mlsanityData to 14 Oct 2026

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

arXiv 1812.00076 ↗First posted 30 Nov 2018cs.SI cs.AI cs.LGcited by 33

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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.

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

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Citing papers per year: 2021: 6; 2022: 5; 2023: 9; 2024: 1; 2025: 4; 2026: 8.

33 papers.

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  1. 2023Realistic Synthetic Financial Transactions for Anti-Money Laundering Modelscited by 50
  2. 2023Provably Powerful Graph Neural Networks for Directed Multigraphscited by 27
  3. 2022LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Launderingcited by 20
  4. 2021Efficient Scaling of Dynamic Graph Neural Networkscited by 11
  5. 2021Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlookcited by 8
  6. 2021Anti-Money Laundering Alert Optimization Using Machine Learning with Graphscited by 7
  7. 2022Fighting Money Laundering with Statistics and Machine Learningcited by 5
  8. 2022Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensicscited by 5
  9. 2023Graph Mining for Cybersecurity: A Surveycited by 5
  10. 2023Finding Money Launderers Using Heterogeneous Graph Neural Networkscited by 5
  11. 2021Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlookcited by 4
  12. 20232SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detectioncited by 3
21 more
  1. 2021Generating a Doppelganger Graph: Resembling but Distinctcited by 2
  2. 2021How effective are Graph Neural Networks in Fraud Detection for Network Data?cited by 2
  3. 2023Catch Me If You Can: Semi-supervised Graph Learning for Spotting Money Launderingcited by 2
  4. 2022Machine Learning in Transaction Monitoring: The Prospect of xAIcited by 1
  5. 2023Privacy-Preserving Federated Learning over Vertically and Horizontally Partitioned Data for Financial Anomaly Detectioncited by 1
  6. 2024Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Launderingcited by 1
  7. 2025Towards Collaborative Anti-Money Laundering Among Financial Institutionscited by 1
  8. 2022Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?cited by 0
  9. 2023Hulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systemscited by 0
  10. 2023The GANfather: Controllable generation of malicious activity to improve defence systemscited by 0
  11. 2025Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detectioncited by 0
  12. 2025Ralts: Robust Aggregation for Enhancing Graph Neural Network Resilience on Bit-flip Errorscited by 0
  13. 2025AMLNet: A Knowledge-Based Multi-Agent Framework to Generate and Detect Realistic Money Laundering Transactionscited by 0
  14. 2026Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemescited by 0
  15. 2026TransXion: A High-Fidelity Graph Benchmark for Realistic Anti-Money Launderingcited by 0
  16. 2026Explainable AML Triage with LLMs: Evidence Retrieval and Counterfactual Checkscited by 0
  17. 2026Mechanism Design for Decentralized Risk Detection: Strict Propriety, Network Coalitions, and the Backfiring Mandatcited by 0
  18. 2026Temporal Contrastive Transformer for Financial Crime Detection: Self-Supervised Sequence Embeddings via Predictive Contrastive Codingcited by 0
  19. 2026Provably Communication-Efficient and Privacy-Preserving Federated Graph Neural Networkscited by 0
  20. 2026Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chainscited by 0
  21. 2026CARE: A Lightweight Plug-in Gated Correction and Uncertainty-aware Module for Long-term Time Series Forecastingcited by 0