Money laundering is the process where criminals use financial services to move massive amounts of illegal money to untraceable destinations and integrate them into legitimate financial systems. It is very crucial to identify such activities accurately and reliably in order to enforce an anti-money laundering (AML). Despite tremendous efforts to AML only a tiny fraction of illicit activities are prevented. From a given graph of money transfers between accounts of a bank, existing approaches attempted to detect money laundering. In particular, some approaches employ structural and behavioural dynamics of dense subgraph detection thereby not taking into consideration that money laundering involves high-volume flows of funds through chains of bank accounts. Some approaches model the transactions in the form of multipartite graphs to detect the complete flow of money from source to destination. However, existing approaches yield lower detection accuracy, making them less reliable. In this paper, we employ semi-supervised graph learning techniques on graphs of financial transactions in order to identify nodes involved in potential money laundering. Experimental results suggest that our approach can sport money laundering from real and synthetic transaction graphs.

翻译：洗钱是犯罪分子利用金融服务将大量非法资金转移至不可追踪目的地并融入合法金融体系的过程。为实施反洗钱（AML）行动，准确可靠地识别此类活动至关重要。尽管在反洗钱领域付出了巨大努力，但仅有极小部分非法活动得以阻止。现有方法尝试从银行账户间资金转账图中检测洗钱行为。具体而言，部分方法利用稠密子图检测的结构与行为动态特征，却未考虑洗钱过程涉及通过银行账户链进行的高流量资金转移。另有方法将交易建模为多部图形式以检测资金从源头至目的地的完整流动路径。然而，现有方法检测精度较低，可靠性不足。本文在金融交易图上采用半监督图学习技术，识别可能涉及洗钱的节点。实验结果表明，本方法能从真实与合成交易图中有效识别洗钱行为。