# Scalable Graph Learning for Anti-Money Laundering: A First Look

arXiv: https://arxiv.org/abs/1812.00076 · HTML: https://mlsanity.com/paper/1812.00076/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 30 of 33)

- [Realistic Synthetic Financial Transactions for Anti-Money Laundering Models](https://mlsanity.com/paper/2306.16424/) (2023)
- [Provably Powerful Graph Neural Networks for Directed Multigraphs](https://mlsanity.com/paper/2306.11586/) (2023)
- [LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering](https://mlsanity.com/paper/2210.14360/) (2022)
- [Efficient Scaling of Dynamic Graph Neural Networks](https://mlsanity.com/paper/2109.07893/) (2021)
- [Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook](https://mlsanity.com/paper/2103.03227/) (2021)
- [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)
- [Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics](https://mlsanity.com/paper/2206.04803/) (2022)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Finding Money Launderers Using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2307.13499/) (2023)
- [Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook](https://mlsanity.com/paper/2103.01854/) (2021)
- [2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection](https://mlsanity.com/paper/2310.08335/) (2023)
- [Generating a Doppelganger Graph: Resembling but Distinct](https://mlsanity.com/paper/2101.09593/) (2021)
- [How effective are Graph Neural Networks in Fraud Detection for Network Data?](https://mlsanity.com/paper/2105.14568/) (2021)
- [Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Laundering](https://mlsanity.com/paper/2302.11880/) (2023)
- [Machine Learning in Transaction Monitoring: The Prospect of xAI](https://mlsanity.com/paper/2210.07648/) (2022)
- [Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detection](https://mlsanity.com/paper/2310.19304/) (2023)
- [Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering](https://mlsanity.com/paper/2411.02926/) (2024)
- [Towards Collaborative Anti-Money Laundering Among Financial Institutions](https://mlsanity.com/paper/2502.19952/) (2025)
- [Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?](https://mlsanity.com/paper/2205.09648/) (2022)
- [Hulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systems](https://mlsanity.com/paper/2302.13741/) (2023)
- [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)
- [Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errors](https://mlsanity.com/paper/2507.18804/) (2025)
- [AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactions](https://mlsanity.com/paper/2509.11595/) (2025)
- [Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes](https://mlsanity.com/paper/2602.16109/) (2026)
- [TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Laundering](https://mlsanity.com/paper/2604.17420/) (2026)
- [Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checks](https://mlsanity.com/paper/2604.19755/) (2026)
- [Mechanism Design for Decentralized Risk Detection: Strict Propriety, Network Coalitions, and the Backfiring Mandat](https://mlsanity.com/paper/2604.21789/) (2026)
- [Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Coding](https://mlsanity.com/paper/2605.21490/) (2026)

## Abstract (arXiv)

Organized crime inflicts human suffering on a genocidal scale: the Mexican drug cartels have murdered 150,000 people since 2006, upwards of 700,000 people per year are "exported" in a human trafficking industry enslaving an estimated 40 million people. These nefarious industries rely on sophisticated money laundering schemes to operate. Despite tremendous resources dedicated to anti-money laundering (AML) only a tiny fraction of illicit activity is prevented. The research community can help. In this brief paper, we map the structural and behavioral dynamics driving the technical challenge. We review AML methods, current and emergent. We provide a first look at scalable graph convolutional neural networks for forensic analysis of financial data, which is massive, dense, and dynamic. We report preliminary experimental results using a large synthetic graph (1M nodes, 9M edges) generated by a data simulator we created called AMLSim. We consider opportunities for high performance efficiency, in terms of computation and memory, and we share results from a simple graph compression experiment. Our results support our working hypothesis that graph deep learning for AML bears great promise in the fight against criminal financial activity.