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。

亿级交易中无拓扑关联的时间性洗钱流的检测

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

A challenging problem in the study of complex systems is that of resolving,
without prior information, the emergent, mesoscopic organization determined by
groups of units whose dynamical activity is more strongly correlated internally
than with the rest of the system. The existing techniques to filter
correlations are not explicitly oriented towards identifying such modules and
can suffer from an unavoidable information loss. A promising alternative is
that of employing community detection techniques developed in network theory.
Unfortunately, this approach has focused predominantly on replacing network
data with correlation matrices, a procedure that tends to be intrinsically
biased due to its inconsistency with the null hypotheses underlying the
existing algorithms. Here we introduce, via a consistent redefinition of null
models based on random matrix theory, the appropriate correlation-based
counterparts of the most popular community detection techniques. Our methods
can filter out both unit-specific noise and system-wide dependencies, and the
resulting communities are internally correlated and mutually anti-correlated.
We also implement multiresolution and multifrequency approaches revealing
hierarchically nested sub-communities with `hard' cores and `soft' peripheries.
We apply our techniques to several financial time series and identify
mesoscopic groups of stocks which are irreducible to a standard, sectorial
taxonomy, detect `soft stocks' that alternate between communities, and discuss
implications for portfolio optimization and risk management.

通过基于随机矩阵理论的一致重新定义空模型的方法，我们提出了最流行的社区检测技术的适当基于相关性的对应物。我们将技术应用于几个金融时间序列，并确定了不可简化为标准部门分类法的股票介观群体，发现了在社区之间交替的 “软股票”，并讨论了投资组合优化和风险管理的影响。