论文标题
Delator：通过大型交易图上的多任务学习洗钱检测
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs
论文作者
论文摘要
洗钱已成为现代社会中最相关的犯罪活动之一，因为它会给政府，银行和其他机构造成巨大的财务损失。在财务分析方面，检测此类活动是首要任务之一，但是当前的方法通常是由于要分析的数据量昂贵而劳动密集型。因此，越来越需要自动反洗钱系统来协助专家。在这项工作中，我们提出了Delator，这是一个新颖的框架，用于检测基于图形神经网络的洗钱活动，从大规模的时间图中学习。 Delator通过从GraphSmote框架中调整概念并结合了多任务学习的元素，从而提供了一种有效而有效的方法，可从严重失衡的图形数据中学习，以获取用于节点分类的丰富节点嵌入的元素。 Delator的表现都优于所有考虑的基线，包括亚马逊AWS的现成解决方案相对于AUC-ROC。我们还进行了真实的实验，导致在50例被分析的案件中发现了7个新的可疑案例，这些案件已报告给当局。
Money laundering has become one of the most relevant criminal activities in modern societies, as it causes massive financial losses for governments, banks and other institutions. Detecting such activities is among the top priorities when it comes to financial analysis, but current approaches are often costly and labor intensive partly due to the sheer amount of data to be analyzed. Hence, there is a growing need for automatic anti-money laundering systems to assist experts. In this work, we propose DELATOR, a novel framework for detecting money laundering activities based on graph neural networks that learn from large-scale temporal graphs. DELATOR provides an effective and efficient method for learning from heavily imbalanced graph data, by adapting concepts from the GraphSMOTE framework and incorporating elements of multi-task learning to obtain rich node embeddings for node classification. DELATOR outperforms all considered baselines, including an off-the-shelf solution from Amazon AWS by 23% with respect to AUC-ROC. We also conducted real experiments that led to the discovery of 7 new suspicious cases among the 50 analyzed ones, which have been reported to the authorities.
