Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection

• URL: http://arxiv.org/abs/2609.07100v1
• Date: Mon, 07 Sep 2026 06:43:38 GMT
• Title: Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection
• Abstract summary: This paper proposes THGT-FD, a Temporal Heterogeneous Graph Transformer for Fraud Detection.<n>Experiments were conducted on 150,000 transactions sampled from the IEEE-CIS Fraud Detection dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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