arXiv AI June 9, 2026 By Yichen Chen, Siying Li, Yuhang Liang, Lijun Wang, Renyang Liu

SAGE: An LLM-driven Self Reflective Agentic Framework for 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.

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

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

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

Sep 24

SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection

SR‑Fraud is a framework that uses a frozen, stateless LLM agent to score transactions in real time while an offline reflection agent proposes boundary hypotheses based on matured errors. The system then verifies these hypotheses deterministically before updating its knowledge state. On a production payment‑fraud benchmark, SR‑Fraud outperforms both static and periodically retrained CatBoost models and successfully detects an emerging fraud burst.

By Xuwei Tan, Yao Ma, Xueru Zhang

llms agents benchmarks

arXiv AI

Jul 28

Traceable LLM Reasoning for Fake-Order Fraud Detection

arXiv:2607. 23075v1 Announce Type: cross Abstract: Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rely on expert-designed features, produce black-box decisions, and provide limited interpretability.

By Siqi You, Bingsong Xu, Zhixian Zheng, Xinjian Peng, Yang Xie, Ying Wang, Jiarong Xu

llms reinforcement-learning fine-tuning safety

arXiv AI

Aug 18

FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection

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

benchmarks

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Related stories

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

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

SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection

Traceable LLM Reasoning for Fake-Order Fraud Detection

FinFraudBench: A Heterogeneous Graph Benchmark for Financial Fraud Detection