论文标题
MONLAD：洗钱代理商在交易流中的检测
MonLAD: Money Laundering Agents Detection in Transaction Streams
论文作者
论文摘要
鉴于银行中帐户之间的一系列货币交易流，我们如何能够实时检测洗钱代理商帐户和可疑行为？洗钱代理商试图通过分散多次小型交易并通过智能策略逃避检测来掩盖非法获得的资金的起源。因此，以无监督的方式准确地捕获此类欺诈者是一项挑战。现有方法不考虑这些代理帐户的特征，也不适合流设置。因此，我们建议Monlad和Monlad-W通过跟踪其残留物和其他功能，以在交易流中检测洗钱代理商。我们设计了基于统计偏差的稳健度量的抗异常算法以找到异常。实验结果表明，Monlad的表现优于现实数据的最先进基线，并发现了各种可疑的洗钱行为模式。此外，在真正的洗钱情况下，已经手动验证了一些被发现的可疑帐户作为代理商。
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.
