E-commerce platforms and payment solution providers face increasingly sophisticated fraud schemes, ranging from identity theft and account takeovers to complex money laundering operations that exploit the speed and anonymity of digital transactions. However, despite their theoretical promise, the application of Large Language Models (LLMs) to fraud detection in real-world financial contexts remains largely unexploited, and their practical effectiveness in handling domain-specific e-commerce transaction data has yet to be empirically validated. To bridge this gap between conventional machine learning limitations and the untapped potential of LLMs in fraud detection, this paper proposes a novel approach that employs Reinforcement Learning (RL) to post-train lightweight language models specifically for fraud detection tasks using only raw transaction data. We utilize the Group Sequence Policy Optimization (GSPO) algorithm combined with a rule-based reward system to fine-tune language models of various sizes on a real-life transaction dataset provided by a Chinese global payment solution company. Through this reinforcement learning framework, the language models are encouraged to explore diverse trust and risk signals embedded within the textual transaction data, including patterns in customer information, shipping details, product descriptions, and order history. Our experimental results demonstrate the effectiveness of this approach, with post-trained language models achieving substantial F1-score improvements on held-out test data. Our findings demonstrate that the observed performance improvements are primarily attributable to the exploration mechanism inherent in reinforcement learning, which allows models to discover novel fraud indicators beyond those captured by traditional engineered features.

翻译：电子商务平台与支付解决方案提供商面临着日益复杂的欺诈手段，包括身份盗用、账户接管以及利用数字交易速度与匿名性实施的复杂洗钱操作。然而，尽管大型语言模型在理论上具有巨大潜力，其在真实金融场景下欺诈检测中的应用仍未被充分探索，且其在处理特定领域电子商务交易数据时的实际有效性尚未得到实证验证。为弥合传统机器学习方法的局限性与大型语言模型在欺诈检测中尚未开发的潜力之间的鸿沟，本文提出一种创新方法：采用强化学习技术，仅使用原始交易数据对轻量级语言模型进行专门针对欺诈检测任务的后续训练。我们结合基于规则的奖励系统，采用组序列策略优化算法，基于一家中国全球支付解决方案公司提供的真实交易数据集，对不同规模的语言模型进行微调。通过该强化学习框架，语言模型能够深入探索文本化交易数据中蕴含的多样化信任与风险信号，包括客户信息、配送详情、商品描述和订单历史中的模式特征。实验结果表明该方法具有显著效果，经过后续训练的语言模型在预留测试数据上实现了F1分数的大幅提升。我们的研究证实，观察到的性能提升主要归因于强化学习固有的探索机制，该机制使模型能够发现超越传统人工设计特征所能捕捉的新型欺诈指标。