@inproceedings{qu-etal-2025-llm,
    title = "{LLM}-Enhanced Self-Evolving Reinforcement Learning for Multi-Step {E}-Commerce Payment Fraud Risk Detection",
    author = "Qu, Bo  and
      Wang, Zhurong  and
      Yagi, Daisuke  and
      Xu, Zach  and
      Zhao, Yang  and
      Shan, Yinan  and
      Zahradnik, Frank",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-industry.9/",
    pages = "92--103",
    ISBN = "979-8-89176-288-6",
    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."
}