Credit Card Fraud Detection Using Selective Class Sampling and Random Forest Classifier

The incidences of credit card fraud are increasing. It puts the hard-earned money of users at risk. The financial institutions and governing bodies are looking forward to some robust and reliable ways of detecting credit card fraudulent transactions. The task is challenging due to non-availability of enough data, high class imbalance, and high stake on false negative rates (FNR). Most of the methods available in literature perform well on the accuracy-based performance metrics. However, they fail to yield satisfactory ROC-AUC performance due to the high false positive or false negative rates. Mitigating this issue is the real challenge for the task of fraudulent credit card transaction detection. This paper investigates the problem of credit card fraudulent transaction detection and proposes a technique for it. The proposed method uses a custom selective class sampling-based class balancing technique, and subsequently, it uses random forest for classification. The experimental results show that the proposed technique has better AUC score, accuracy, precision, and recall values as compared with other similar approaches.