Clue-Guided Money Laundering Group Discovery
Summary
This paper proposes Clue2Group, a framework for clue-guided money laundering group discovery in financial networks, using a graph neural network to progressively recover criminal groups from initial clues.
View Cached Full Text
Cached at: 06/26/26, 05:15 AM
# Clue-Guided Money Laundering Group Discovery Source: [https://arxiv.org/abs/2606.26189](https://arxiv.org/abs/2606.26189) [View PDF](https://arxiv.org/pdf/2606.26189) > Abstract:Money Laundering Group Discovery \(MLGD\) aims to identify hidden criminal groups and recover their complete structures in large\-scale financial networks\. Existing graph anomaly detection methods mainly produce node\-level risk alerts, while global group discovery methods passively search for suspicious groups over the whole network\. Both are mismatched with real Anti\-money\-laundering \(AML\) investigations, where analysts usually start from a concrete clue and gradually expand the investigation to recover the responsible group\. To address this gap, we propose Clue\-Guided Group Discovery \(CGGD\), where a laundering group is progressively recovered from an initial clue set through analyst interaction\. We further propose Clue2Group, a framework that first constructs a compact local investigation context to reduce noise and preserve chain\-like and cycle\-like laundering structures\. It then estimates a clue\-conditioned local risk field with a multi\-semantic local\-temporal GNN, and finally integrates risk, structural, and prior\-pattern evidence to recover a coherent laundering group\. Experiments on two large\-scale AML benchmarks show that Clue2Group provides a practical clue\-driven analysis framework for AML investigations, offering a feasible step toward bridging the gap between graph\-based AML research and real investigation workflows\. ## Submission history From: Boyang Wang \[[view email](https://arxiv.org/show-email/9961ac1e/2606.26189)\] **\[v1\]**Wed, 24 Jun 2026 13:57:22 UTC \(3,547 KB\)
Similar Articles
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
The paper proposes a graph neural network approach to refine heuristic-based Bitcoin address clustering, releasing a dataset and introducing hierarchical clustering for better analysis of suspicious merges.
Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks
This paper proposes a graph-driven real-time anti-money laundering monitoring framework (GCRMF) for cross-industry supply chain networks, leveraging heterogeneous graphs and temporal attention networks, achieving over 17.8% F1 improvement.
Towards Adversarially Robust Multigraph Neural Networks for Financial Crime Analysis
Delft University of Technology researchers present the first scalable framework for structural adversarial attacks and adversarial training on multigraph neural networks used for anti-money laundering detection, introducing a cycle-based regularizer to keep perturbations plausible and showing that adversarial training restores detection robustness.
Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization
This paper proposes a graph neural network framework for financial fraud detection that integrates transaction records and identity information into node attributes, employs a multi-layer message passing mechanism, and uses weighted supervision and structural consistency regularization to improve risk scoring and probability calibration. Experiments on a public dataset show the method outperforms existing approaches.
Clustering-Based Collective Anomaly Detection in IoT Systems: A Graph Neural Network Approach
The paper proposes UGCAD, an unsupervised framework using variational graph autoencoders and clustering to detect collective anomalies in IoT network traffic, with experiments on benchmark datasets showing its effectiveness.