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AI-Driven Fraud Detection in Payments Using Transformer-Based Behavioral Modeling

Author(s) Abhigyan Mukherjee
Country United States
Abstract As digital payments got mainstream, fraudsters have been getting smarter. Digital payment fraud detection gets powered by AI. In this paper we propose a Transformer-based fraud detection framework based on Generative Pretrained Transformers (GPT) to encode long-term transactional behavior sequences. Based on a very large payment data, we pretrained our model to reconstruct behavior and identify anomalies in real time. Our strategy employs unsupervised pretraining, differential convolutional operations and contrastive learning for anomaly detection without requiring labeled data. Most of the work is evaluated on real transaction data from WeChat Pay, one of the largest payment platforms in China. The proposed model achieves better accuracy than traditional machine learning. According to the findings, the implementation of AI-enhanced behavioral analysis on payment transactions effectively identifies high-risk consumers or frauds while minimizing false positives and securing transactions. The paper demonstrates the advantages of deep learning in financial fraud prevention through an empirical evaluation and presents a scalable approach for real-time fraud detection in payment ecosystems.
Keywords AI-driven fraud detection, Transformer-based modeling, Behavioral sequence analysis, Unsupervised learning, Contrastive learning, Few-shot learning, financial transaction data, Anomaly detection, Deep learning in finance, Payment risk management, Autoregressive models, Data compression in behavioral modeling, Self-supervised pre-training, Convolutional neural networks (CNN), Real-time fraud prevention.
Field Engineering
Published In Volume 7, Issue 5, May 2026
Published On 2026-05-06
DOI https://doi.org/10.70528/IJLRP.v7.i5.2265
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