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.
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:2606. 25007v1 Announce Type: new Abstract: Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams -- transactions, login sessions, risk signals -- that individually appear benign but collectively reveal fraudulent patterns.
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.
arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.
GraphFAS is a distributed system that automates graph feature generation and selection for industrial transaction networks. It uses a non‑parametric module to create explicit, interpretable structural features through multi‑hop subgraph extraction and multi‑scale aggregation, and extends the Boruta algorithm with median‑based aggregation across partitions for robust feature selection. The approach decouples feature aggregation from model training, allowing integration with tabular models and TreeSHAP explanations, and has been deployed in Alipay, yielding significant engineering efficiency gains and strong performance against expert‑driven and graph‑learning baselines.
GraphFAS is a distributed system that automates graph feature generation and selection for industrial transaction networks. It uses a non‑parametric graph feature generator that creates explicit, interpretable structural features through multi‑hop subgraph extraction and multi‑scale aggregation, and an extended Boruta algorithm that aggregates feature importance across partitions to robustly identify informative features at scale. By decoupling feature aggregation from model training, GraphFAS can be directly integrated with tabular models and TreeSHAP‑based explanations, and it has been deployed in Alipay, achieving significant engineering efficiency gains and strong performance against expert‑driven and graph‑learning baselines on large‑scale graphs.
Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems. Graph Neural Networks (GNNs) are commonly adopted for graph fraud detection.
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:2607. 11107v1 Announce Type: new Abstract: Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems.
arXiv:2607. 00477v1 Announce Type: new Abstract: A total of seven categorical encoding methods were tested on the IEEE-CIS fraud benchmark dataset (590,540 records, 3.