Credit card transaction fraud detection based on DB-SVMSmote-ANN

Tianbao Xie; Shaofan Liu; Yanxin Li

doi:10.1117/12.3050686

27 September 2024 Credit card transaction fraud detection based on DB-SVMSmote-ANN

Tianbao Xie, Shaofan Liu, Yanxin Li

Author Affiliations +

Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132810F (2024) https://doi.org/10.1117/12.3050686
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China

Abstract

In response to the issues of extremely unbalanced sample distribution, high-dimensional samples, and large volumes of data in the problem of credit card transaction fraud detection, this paper proposes a hybrid model that combines DBSCAN, SVMSmote, and Artificial Neural Networks (ANN), namely the DB-SVMSmote-ANN model. This model can generate samples of the minority class, addressing the issue of too few positive samples in credit card transaction fraud datasets. Subsequently, the balanced dataset is used to train the ANN classification model, solving the classification problem between fraudulent and normal transaction samples. In the experimental process of this paper, the differences between balanced and unbalanced datasets were first compared, followed by a comparison of the effects of using the DBSVMSmote-ANN and other classification models. Ultimately, the experiments demonstrated that the DB-SVMSmoteANN model can excellently solve the problem of credit card transaction fraud detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Tianbao Xie, Shaofan Liu, and Yanxin Li "Credit card transaction fraud detection based on DB-SVMSmote-ANN", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132810F (27 September 2024); https://doi.org/10.1117/12.3050686

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