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

TL;DR

GARG-AML is a scalable, transparent graph-based method for detecting money-laundering smurfing activities, providing interpretable risk scores suitable for large financial networks.

Contribution

It introduces a simple, network-based approach that balances detection accuracy with speed and interpretability, avoiding complex deep learning models.

Findings

GARG-AML matches or exceeds state-of-the-art performance.

It efficiently processes large transaction graphs.

Uses only basic network features for effective detection.

Abstract

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.