Credit card fraud detection based on ensemble learning

Jingyi Yao; Chengying Zhu; Yifei Wang; Zhaoyang Liu; Qiming Yu

doi:10.1117/12.2659330

23 November 2022 Credit card fraud detection based on ensemble learning

Jingyi Yao, Chengying Zhu, Yifei Wang, Zhaoyang Liu, Qiming Yu

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 124540F (2022) https://doi.org/10.1117/12.2659330
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

With the development of the Internet industry and the rise of mobile payment, the risk prevention of credit card fraud has become more difficult. With the continuous expansion of the number of cards issued, the total amount of credit, and the transaction volume, the problem of credit card fraud has become increasingly prominent. This seriously affects the normal operation of financial institutions such as banks, threatens the property safety of users, and even directly threatens the normal operation of society. In this paper, after standardizing the data set, the SMOTE oversampling method is used for sample expansion and synthesis, so that the black and white samples are balanced, and the integrated algorithm is used to train a high-performance credit card fraud detection model. Through experimental analysis, the accuracy of the model is over 99.4%, and compared with the conventional classification model, the model has superior performance.

Citation Download Citation

Jingyi Yao, Chengying Zhu, Yifei Wang, Zhaoyang Liu, and Qiming Yu "Credit card fraud detection based on ensemble learning", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 124540F (23 November 2022); https://doi.org/10.1117/12.2659330

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