Abstract

Financial fraud in online credit card transactions poses significant challenges due to its increasing prevalence and the highly imbalanced nature of transactional data. This paper proposes a hybrid deep learning framework combining one-dimensional convolutional neural networks (CNN) and gated recurrent units (GRU) to effectively capture both spatial and temporal features of transaction sequences. Bayesian optimization is employed to fine-tune the model’s hyperparameters, improving detection performance without relying on synthetic oversampling. Evaluated on the widely-used European credit card fraud dataset, the proposed CNN-GRU model achieves superior results with an accuracy of 0.9996, an AUC-ROC of 0.9693, and an AUC-PR of 0.8709. These findings highlight the model’s robustness in identifying rare fraudulent transactions, outperforming several state-of-the-art methods and demonstrating the practical utility of deep learning combined with Bayesian optimization in fraud detection.

Authors

Srikar Ayyagari, Sai Shyam
Sri Sathya Sai Institute of Higher Learning, India

Keywords

Financial Fraud, Credit Card Transactions, Transactional Data, Convolutional Neural Network