SAGE: New LLM-Based Framework Achieves High Performance in Fraud Detection
Researchers have developed SAGE, an artificial intelligence framework that uses large language models to detect fraud in payment, e-commerce, and telecommunications systems. The system coordinates multiple AI agents using a decision-making structure called a Data Diagnostic Tree and achieves an average 40.86% improvement in F1 scores compared to existing methods. This work addresses a critical need for fraud detection systems that are both accurate and interpretable to human risk managers.
SAGE is a multi-agent framework designed to overcome limitations in existing fraud detection approaches. While automated machine learning systems lack semantic awareness and graph neural network methods require pre-defined relationships and lack transparency, SAGE uses large language models to coordinate three specialized agents that make decisions based on a six-layer Data Diagnostic Tree and a Markov decision process guided by natural-language gradients. The framework automatically optimizes itself using fraud-specific reward signals. Testing across five fraud datasets and five different LLM backbones, SAGE outperformed baseline methods in 96% of comparisons, with an average F1 improvement of 40.86%. The researchers have made their code publicly available on GitHub.
What's missing
The paper does not discuss computational costs or inference latency compared to baseline methods, which are important practical considerations for real-world deployment. Additionally, the study does not address potential adversarial robustness or how the framework performs against novel fraud patterns not seen during training.
What different sources said
- arXiv cs.AICenter
SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
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