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AI Research
arXiv CS.AI·Jun 9·5 min read
SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection
TL;DR
Fraud detection in payment and e-commerce systems often struggles with accuracy, especially under class imbalance, and lacks transparency for decision-makers. SAGE, a self-reflective agentic framework driven by large language models (LLMs), was developed to address these issues.
✦ Why It Matters
Engineers can leverage SAGE to enhance fraud detection systems with improved accuracy and transparency.
Key Takeaways
- Explore SAGE for advanced fraud detection solutions.
- Utilize LLMs to improve decision-making in fraud scenarios.
- Leverage the Data Diagnostic Tree for structured analysis.
- Aim for higher F1 scores by adopting innovative frameworks.
How It Works
SAGE operates through a coordinated system of three dedicated agents that analyze data using a six-layer Data Diagnostic Tree (DDT). This structure allows for a nuanced understanding of the data, while a Markov decision process, guided by natural-language gradients, helps optimize the model's performance based on fraud-specific rewards.