Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities, and always be a fast process involving frequent and chained transactions. How can we detect ML and fraudulent activity in large scale attributed transaction data (i.e.~tensors)? Most existing methods detect dense blocks in a graph or a tensor, which do not consider the fact that money are frequently transferred through middle accounts. CubeFlow proposed in this paper is a scalable, flow-based approach to spot fraud from a mass of transactions by modeling them as two coupled tensors and applying a novel multi-attribute metric which can reveal the transfer chains accurately. Extensive experiments show CubeFlow outperforms state-of-the-art baselines in ML behavior detection in both synthetic and real data.
翻译:洗钱(ML)是隐藏非法活动所得资金来源的行为,而且总是涉及频繁和链式交易的快速过程。 我们怎样才能在大规模交易数据(即~tensors)中发现ML和欺诈活动? 多数现有方法在图表或发声器中检测密块,这些方法不考虑资金经常通过中间账户转移的事实。 本文提出的CubeFlow是一种可扩展的、流动的办法来从大量交易中发现欺诈,办法是将两者模拟为两个并存的加压器,并采用新的多分配指标来准确披露转移链。 广泛的实验显示CubeFlow在合成和真实数据中都显示ML行为探测中最先进的基线。