SAGE is the first end-to-end LLM-driven multi-agent fraud detection system using a Data Diagnostic Tree and MDP optimization, achieving 40.86% average F1 gain and winning 96% of comparisons across five datasets and five backbones.
A comprehensive survey on machine learning techniques and user authentication approaches for credit card fraud detection
2 Pith papers cite this work. Polarity classification is still indexing.
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Post-hoc learning to defer is cast as density-ratio learning between model and expert ideal distributions, producing DR CPE losses that recover Chow's rule for KL-based ideals and support adjustable deferral via thresholding.
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SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
SAGE is the first end-to-end LLM-driven multi-agent fraud detection system using a Data Diagnostic Tree and MDP optimization, achieving 40.86% average F1 gain and winning 96% of comparisons across five datasets and five backbones.
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Density-Ratio Losses for Post-Hoc Learning to Defer
Post-hoc learning to defer is cast as density-ratio learning between model and expert ideal distributions, producing DR CPE losses that recover Chow's rule for KL-based ideals and support adjustable deferral via thresholding.