论文标题
用统计和机器学习与洗钱打架
Fighting Money Laundering with Statistics and Machine Learning
论文作者
论文摘要
洗钱是一个深刻的全球问题。尽管如此，关于反洗钱的统计和机器学习方法，几乎没有科学文献。在本文中，我们专注于银行中的反洗钱，并对文献进行介绍和审查。我们提出了一个统一的术语，其中有两个中心元素：（i）客户风险分析和（ii）可疑行为标记。我们发现，客户风险分析的特征是诊断，即寻找和解释风险因素的努力。另一方面，可疑行为标记的特征是未披露功能和手工制作的风险指数。最后，我们讨论未来研究的方向。一个主要的挑战是需要更多的公共数据集。这可能会通过合成数据生成来解决。其他可能的研究方向包括半监督和深度学习，可解释性和结果的公平性。
Money laundering is a profound global problem. Nonetheless, there is little scientific literature on statistical and machine learning methods for anti-money laundering. In this paper, we focus on anti-money laundering in banks and provide an introduction and review of the literature. We propose a unifying terminology with two central elements: (i) client risk profiling and (ii) suspicious behavior flagging. We find that client risk profiling is characterized by diagnostics, i.e., efforts to find and explain risk factors. On the other hand, suspicious behavior flagging is characterized by non-disclosed features and hand-crafted risk indices. Finally, we discuss directions for future research. One major challenge is the need for more public data sets. This may potentially be addressed by synthetic data generation. Other possible research directions include semi-supervised and deep learning, interpretability, and fairness of the results.
