Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection

• URL: http://arxiv.org/abs/2312.14406v1
• Date: Fri, 22 Dec 2023 03:15:17 GMT
• Title: Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection
• Authors: Ze Yu Zhao (1), Zheng Zhu (1), Guilin Li (1), Wenhan Wang (1), Bo Wang (1) ((1) Tencent, WeChat Pay)
• Abstract summary: Our model confronts token explosion and reconstructs behavioral sequences, providing a nuanced understanding of transactional behavior. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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