基于线索逐步发现洗钱团伙，贴近真实反洗钱调查流程。
Clue-Guided Money Laundering Group Discovery
- 从初始线索出发，通过交互式方式逐步扩展识别洗钱集团
- 在两个大规模数据集上显著提升团伙发现准确率与可解释性
- 适合反洗钱分析师及金融风控系统集成使用
洗钱团伙发现（MLGD）旨在大规模金融网络中识别隐藏的犯罪团体并恢复其完整结构。现有图异常检测方法多生成节点级风险警告，而全局团伙发现方法则被动遍历全网搜寻可疑群体，均不匹配真实反洗钱（AML）调查中分析师从具体线索出发、逐步拓展调查的实践。为此，我们提出线索引导的团伙发现（CGGD），通过分析师交互逐步从初始线索集恢复洗钱团伙。进一步提出Clue2Group框架：首先构建紧凑的局部调查上下文以降低噪声并保留链状与环状洗钱结构；然后利用多语义时空图神经网络估计线索条件下的局部风险场；最后融合风险、结构与先验模式证据，恢复连贯的洗钱团伙。在两个大规模AML基准上的实验表明，Clue2Group为AML调查提供了实用的线索驱动分析框架，是弥合图方法研究与真实调查流程差距的重要一步。
原文摘要 · Abstract (English)
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.
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