# CubeFlow: Money Laundering Detection with Coupled Tensors

arXiv: https://arxiv.org/abs/2103.12411 · HTML: https://mlsanity.com/paper/2103.12411/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs](https://mlsanity.com/paper/2112.07508/) (2021)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [Detecting Complex Money Laundering Patterns with Incremental and Distributed Graph Modeling](https://mlsanity.com/paper/2604.01315/) (2026)
- [The GANfather: Controllable generation of malicious activity to improve defence systems](https://mlsanity.com/paper/2307.13787/) (2023)

## Abstract (arXiv)

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.