用时空图神经网络检测洗钱，更准且误报少。
Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection
- 基于全时序图结构建模交易关系，不拆分时间片段。
- 在公开数据集上达F1 0.76，误报率比现有模型低55%。
- 适合金融风控、反洗钱系统部署，尤其数据有限时表现佳。
洗钱是威胁金融安全与社会稳定的严重犯罪行为。随着交易量持续增长，亟需自动化工具辅助执法机构识别此类活动。本文提出Amatriciana，一种基于图神经网络的新型方法，通过融合时间信息，在完整的交易图中检测洗钱者。该方法不将图按时间切分为子图，从而充分利用数据中的全部关联信息。在公开数据集上的实验表明，模型可在少量数据下学习有效特征；当数据量增加时，性能超越现有最先进方法。具体而言，Amatriciana实现了0.76的F1分数，同时相较其他领先模型将误报数降低55%。
原文摘要 · Abstract (English)
Money laundering is a financial crime that poses a serious threat to financial integrity and social security. The growing number of transactions makes it necessary to use automatic tools that help law enforcement agencies detect such criminal activity. In this work, we present Amatriciana, a novel approach based on Graph Neural Networks to detect money launderers inside a graph of transactions by considering temporal information. Amatriciana uses the whole graph of transactions without splitting it into several time-based subgraphs, exploiting all relational information in the dataset. Our experiments on a public dataset reveal that the model can learn from a limited amount of data. Furthermore, when more data is available, the model outperforms other State-of-the-art approaches; in particular, Amatriciana decreases the number of False Positives (FPs) while detecting many launderers. In summary, Amatriciana achieves an F1 score of 0.76. In addition, it lowers the FPs by 55% with respect to other State-of-the-art models.
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