Temporal Heterogeneous Graph Transformer for Credit Card Fraud Detection
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arXiv:2610.27107v1 Announce Type: new Abstract: Credit card fraud is a significant problem for financial institutions, resulting in substantial monetary loss each year. Fraud patterns and methods are...
Credit card fraud is a significant problem for financial institutions, resulting in substantial monetary loss each year. Fraud patterns and methods are continuously evolving, with fraudulent transacti...
arXiv:2608. 15177v1 Announce Type: cross Abstract: The increasing complexity of digital financial systems has reshaped financial fraud detection from isolated transaction classification into relational risk reasoning over interconnected financial entities.
arXiv:2606. 18444v1 Announce Type: cross Abstract: In recent years, credit card fraud detection has faced significant challenges due to highly imbalanced data, evolving fraud patterns, and complex relational structures among transaction entities.
SemHGT is a new framework that enhances heterogeneous graph learning for financial fraud detection by explicitly incorporating business semantics of structured attributes. It uses role‑aware prompts to generate risk‑related descriptions, maps these to semantic vectors, and then guides multi‑round sampling of transaction paths based on semantic similarity. The framework combines local path evidence and global neighbor summaries in a unified transformer, achieving superior performance on three benchmark datasets compared to existing baselines.
The paper introduces Temporal-Context Edge Enrichment (TCEE), a framework that uses frozen time‑series foundation models (TSFMs) to summarise each account’s transaction history and enrich transaction representations with temporal context. These enriched edges can be directly scored for classification or used as features in a graph neural network (GNN). Across six AMLWorld datasets, TCEE improves PR‑AUC by an average of 10.8% over the best baseline and trains 6.0× faster; when combined with a GNN, it yields an additional 4.8% improvement on smaller datasets and a 14.5% gain on an Ethereum phishing dataset.