Open Access

ARTICLE

Credit Card Fraud Detection Method Based on RF-WGAN-TCN

Ao Zhang¹, Hongzhen Xu^1,*, Ruxin Liu²

1 School of Software, East China University of Technology, Nanchang, 330013, China
2 School of Information Engineering, East China University of Technology, Nanchang, 330013, China

* Corresponding Author: Hongzhen Xu. Email:

Computers, Materials & Continua 2025, 85(3), 5159-5181. https://doi.org/10.32604/cmc.2025.067241

Received 28 April 2025; Accepted 15 August 2025; Issue published 23 October 2025

Abstract

Credit card fraud is one of the primary sources of operational risk in banks, and accurate prediction of fraudulent credit card transactions is essential to minimize banks’ economic losses. Two key issues are faced in credit card fraud detection research, i.e., data category imbalance and data drift. However, the oversampling algorithm used in current research suffers from excessive noise, and the Long Short-Term Memory Network (LSTM) based temporal model suffers from gradient dispersion, which can lead to loss of model performance. To address the above problems, a credit card fraud detection method based on Random Forest-Wasserstein Generative Adversarial Network-Temporal Convolutional Network (RF-WGAN-TCN) is proposed. First, the credit card data is preprocessed, the feature importance scores are calculated by Random Forest (RF), the features with lower importance are eliminated, and then the remaining features are standardized. Second, the Wasserstein Distance Improvement Generative Adversarial Network (GAN) is introduced to construct the Wasserstein Generative Adversarial Network (WGAN), the preprocessed data is input into the WGAN, and under the mutual game training of generator and discriminator, the fraud samples that meet the target distribution are obtained. Finally, the temporal convolutional network (TCN) is utilized to extract the long-time dependencies, and the classification results are output through the Softmax layer. Experimental results on the European cardholder dataset show that the method proposed in the paper achieves 91.96%, 98.22%, and 81.95% in F1-Score, Area Under Curve (AUC), and Area Under the Precision-Recall Curve (AUPRC) metrics, respectively, and has higher prediction accuracy and classification performance compared with existing mainstream methods.

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