利用统计和机器学习打击洗钱
Fighting Money Laundering with Statistics and Machine Learning
摘要：
洗钱是一个深刻的全球性问题。然而，关于反洗钱的统计和机器学习方法的科学文献很少。本文以银行反洗钱为研究对象，对相关文献进行了介绍和综述。我们提出了一个统一的术语，有两个核心要素：（i）客户风险分析和（ii）可疑行为标记。我们发现，客户风险分析的特点是诊断，即，努力寻找和解释风险因素。另一方面，可疑行为标记的特点是不公开的特征和手工制作的风险指数。最后，我们讨论了未来的研究方向。一个主要挑战是需要更多的公共数据集。这可能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。
英文摘要：
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.
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。
