LLM-Enhanced Self-Evolving Reinforcement Learning for Multi-Step E-Commerce Payment Fraud Risk Detection

2025-09-23

AI Summary

In multi-step e-commerce payment fraud detection, conventional reinforcement learning approaches rely on manually designed reward functions, resulting in poor generalization across scenarios. Method: This paper proposes an LLM-driven multi-stage risk detection framework. By modeling the transaction process as a Markov Decision Process (MDP), it leverages large language models to autonomously generate and iteratively refine high-order logical reward functions—enabling fully automated, human-in-the-loop-free reward evolution. Contribution/Results: The framework exhibits zero-shot transfer capability, significantly enhancing cross-scenario adaptability and robustness. Long-term evaluation on real-world industrial data demonstrates substantial improvements in fraud detection accuracy while maintaining a low false positive rate and strong resilience to adversarial perturbations, confirming its practical deployability.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a novel approach to e-commerce payment fraud detection by integrating reinforcement learning (RL) with Large Language Models (LLMs). By framing transaction risk as a multi-step Markov Decision Process (MDP), RL optimizes risk detection across multiple payment stages. Crafting effective reward functions, essential for RL model success, typically requires significant human expertise due to the complexity and variability in design. LLMs, with their advanced reasoning and coding capabilities, are well-suited to refine these functions, offering improvements over traditional methods. Our approach leverages LLMs to iteratively enhance reward functions, achieving better fraud detection accuracy and demonstrating zero-shot capability. Experiments with real-world data confirm the effectiveness, robustness, and resilience of our LLM-enhanced RL framework through long-term evaluations, underscoring the potential of LLMs in advancing industrial RL applications.

Problem

• Optimizing multi-step e-commerce fraud detection using reinforcement learning
• Automating reward function design with LLMs to reduce human expertise dependency
• Improving fraud detection accuracy and robustness across payment stages

Innovation

• Integrating reinforcement learning with large language models
• Framing fraud detection as multi-step Markov decision process
• Using LLMs to iteratively refine reward functions