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
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.
Evi-VN is a new framework for graph neural network (GNN) fraud detection that injects evidence from diverse modalities—structured records, text, images, and audio—into virtual node representations. It targets shared hard regions where standard GNNs fail to distinguish well‑disguised fraudsters from legitimate users, correcting blind spots without disrupting reliable predictions. Experiments on bot, fake‑review, refund‑evidence, and telecom‑fraud datasets demonstrate its effectiveness across generic, fraud‑specific, and unseen GNNs.
By Jiran Tao, Yifan Wu, Binyan Jiang
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...
By Sergei, Komarov
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.
By Xin Liu, Xiyuan Chen, Chenglong Wu, Xuan Zong, Jun Zhou, Dawei Cheng