用图注意力模型分析交易关系，提升信用卡欺诈识别能力
Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection
- 将每笔交易和六类关联信息转为令牌，结合时间编码建模
- 在真实数据上达AUC 0.8536，召回率在5%阈值下为47.08%
- 适合做金融风控中关系特征挖掘的从业者参考
信用卡欺诈检测通常依赖表格特征，但重复属性也能提供有用的关系信号。本文提出THGT-FD，一种用于欺诈检测的时间异构图变压器。每笔交易通过一个交易令牌和六种关系令牌表示，并融入Time2Vec时间编码。Transformer在单个交易内学习这些令牌间的交互，输出欺诈概率。实验基于IEEE-CIS欺诈检测数据集采样的15万笔交易，按交易时间切分。测试集上，THGT-FD获得AUC-ROC 0.8536、平均精度0.4164、召回率@5%为0.4708。类权重直方图梯度提升基线的AUC-ROC为0.8722。结果表明，关系令牌对欺诈风险排序有帮助，但当前模型尚未整合实体级历史聚合。
原文摘要 · Abstract (English)
Credit card fraud detection typically relies on tabular features, while repeated attributes can also provide useful relational signals. This paper proposes THGT-FD, a Temporal Heterogeneous Graph Transformer for Fraud Detection. Each transaction is represented using one transaction token and six types of relation tokens and incorporates Time2Vec encoding into the transaction representation. A Transformer learns the interactions among these tokens within each individual transaction and then outputs a fraud probability. Experiments were conducted on 150,000 transactions sampled from the IEEE-CIS Fraud Detection dataset and chronologically partitioned according to TransactionDT. On the test set, THGT-FD achieved an AUC-ROC of 0.8536, an average precision of 0.4164, and a Recall@5% of 0.4708. The class-weighted histogram-based gradient-boosting baseline achieved an AUC-ROC of 0.8722. The results indicate that relation tokens provide useful information for fraud-risk ranking, although the current model does not yet incorporate entity-level historical aggregation.
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