# Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs

arXiv: https://arxiv.org/abs/2112.07508 · HTML: https://mlsanity.com/paper/2112.07508/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 14 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [Scalable Graph Learning for Anti-Money Laundering: A First Look](https://mlsanity.com/paper/1812.00076/) (2018)
- [Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity](https://mlsanity.com/paper/2005.14635/) (2020)
- [Interleaved Sequence RNNs for Fraud Detection](https://mlsanity.com/paper/2002.05988/) (2020)
- [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021)
- [Detection of money laundering groups using supervised learning in networks](https://mlsanity.com/paper/1608.00708/) (2016)
- [Characterizing and Detecting Money Laundering Activities on the Bitcoin Network](https://mlsanity.com/paper/1912.12060/) (2019)
- [CubeFlow: Money Laundering Detection with Coupled Tensors](https://mlsanity.com/paper/2103.12411/) (2021)

## Built on by (most cited first, 7 of 7)

- [LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering](https://mlsanity.com/paper/2210.14360/) (2022)
- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [Graph Feature Preprocessor: Real-time Subgraph-based Feature Extraction for Financial Crime Detection](https://mlsanity.com/paper/2402.08593/) (2024)
- [Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach](https://mlsanity.com/paper/2308.01063/) (2023)
- [Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering](https://mlsanity.com/paper/2411.02926/) (2024)
- [Representation learning with a transformer by contrastive learning for money laundering detection](https://mlsanity.com/paper/2507.08835/) (2025)

## Abstract (arXiv)

Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption. The anti-money laundering systems deployed by financial institutions typically comprise rules aligned with regulatory frameworks. Human investigators review the alerts and report suspicious cases. Such systems suffer from high false-positive rates, undermining their effectiveness and resulting in high operational costs. We propose a machine learning triage model, which complements the rule-based system and learns to predict the risk of an alert accurately. Our model uses both entity-centric engineered features and attributes characterizing inter-entity relations in the form of graph-based features. We leverage time windows to construct the dynamic graph, optimizing for time and space efficiency. We validate our model on a real-world banking dataset and show how the triage model can reduce the number of false positives by 80% while detecting over 90% of true positives. In this way, our model can significantly improve anti-money laundering operations.