In this work, we introduce an innovative autoregressive model leveraging Generative Pretrained Transformer (GPT) architectures, tailored for fraud detection in payment systems. Our approach innovatively confronts token explosion and reconstructs behavioral sequences, providing a nuanced understanding of transactional behavior through temporal and contextual analysis. Utilizing unsupervised pretraining, our model excels in feature representation without the need for labeled data. Additionally, we integrate a differential convolutional approach to enhance anomaly detection, bolstering the security and efficacy of one of the largest online payment merchants in China. The scalability and adaptability of our model promise broad applicability in various transactional contexts.

介绍了一种创新的自回归模型，利用了Generative Pretrained Transformer (GPT)架构，专为支付系统中的欺诈检测而设计。我们的方法创新性地解决了令牌爆炸问题，并通过时间和上下文分析重构行为序列，提供了对交易行为的细致理解。利用无监督预训练，我们的模型在特征表示方面表现突出，无需有标签的数据。此外，我们整合了一种差分卷积方法来增强异常检测，提高了中国最大在线支付商的安全性和效果。我们模型的可扩展性和适应性在各种交易背景下有着广泛的适用性。

规模化生成预训练：基于Transformer的交易行为编码用于欺诈检测