CubeFlow: Money Laundering Detection with Coupled Tensors
- URL: http://arxiv.org/abs/2103.12411v1
- Date: Tue, 23 Mar 2021 09:24:31 GMT
- Title: CubeFlow: Money Laundering Detection with Coupled Tensors
- Authors: Xiaobing Sun, Jiabao Zhang, Qiming Zhao, Shenghua Liu, Jinglei Chen,
Ruoyu Zhuang, Huawei Shen, Xueqi Cheng
- Abstract summary: Money laundering (ML) is the behavior to conceal the source of money achieved by illegitimate activities.
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.
- Score: 39.26866956921283
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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