Clue-Guided Money Laundering Group Discovery
This paper introduces Clue-Guided Group Discovery (CGGD), a paradigm that progressively recovers money laundering groups from initial clues through analyst interaction. The proposed Clue2Group framework constructs a compact local investigation context, uses a multi-semantic local-temporal GNN to estimate risk fields, and integrates evidence to recover criminal groups. Experiments on large-scale AML benchmarks demonstrate its practical potential for real-world investigations.
[2606.26189] Clue-Guided Money Laundering Group Discovery
[Submitted on 24 Jun 2026]
Title:Clue-Guided Money Laundering Group Discovery
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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.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2606.26189 [cs.LG]
(or arXiv:2606.26189v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.26189
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Boyang Wang [view email] [v1] Wed, 24 Jun 2026 13:57:22 UTC (3,547 KB)
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