Breaking the Chain: SNA-Based Resilience Analysis of Synthetic Financial Transaction Networks for Anti-Money Laundering

Money laundering remains a critical challenge for financial systems because of the complex, hidden, and interlinked nature of illicit financial transaction networks. Understanding how these networks respond to targeted disruption is essential for exposing structural vulnerabilities and refining existing anti-money laundering (AML) prevention and intervention strategies. This study involves a social network analysis (SNA)-based resilience framework to evaluate the robustness of financial transaction networks through targeted node removal. In this approach, a network is represented as a directed graph, where nodes correspond to accounts and edges represent transactions. Centrality measures (i.e., degree, closeness, betweenness and pagerank), which capture local influence, global reach, and control over information flow, are applied to identify the most influential nodes. Network resilience is assessed by analyzing the variation in the size of the Largest Connected Component (LCC) under progressive node removal. An adaptive LCC-based resilience strategy is used, starting with large batches of nodes and gradually moving to smaller ones until the LCC drops below 50% of its original size, allowing for a more detailed analysis near the fragmentation threshold. The findings reveal that Betweenness centrality is the most effective metric in disrupting network connectivity under targeted attack scenarios, both outflow- and inflow-based analyses. Specifically, targeting only the top 2% of nodes by Betweenness centrality collapses the network’s core, reducing the Largest Connected Component (LCC) to 60% of its original size. In contrast, random attack strategy exhibit limited impact on overall network resilience compared to targeted approaches. Our findings provide actionable AML insights, showing that resilience-driven targeting of structurally critical accounts can effectively fragment money laundering networks and support more focused interdiction strategies.