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.

该研究提出了一个适用于领域特定约束条件的框架（称为 FaSTMAN），以有效地构建顺序交易的时间图。该框架使用二阶图表示的加权方法来量化边的重要性，使我们能够在更小而密集的网络流中分配复杂查询。最后，基于这些查询，我们可以有效地识别可疑流动的网络。在对来自多个大型欧洲银行的超过 10 亿个交易数据集进行了广泛评估后，结果表明我们的框架在效率和实用性上具有明显的优势。