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arXiv · 2103.12411

CubeFlow: Money Laundering Detection with Coupled Tensors

Xiaobing Sun·Jiabao Zhang·Qiming Zhao·Shenghua Liu·Jinglei Chen·Ruoyu Zhuang·Huawei Shen·Xueqi Cheng
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Abstract

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.

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Xiaobing Sun, Jiabao Zhang, Qiming Zhao, Shenghua Liu, Jinglei Chen, Ruoyu Zhuang, Huawei Shen, Xueqi Cheng. 2021-03-23. CubeFlow: Money Laundering Detection with Coupled Tensors. https://arxiv.org/abs/2103.12411

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