Graph-Transformer Fraud Detection with Self-Supervised Pretraining and Conformal Risk Control
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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...
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 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. 28134v1 Announce Type: cross Abstract: Graph-based fraud detection is essential for safeguarding large-scale transaction systems, where undetected anomalies may lead to substantial financial losses and security risks.
arXiv:2610.31718v1 Announce Type: new Abstract: Financial transaction networks are naturally represented as multigraphs, where repeated transactions between the same pair of accounts are modeled as d...