arXiv AI July 10, 2026 By Rongkun Cui, Nana Zhang, Kun Zhu, Qi Zhang

Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud

arXiv:2601. 11073v3 Announce Type: replace-cross Abstract: Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Sep 15

Graph-Transformer Fraud Detection with Self-Supervised Pretraining and Conformal Risk Control

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

llms multimodal benchmarks safety

arXiv AI

Jul 23

Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience

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

benchmarks safety

arXiv Machine Learning

Jul 13

TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems

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

multimodal benchmarks

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.

diffusion safety

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

diffusion benchmarks

arXiv AI

Jul 22

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

arXiv:2607. 19266v1 Announce Type: cross Abstract: Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable.

By Rahil Sharma

llms agents

One email a morning, machine-written

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Graph-Transformer Fraud Detection with Self-Supervised Pretraining and Conformal Risk Control

Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience

TSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems

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

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

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation