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.
arXiv:2608. 02168v1 Announce Type: new Abstract: Credit risk detection, particularly mitigating individual fraud, is crucial for maintaining the stability of digital financial ecosystems.
By Xin Liu, Xiyuan Chen, Chenglong Wu, Xuan Zong, Jun Zhou, Dawei Cheng
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.
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:2412.00241v3 Announce Type: replace
Abstract: Edge-attributed multigraphs, in which multiple edges with distinct attributes connect the same pair of nodes, arise naturally in many real-world sy...
By H. \c{C}a\u{g}r{\i} Bilgi, Kubilay Atasu
arXiv:2607. 11107v1 Announce Type: new Abstract: Graph fraud detection plays a pivotal role in safeguarding the security and integrity of modern digital ecosystems.
By Junpeng Wu, Ye Yuan
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.
By Yixuan Chen, Hongyu Zhan, Jie Sheng, Weiyu Han, Shuai Chen, Tianyi Zhang, Xiao Tan, Jun Xia
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...
By Qinwen Yan
arXiv:2606. 24509v1 Announce Type: cross Abstract: Due to the wide use of graph-structured data in different fields of industry and science, the development of Graph Foundation Models (GFMs) has recently attracted a lot of attention.
By Oleg Platonov, Gleb Bazhenov, Dmitry Eremeev, Liudmila Prokhorenkova
arXiv:2607. 09528v1 Announce Type: new Abstract: The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior.
By Refat Ishrak Hemel, Ehsan Hallaji, Roozbeh Razavi-Far
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.
By Liming Liu, Chao Hu, Mingfei Lu, Yiwei Ge, Xingle Li, Heyuan Shi
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.
By Mariam Zakaria Moussa Ali