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.
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# 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 \(3,547 KB\)
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