# GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering

arXiv: https://arxiv.org/abs/2506.04292 · HTML: https://mlsanity.com/paper/2506.04292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 68 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 15 of 15)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [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)
- [A Comparative Analysis of Community Detection Algorithms on Artificial Networks](https://mlsanity.com/paper/1608.00763/) (2016)
- [Detecting Mixing Services via Mining Bitcoin Transaction Network with Hybrid Motifs](https://mlsanity.com/paper/2001.05233/) (2020)
- [LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering](https://mlsanity.com/paper/2210.14360/) (2022)
- [AutoAudit: Mining Accounting and Time-Evolving Graphs](https://mlsanity.com/paper/2011.00447/) (2020)
- [Using social network analysis to prevent money laundering](https://mlsanity.com/paper/2105.05793/) (2021)
- [Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin](https://mlsanity.com/paper/2203.10465/) (2022)
- [Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions](https://mlsanity.com/paper/2309.13662/) (2023)
- [Knowledge Discovery in Cryptocurrency Transactions: A Survey](https://mlsanity.com/paper/2010.01031/) (2020)
- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [Towards Understanding and Demystifying Bitcoin Mixing Services](https://mlsanity.com/paper/2010.16274/) (2020)

## Abstract (arXiv)

Purpose: We introduce GARG-AML, a fast and transparent graph-based method to catch `smurfing', a common money-laundering tactic. It assigns a single, easy-to-understand risk score to every account in both directed and undirected networks. Unlike overly complex models, it balances detection power with the speed and clarity that investigators require. Methodology: The method maps an account's immediate and secondary connections (its second-order neighbourhood) into an adjacency matrix. By measuring the density of specific blocks within this matrix, GARG-AML flags patterns that mimic smurfing behaviour. We further boost the model's performance using decision trees and gradient-boosting classifiers, testing the results against current state-of-the-art on both synthetic and open-source data. Findings: GARG-AML matches or beats state-of-the-art performance across all tested datasets. Crucially, it easily processes the massive transaction graphs typical of large financial institutions. By leveraging only the adjacency matrix of the second-order neighbourhood and basic network features, this work highlights the potential of fundamental network properties towards advancing fraud detection. Originality: The originality lies in the translation of human expert knowledge of smurfing directly into a simple network representation, rather than relying on uninterpretable deep learning. Because GARG-AML is built expressly for the real-world business demands of scalability and interpretability, banks can easily incorporate it in their existing AML solutions.