Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions
This addresses the challenge of scalable and effective detection of temporal money laundering flows for financial institutions, representing a domain-specific incremental improvement.
The paper tackles the problem of detecting money laundering flows in large-scale transaction graphs by proposing the FaSTMAN framework, which efficiently constructs temporal graphs and uses a weighting method to identify suspicious networks, showing clear superiority in efficiency and usefulness on a dataset of over 1 billion transactions.
Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integrated into the financial system without raising suspicion. Path finding algorithms that aim at tracking suspicious flows of money usually struggle with scale and complexity. Existing community detection techniques also fail to properly capture the time-dependent relationships. This is particularly evident when performing analytics over massive transaction graphs. We propose a framework (called FaSTMAN), adapted for domain-specific constraints, to efficiently construct a temporal graph of sequential transactions. The framework includes a weighting method, using 2nd order graph representation, to quantify the significance of the edges. This method enables us to distribute complex queries on smaller and densely connected networks of flows. Finally, based on those queries, we can effectively identify networks of suspicious flows. We extensively evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions. For a dataset of over 1 Billion transactions from multiple large European banks, the results show a clear superiority of our framework both in efficiency and usefulness.