CubeFlow: Money Laundering Detection with Coupled Tensors

TL;DR

CubeFlow introduces a scalable tensor-based method that models transaction data as coupled tensors, effectively detecting money laundering by capturing transfer chains and outperforming existing approaches.

Contribution

It presents a novel flow-based approach using coupled tensors and a multi-attribute metric for accurate detection of money laundering activities.

Findings

Outperforms state-of-the-art baselines in synthetic data

Effective in real-world transaction datasets

Accurately reveals transfer chains in money laundering

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.