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.
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...
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.
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...
MINT is a graph neural network framework designed to detect illicit accounts in financial transaction networks. It addresses structural inconsistency by using an attentive message‑passing scheme that tracks deviations between nodes in bi‑directional flows and a transaction salience module that regularizes node interactions. To cope with limited labeled data, MINT employs Positive‑Unlabeled learning, leveraging a small set of known illicit nodes and many unlabeled ones, and demonstrates superior performance on three benchmark datasets.
arXiv:2606. 08146v1 Announce Type: new Abstract: Fraud detection in payment, e-commerce, and telecommunications systems requires accuracy at the individual level, robustness under severe class imbalance, and ease of understanding for risk managers.
arXiv:2609.14234v1 Announce Type: cross Abstract: Financial fraud in corporate transaction networks has grown more coordinated and harder to detect with rule-based engines and with classical learning...
arXiv:2609.07100v1 Announce Type: cross Abstract: Credit card fraud detection typically relies on tabular features, while repeated attributes can also provide useful relational signals. This paper pr...
arXiv:2609.34211v2 Announce Type: replace Abstract: In financial fraud detection, rich semantic context can provide important evidence for transaction behavior modeling and fraud reasoning. However,...
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.
arXiv:2607. 19350v1 Announce Type: new Abstract: Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due to extreme data imbalance (0.
Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems. Accurately identifying credit fraud among billions of users is critical for minimizing financial losses and safeguarding the sustainability of inclusive financial services.
arXiv:2608. 02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems.