Gated attention based generative adversarial networks for imbalanced credit card fraud detection
Credit card fraud detection is highly important to maintain financial security. However, it is challenging to train suitable models due to the class imbalance in credit card transaction data. To address this issue, this work proposes a novel deep learning framework, gated attention-based generative...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-06-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2972.pdf |
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| _version_ | 1850111947886821376 |
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| author | Jiangmeng Ge Lanxiang Yin Shiqing Zhang Xiaoming Zhao |
| author_facet | Jiangmeng Ge Lanxiang Yin Shiqing Zhang Xiaoming Zhao |
| author_sort | Jiangmeng Ge |
| collection | DOAJ |
| description | Credit card fraud detection is highly important to maintain financial security. However, it is challenging to train suitable models due to the class imbalance in credit card transaction data. To address this issue, this work proposes a novel deep learning framework, gated attention-based generative adversarial networks (GA-GAN) for credit card fraud detection in class-imbalanced data. GA-GAN integrates GAN and the gated attention mechanism to generate high-quality synthetic data that realistically simulates fraudulent behaviors. Experimental results on two public credit card datasets demonstrate that GA-GAN outperforms state-of-the-art methods on credit card fraud detection tasks in class-imbalanced data, indicating the advantage of GA-GAN. The code is publicly available at https://github.com/Gejiangmeng/gagan/tree/main. |
| format | Article |
| id | doaj-art-e529ff02bbaf4af8ae2742d916914871 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e529ff02bbaf4af8ae2742d9169148712025-08-20T02:37:30ZengPeerJ Inc.PeerJ Computer Science2376-59922025-06-0111e297210.7717/peerj-cs.2972Gated attention based generative adversarial networks for imbalanced credit card fraud detectionJiangmeng Ge0Lanxiang Yin1Shiqing Zhang2Xiaoming Zhao3Institute of Intelligent Information Processing, Taizhou University, Taizhou, Zhejiang, ChinaSchool of Business, Taizhou University, Taizhou, Zhejiang, ChinaInstitute of Intelligent Information Processing, Taizhou University, Taizhou, Zhejiang, ChinaInstitute of Intelligent Information Processing, Taizhou University, Taizhou, Zhejiang, ChinaCredit card fraud detection is highly important to maintain financial security. However, it is challenging to train suitable models due to the class imbalance in credit card transaction data. To address this issue, this work proposes a novel deep learning framework, gated attention-based generative adversarial networks (GA-GAN) for credit card fraud detection in class-imbalanced data. GA-GAN integrates GAN and the gated attention mechanism to generate high-quality synthetic data that realistically simulates fraudulent behaviors. Experimental results on two public credit card datasets demonstrate that GA-GAN outperforms state-of-the-art methods on credit card fraud detection tasks in class-imbalanced data, indicating the advantage of GA-GAN. The code is publicly available at https://github.com/Gejiangmeng/gagan/tree/main.https://peerj.com/articles/cs-2972.pdfCredit card fraud detectionDeep learningGenerative adversarial networksAttention mechanismClass imbalance |
| spellingShingle | Jiangmeng Ge Lanxiang Yin Shiqing Zhang Xiaoming Zhao Gated attention based generative adversarial networks for imbalanced credit card fraud detection PeerJ Computer Science Credit card fraud detection Deep learning Generative adversarial networks Attention mechanism Class imbalance |
| title | Gated attention based generative adversarial networks for imbalanced credit card fraud detection |
| title_full | Gated attention based generative adversarial networks for imbalanced credit card fraud detection |
| title_fullStr | Gated attention based generative adversarial networks for imbalanced credit card fraud detection |
| title_full_unstemmed | Gated attention based generative adversarial networks for imbalanced credit card fraud detection |
| title_short | Gated attention based generative adversarial networks for imbalanced credit card fraud detection |
| title_sort | gated attention based generative adversarial networks for imbalanced credit card fraud detection |
| topic | Credit card fraud detection Deep learning Generative adversarial networks Attention mechanism Class imbalance |
| url | https://peerj.com/articles/cs-2972.pdf |
| work_keys_str_mv | AT jiangmengge gatedattentionbasedgenerativeadversarialnetworksforimbalancedcreditcardfrauddetection AT lanxiangyin gatedattentionbasedgenerativeadversarialnetworksforimbalancedcreditcardfrauddetection AT shiqingzhang gatedattentionbasedgenerativeadversarialnetworksforimbalancedcreditcardfrauddetection AT xiaomingzhao gatedattentionbasedgenerativeadversarialnetworksforimbalancedcreditcardfrauddetection |