Open Access
ARTICLE
A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network
Yalong Xie1, Aiping Li1,*, Biyin Hu2, Liqun Gao1, Hongkui Tu1
1
College of Computer, National University of Defense Technology, Changsha, 410003, China
2
Credit Card Department, Bank of Changsha, Changsha, 410016, China
* Corresponding Author: Aiping Li. Email:
Computers, Materials & Continua 2023, 76(3), 2707-2726. https://doi.org/10.32604/cmc.2023.037039
Received 20 October 2022; Accepted 06 January 2023; Issue published 08 October 2023
Abstract
Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and
safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card
transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification
models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules:
a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space, and a balance module based on the generative adversarial network to decrease the class
imbalance ratio. The effectiveness of the MFGAN model is validated on two actual credit card datasets. The impacts
of different class balance ratios on the performance of the four resampling models are analyzed, and the contribution
of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.
Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of
recall, F1, and Area Under the Curve (AUC) metrics, which means that the MFGAN model can help banks find
more fraudulent transactions and reduce fraud losses.
Keywords
Cite This Article
APA Style
Xie, Y., Li, A., Hu, B., Gao, L., Tu, H. (2023). A credit card fraud detection model based on multi-feature fusion and generative adversarial network. Computers, Materials & Continua, 76(3), 2707-2726. https://doi.org/10.32604/cmc.2023.037039
Vancouver Style
Xie Y, Li A, Hu B, Gao L, Tu H. A credit card fraud detection model based on multi-feature fusion and generative adversarial network. Comput Mater Contin. 2023;76(3):2707-2726 https://doi.org/10.32604/cmc.2023.037039
IEEE Style
Y. Xie, A. Li, B. Hu, L. Gao, and H. Tu "A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network," Comput. Mater. Contin., vol. 76, no. 3, pp. 2707-2726. 2023. https://doi.org/10.32604/cmc.2023.037039