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arXiv 2201.10051cs.SI
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MonLAD：交易流中的洗钱代理检测

MonLAD: Money Laundering Agents Detection in Transaction Streams

Xiaobing Sun, Wenjie Feng, Shenghua Liu, Yuyang Xie, Siddharth Bhatia, Bryan Hooi, Wenhan Wang, Xueqi Cheng

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摘要：

鉴于银行账户之间的资金交易流，我们如何实时准确地检测洗钱代理账户和可疑行为？洗钱代理人试图通过分散多笔小额交易来隐藏非法所得资金的来源，并通过聪明的策略逃避侦查。因此，以无监督的方式准确地抓住这些欺诈者是具有挑战性的。现有的方法不考虑这些代理帐户的特性，并且不适合流媒体设置。因此，我们提出MonLAD和MonLAD-W通过跟踪其残差和其他特征来检测交易流中的洗钱代理账户;我们设计AnoScore算法来基于统计偏差的鲁棒性度量来发现异常。实验结果表明，MonLAD在真实世界数据上的表现优于最先进的基线，并发现了各种可疑的洗钱行为模式。此外，几个检测到的可疑账户已被手动验证为真实洗钱场景中的代理人。

英文摘要：

Given a stream of money transactions between accounts in a bank, how can we accurately detect money laundering agent accounts and suspected behaviors in real-time? Money laundering agents try to hide the origin of illegally obtained money by dispersive multiple small transactions and evade detection by smart strategies. Therefore, it is challenging to accurately catch such fraudsters in an unsupervised manner. Existing approaches do not consider the characteristics of those agent accounts and are not suitable to the streaming settings. Therefore, we propose MonLAD and MonLAD-W to detect money laundering agent accounts in a transaction stream by keeping track of their residuals and other features; we devise AnoScore algorithm to find anomalies based on the robust measure of statistical deviation. Experimental results show that MonLAD outperforms the state-of-the-art baselines on real-world data and finds various suspicious behavior patterns of money laundering. Additionally, several detected suspected accounts have been manually-verified as agents in real money laundering scenario.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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