CUS-RF-Based Credit Card Fraud Detection with Imbalanced Data

With the continuous expansion of the banks' credit card businesses, credit card fraud has become a serious threat to banking financial institutions. So, the automatic and real-time credit card fraud detection is the meaningful research work. Because machine learning has the characteristics of non-linearity, automation, and intelligence, so that credit card fraud detection can improve the detection efficiency and accuracy. In view of this, this paper proposes a credit card fraud detection model based on heterogeneous ensemble, namely CUS-RF (cluster-based under-sampling boosting and random forest), based on clustering under-sampling and random forest algorithm. CUS-RF-based credit card fraud detection model has the following advantages. Firstly, the CUS-RF model can better overcome the issue of data imbalance. Secondly, based on the idea of heterogeneous ensemble learning, the clustering under-sampling method and random forest model are fused to achieve a better performance for credit card fraud detection. Finally, through the verification of real credit card fraud dataset, the CUS-RF model proposed in this paper has achieved better performance in credit card fraud detection compared with the benchmark model.