构建跨行业图神经网络框架，实时识别出行与能源融合中的洗钱行为
Graph-Driven Cross-Industry Real-Time Monitoring Framework for Anti-Money Laundering Detection in Converged Mobility-Energy Supply Chain Networks
- 基于多源数据构建跨行业异质图，动态编码资金流动演化特征
- 通过对比学习与分层采样提升对合谋洗钱模式的识别能力，F1提升超17.8%
- 支持自监督在线学习，可实时应对新型洗钱策略，适合金融监管场景
随着出行与能源行业的深度融合，跨行业供应链金融正成为隐蔽洗钱的高风险领域。本文提出一种图驱动的跨行业实时反洗钱监控框架（GCRMF），用于整合出行-能源供应链网络。首先构建涵盖新能源汽车租赁平台、能源供应商、金融科技机构等的跨行业异质图（CIHG），并通过时序双图注意力网络（Temporal Dual-GAT）融合行业语义，动态编码资金流路径与演化特征。其次，为识别合谋主体产生的结构性欺诈行为，提出基于对比学习与层级图采样的元路径子图推理模块，增强对跨行业重复洗钱行为的判别能力。同时引入自监督在线学习机制，实现对新型洗钱策略的实时适应与持续优化。实验表明，相比现有跨行业图神经网络方法，GCRMF在F1分数上提升超过17.8%，并显著降低误报率。
原文摘要 · Abstract (English)
With the deep integration of the travel and energy industries, cross-industry supply chain finance has gradually become a high-risk field of hidden money laundering incidents. For this reason, this work proposes a graph-driven cross-industry real-time anti-money laundering monitoring framework (GCRMF) for integrated travel - energy supply chain networks. First, a cross-industry heterogeneous graph (CIHG) covering new energy vehicle rental platforms, energy suppliers, fintech institutions, etc., is constructed, and industry semantics are integrated through temporarily Dual-GAT (Temporal Dual-Graph Attention Network), dynamically encoding capital flow paths and evolution features over time. Subsequently, in order to identify the structural fraud behavior together produced by colluding subjects, a meta-path subgraph reasoning module based on contrastive learning and hierarchical graph sampling is proposed to enhance the discrimination capability of cross-industry recurring money laundering behavior. Meanwhile, a self-supervised online learning mechanism is adopted for real-time adaptation and continuous optimization to new money laundering strategies. The experimental results show that compared with existing graph neural network methods in cross-industry scenarios, GCRMF improves the performance by more than 17.8% of F1 score and greatly reduces the false positive rate.
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