of financial institutions in an increasingly data-rich and interconnected global economy.
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