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SR-Fraud: An Outcome-Supervised Reflective LLM Agent Framework for Non-Stationary Payment Fraud Detection
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers propose SR-Fraud, an outcome-supervised reflective LLM agent framework to detect non-stationary payment fraud streams.
Inspect the evidence
- Inclusion basis
- AI in finance
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 24 September 2026
- Collected by OneBench
- 25 Sept 2026, 03:01 UK
Stored source excerpt
arXiv:2609.27287v1 Announce Type: new Abstract: Real-time payment fraud detection is a non-stationary streaming prediction problem: adversaries adapt before supervised labels mature, and localized burst attacks…
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