# Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering

arXiv: https://arxiv.org/abs/2302.11880 · HTML: https://mlsanity.com/paper/2302.11880/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 17 of 17)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [SimplE Embedding for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1802.04868/) (2018)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Anomaly Detection in Dynamic Graphs via Transformer](https://mlsanity.com/paper/2106.09876/) (2021)
- [Interaction Embeddings for Prediction and Explanation in Knowledge Graphs](https://mlsanity.com/paper/1903.04750/) (2019)
- [Scalable Graph Learning for Anti-Money Laundering: A First Look](https://mlsanity.com/paper/1812.00076/) (2018)
- [Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network](https://mlsanity.com/paper/1908.01288/) (2019)
- [Attributed Network Embedding via Subspace Discovery](https://mlsanity.com/paper/1901.04095/) (2019)
- [Knowlege Graph Embedding by Flexible Translation](https://mlsanity.com/paper/1505.05253/) (2015)

## Built on by (most cited first, 2 of 2)

- [Towards Collaborative Anti-Money Laundering Among Financial Institutions](https://mlsanity.com/paper/2502.19952/) (2025)
- [Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection](https://mlsanity.com/paper/2506.00654/) (2025)

## Abstract (arXiv)

Money laundering is the process where criminals use financial services to move massive amounts of illegal money to untraceable destinations and integrate them into legitimate financial systems. It is very crucial to identify such activities accurately and reliably in order to enforce an anti-money laundering (AML). Despite tremendous efforts to AML only a tiny fraction of illicit activities are prevented. From a given graph of money transfers between accounts of a bank, existing approaches attempted to detect money laundering. In particular, some approaches employ structural and behavioural dynamics of dense subgraph detection thereby not taking into consideration that money laundering involves high-volume flows of funds through chains of bank accounts. Some approaches model the transactions in the form of multipartite graphs to detect the complete flow of money from source to destination. However, existing approaches yield lower detection accuracy, making them less reliable. In this paper, we employ semi-supervised graph learning techniques on graphs of financial transactions in order to identify nodes involved in potential money laundering. Experimental results suggest that our approach can sport money laundering from real and synthetic transaction graphs.