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

• URL: http://arxiv.org/abs/2506.04292v1
• Date: Wed, 04 Jun 2025 11:30:37 GMT
• Title: GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering
• Authors: Bruno Deprez, Bart Baesens, Tim Verdonck, Wouter Verbeke,
• Abstract summary: Money laundering is estimated to account for 2%-5% of the global GDP.<n>GARG-AML is a novel graph-based method that quantifies smurfing risk through a single interpretable metric.
• Score: 1.9461779294968458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Money laundering poses a significant challenge as it is estimated to account for 2%-5% of the global GDP. This has compelled regulators to impose stringent controls on financial institutions. One prominent laundering method for evading these controls, called smurfing, involves breaking up large transactions into smaller amounts. Given the complexity of smurfing schemes, which involve multiple transactions distributed among diverse parties, network analytics has become an important anti-money laundering tool. However, recent advances have focused predominantly on black-box network embedding methods, which has hindered their adoption in businesses. In this paper, we introduce GARG-AML, a novel graph-based method that quantifies smurfing risk through a single interpretable metric derived from the structure of the second-order transaction network of each individual node in the network. Unlike traditional methods, GARG-AML strikes an effective balance among computational efficiency, detection power and transparency, which enables its integration into existing AML workflows. To enhance its capabilities, we combine the GARG-AML score calculation with different tree-based methods and also incorporate the scores of the node's neighbours. An experimental evaluation on large-scale synthetic and open-source networks demonstrate that the GARG-AML outperforms the current state-of-the-art smurfing detection methods. 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.

Related papers

• Detecting Fraud in Financial Networks: A Semi-Supervised GNN Approach with Granger-Causal Explanations [5.407319151576265]
  Fraudulent activity in the financial industry costs billions annually.<n>This article proposes SAGE-FIN, a semi-supervised graph neural network (GNN) based approach with Granger causal explanations for Financial Interaction Networks.<n>We empirically validate the favorable performance of SAGE-FIN on a real-world dataset, Bipartite Edge-And-Node Attributed financial network (Elliptic++), with Granger-causal explanations for the identified fraudulent items without any prior assumption on the network structure.
  arXiv  Detail & Related papers  (2025-06-25T12:04:40Z)
• NDCG-Consistent Softmax Approximation with Accelerated Convergence [67.10365329542365]
  We propose novel loss formulations that align directly with ranking metrics.<n>We integrate the proposed RG losses with the highly efficient Alternating Least Squares (ALS) optimization method.<n> Empirical evaluations on real-world datasets demonstrate that our approach achieves comparable or superior ranking performance.
  arXiv  Detail & Related papers  (2025-06-11T06:59:17Z)
• Deep Learning Approaches for Anti-Money Laundering on Mobile Transactions: Review, Framework, and Directions [51.43521977132062]
  Money laundering is a financial crime that obscures the origin of illicit funds.<n>The proliferation of mobile payment platforms and smart IoT devices has significantly complicated anti-money laundering investigations.<n>This paper conducts a comprehensive review of deep learning solutions and the challenges associated with their use in AML.
  arXiv  Detail & Related papers  (2025-03-13T05:19:44Z)
• A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection [60.09453163562244]
  We propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD.<n>In the estimation module, we design a novel label-free heterophily metric called HALO, which captures the critical graph properties for GFD.<n>In the alignment-based fraud detection module, we develop a joint-GNN architecture with ranking loss and asymmetric alignment loss.
  arXiv  Detail & Related papers  (2025-02-18T22:07:36Z)
• Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering [4.1964397179107085]
  This research presents a novel privacy-preserving approach for collaborative AML machine learning. It facilitates secure data sharing across institutions and borders while preserving privacy and regulatory compliance. The research contributes two key privacy-preserving pipelines.
  arXiv  Detail & Related papers  (2024-11-05T09:13:53Z)
• Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions [0.03626013617212666]
  We propose a framework to efficiently construct a temporal graph of sequential transactions. We evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions.
  arXiv  Detail & Related papers  (2023-09-24T15:11:58Z)
• Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding [57.71603937699949]
  We study optimization guarantees, i.e., achieving near-zero training loss with the increase in the number of learning epochs. We show that the threshold on the number of training samples increases with the increase in the network width.
  arXiv  Detail & Related papers  (2023-09-12T13:03:47Z)
• Finding Money Launderers Using Heterogeneous Graph Neural Networks [0.0]
  This paper introduces a graph neural network (GNN) approach to identify money laundering activities within a large heterogeneous network. We extend the homogeneous GNN method known as the Message Passing Neural Network (MPNN) to operate effectively on a heterogeneous graph. Our findings highlight the importance of using an appropriate GNN architecture when combining information in heterogeneous graphs.
  arXiv  Detail & Related papers  (2023-07-25T13:49:15Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering [5.478764356647437]
  LaundroGraph is a novel self-supervised graph representation learning approach. It provides insights to assist the anti-money laundering reviewing process. To the best of our knowledge, this is the first fully self-supervised system within the context of AML detection.
  arXiv  Detail & Related papers  (2022-10-25T21:58:02Z)
• IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound [85.6899802468343]
  We present IBP-R, a novel verified training algorithm that is both simple effective. We also present UPB, a novel robustness based on $beta$-CROWN, that reduces the cost state-of-the-art branching algorithms.
  arXiv  Detail & Related papers  (2022-06-29T17:13:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.