Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection

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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.07100 [cs.LG]

(or arXiv:2609.07100v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.07100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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