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Money launderers exploit the weaknesses in detection systems by purposefullyplacing their ill-gotten money into multiple accounts, at different banks. Thatmoney is then layered and moved around among mule accounts to obscure theorigin and the flow of transactions. Consequently, the money is integrated intothe financial system without raising suspicion. Path finding algorithms thataim at tracking suspicious flows of money usually struggle with scale andcomplexity. Existing community detection techniques also fail to properlycapture the time-dependent relationships. This is particularly evident whenperforming analytics over massive transaction graphs. We propose a framework(called FaSTMAN), adapted for domain-specific constraints, to efficientlyconstruct a temporal graph of sequential transactions. The framework includes aweighting method, using 2nd order graph representation, to quantify thesignificance of the edges. This method enables us to distribute complex querieson smaller and densely connected networks of flows. Finally, based on thosequeries, we can effectively identify networks of suspicious flows. Weextensively evaluate the scalability and the effectiveness of our frameworkagainst two state-of-the-art solutions for detecting suspicious flows oftransactions. For a dataset of over 1 Billion transactions from multiple largeEuropean banks, the results show a clear superiority of our framework both inefficiency and usefulness.

Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions