# Detection of money laundering groups using supervised learning in networks

arXiv: https://arxiv.org/abs/1608.00708 · HTML: https://mlsanity.com/paper/1608.00708/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 9 of 9)

- [Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions](https://mlsanity.com/paper/2309.13662/) (2023)
- [Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs](https://mlsanity.com/paper/2112.07508/) (2021)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Finding Money Launderers Using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.13499/) (2023)
- [Towards Artificial Intelligence Enabled Financial Crime Detection](https://mlsanity.com/paper/2105.10866/) (2021)
- [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)
- [Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection](https://mlsanity.com/paper/2506.00654/) (2025)
- [Tide: A Customisable Dataset Generator for Anti-Money Laundering Research](https://mlsanity.com/paper/2603.01863/) (2026)

## Abstract (arXiv)

Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates that suspicious activity is successfully detected. Importantly, the system exhibits a low rate of false positives, and is therefore suitable for use in a live intelligence environment.