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.

翻译：本文提出了一种创新的自回归模型，该模型利用生成式预训练Transformer架构，专为支付系统中的欺诈检测设计。我们的方法创新性地应对了Token爆炸问题并重构了行为序列，通过时间与上下文分析提供了对交易行为的细粒度理解。借助无监督预训练，该模型无需标注数据即可实现出色的特征表示能力。此外，我们集成了差分卷积方法以增强异常检测，为中国最大在线支付商户之一的安全性和有效性提供了支撑。该模型的扩展性与适应性使其在多种交易场景中具有广泛的应用前景。