← Back to all news
arXiv Machine Learning October 21, 2026 By Mythreyee Hari, Subin Sahayam, Vegesna S M Srinivasavarma

RGH-GNN : Risk-Gated Heterogeneous Graph Neural Network for Financial Fraud Detection

Read the original on arXiv Machine Learning →

The Flow has not summarised this story yet — read it at arXiv Machine Learning.

  • benchmarks

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

Hugging Face Trending Papers
Aug 3

Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning

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.

diffusionsafety
More like this →
arXiv Machine Learning
Aug 4

Empowering Credit Risk Detection in Weixin Pay with Billion-Scale Deep Graph Learning

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
diffusionsafety
More like this →
arXiv AI
Jun 18

TMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network

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.

By Rohit Tewari, Shubhankar Shilpi, Navin Chhibber, Devendra Singh Parmar, Sunil Khemka, Piyush Ranjan
More like this →
arXiv Machine Learning
Jul 14

A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning

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
benchmarks
More like this →
Hugging Face Trending Papers
Jul 13

A Novel Graph Fraud Detector via Grouped Attribute Completion and Confidence-Aware Contrastive Learning

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.

benchmarks
More like this →
arXiv AI
Jun 29

Beyond Sparse Supervision: Diffusion-Guided Learning for Few-Shot Graph Fraud Detection

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
diffusionbenchmarks
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea