Generative Pretraining at Scale: Transformer-Based Encoding of
  Transactional Behavior for Fraud Detection
Ze Yu Zhao Zheng Zhu Guilin Li Wenhan Wang Bo Wang
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.