让交易图神经网络更关注近期交易，提升洗钱检测能力
TeMP-TraG: Edge-based Temporal Message Passing in Transaction Graphs
- 基于时间动态的消息传递机制，优先处理最近交易
- 平均提升4种主流模型性能6.19%
- 适合金融反欺诈、犯罪模式挖掘场景
交易图通过表示银行账户、公司等实体间的资金与贸易往来，可揭示洗钱、欺诈等金融犯罪的模式。但有效识别此类行为需应对交易图中丰富的边特征、多图结构及时间动态等挑战。为此，我们提出TeMP-TraG，一种将时间动态融入消息传递的新型图神经网络机制。该方法在聚合节点信息时优先考虑较新交易，从而更好捕捉时间敏感型异常模式。实验表明，TeMP-TraG使四种先进图神经网络平均性能提升6.19%。结果验证了其在利用交易图打击金融犯罪方面的有效性。
原文摘要 · Abstract (English)
Transaction graphs, which represent financial and trade transactions between entities such as bank accounts and companies, can reveal patterns indicative of financial crimes like money laundering and fraud. However, effective detection of such cases requires node and edge classification methods capable of addressing the unique challenges of transaction graphs, including rich edge features, multigraph structures and temporal dynamics. To tackle these challenges, we propose TeMP-TraG, a novel graph neural network mechanism that incorporates temporal dynamics into message passing. TeMP-TraG prioritises more recent transactions when aggregating node messages, enabling better detection of time-sensitive patterns. We demonstrate that TeMP-TraG improves four state-of-the-art graph neural networks by 6.19% on average. Our results highlight TeMP-TraG as an advancement in leveraging transaction graphs to combat financial crime.
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