A Bayesian deep learning method for credit card fraud detection with uncertainty quantification

Qiming Yu; Qizhi Zhang; Xihan Cao; Tianlin Zhang; Jiawei He; Ruimin Wang; Zhengyi ma

doi:10.1117/12.2667363

22 February 2023 A Bayesian deep learning method for credit card fraud detection with uncertainty quantification

Qiming Yu, Qizhi Zhang, Xihan Cao, Tianlin Zhang, Jiawei He, Ruimin Wang, Zhengyi ma

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 1258728 (2023) https://doi.org/10.1117/12.2667363
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

With the changes of people’s consuming attitudes and the popularization of mobile payment, credit card seems increasingly indispensable in life. However, as the number of issued credit cards and credit lines is increasing, there emerges more and more fraud cases involving credit cards. Due to the rapid development of the Internet industry, the channels for capital flow have become unprecedentedly smooth, making it very difficult to prevent credit card fraud cases. If that continues, the development of banks and other financial institutions in the credit card field would be restricted, which might affect people's daily consumption and even the normal running of the society. The Bayesian Deep Learning method is used to quantify the uncertainty of credit card fraud prediction in this essay. Through experimental analysis, the accuracy of the model is over 99%. Compared with conventional classification models, this model has superior performance.

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Qiming Yu, Qizhi Zhang, Xihan Cao, Tianlin Zhang, Jiawei He, Ruimin Wang, and Zhengyi ma "A Bayesian deep learning method for credit card fraud detection with uncertainty quantification", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 1258728 (22 February 2023); https://doi.org/10.1117/12.2667363

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KEYWORDS

Deep learning

Data modeling

Education and training

Performance modeling

Random forests

Industry

Machine learning

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