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CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation

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Abstract

Establishing a reliable credit card fraud detection model has become a primary focus for academia and the financial industry. The existing anti-fraud methods face challenges related to low recall rates, inaccurate results, and insufficient causal modeling ability. This paper proposes a credit card fraud detection model based on counterfactual data enhancement of the triplet network. Firstly, we convert the problem of generating optimal counterfactual explanations (CFs) into a policy optimization of agents in the discrete–continuous mixed action space, thereby ensuring the stable generation of optimal CFs. The triplet network then utilizes the feature similarity and label difference of positive example samples and CFs to enhance the learning of the causal relationship between features and labels. Experimental results demonstrate that the proposed method improves the accuracy and robustness of the credit card fraud detection model, outperforming existing methods. The research outcomes are of significant value for both credit card anti-fraud research and practice while providing a novel approach to causal modeling issues across other fields.

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Acknowledgements

This study was supported by the Natural Science Foundation of Hunan Province of China(grant number 2022JJ30673), Key R &D Program of Hunan Province(grant number 2023DK2003), Foundation of Department of Science and Technology of Hunan Province(grant number 2022GK3003), and the Graduate Innovation Project of Central South University(2023XQLH032, 2023ZZTS0304).

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Author notes

  1. Menglin Kong and Ruichen Li have contributed equally to this work.

Authors and Affiliations

  1. School of Mathematics and Statistics, Central South University, Lushan Road, Changsha, 410083, Hunan, China

    Menglin Kong, Ruichen Li, Shengzhong Jin, Wanying Xie, Muzhou Hou & Cong Cao

  2. School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Renai Road, Suzhou, 215123, Jiangsu, China

    Jia Wang

  3. Peng Cheng Laboratory, Xingke Road, Shenzhen, 518000, Guangdong, China

    Xingquan Li

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  1. Menglin Kong
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  2. Ruichen Li
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  8. Cong Cao
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Contributions

MK contributed to conceptualization, methodology, software, validation, formal analysis, investigation, data curation, and writing an original draft. RL contributed to investigation, resources, and data curation. JW and WX performed data curation. XL and SJ contributed to software. MH contributed to resources, writing, and review editing. CC contributed to methodology, investigation, resources, data curation, writing, and review editing.

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Correspondence to Cong Cao.

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Kong, M., Li, R., Wang, J. et al. CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation. Neural Comput & Applic 36, 8607–8623 (2024). https://doi.org/10.1007/s00521-024-09546-9

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