Customer Transaction Fraud Detection Using Random Forest

被引：4

作者：

Du Shaohui ^{[1
]}

Qiu, GuanWen ^{[2
]}

Mai, Huafeng ^{[3
]}

Yu, Hongjun ^{[4
]}

机构：

[1] St Petersburg State Univ, St Petersburg, Saint Petersbur, Russia

[2] Univ Calif Irvine, Irvine, CA USA

[3] Univ Arizona, Tucson, AZ 85721 USA

[4] Beijing Foreign Studies Univ, Beijing, Peoples R China

来源：

2021 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS AND COMPUTER ENGINEERING (ICCECE) | 2021年

关键词：

Fraud detection; Data mining; Online Transaction;

D O I：

10.1109/ICCECE51280.2021.9342259

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In the evolution of the electronic money system, frequent transaction fraud has been a shadow behind the prosperity. It not only endangers the property security of users, but also hinders the development of digital finance in the world. With the development of data mining and machine learning, some mature technologies are gradually applied to the detection of transaction fraud. This paper proposes a transaction fraud detection model based on random forest. The experimental results of IEEE CIS fraud dataset show that the method of this model is better than the benchmark model, such as logistic regression, support vector machine. Finally, the accuracy of our model reached 97.4%, and the AUC ROC score was 92.7%.

引用

页码：144 / 147

页数：4

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