Abstract
With the advancement of e-commerce and modern technological development, credit cards are widely used for both online and offline purchases, which has increased the number of daily fraudulent transactions. Many organizations and financial institutions worldwide lose billions of dollars annually because of credit card fraud. Due to the global distribution of both legitimate and fraudulent transactions, it is difficult to discern between the two. Furthermore, because only a small proportion of transactions are fraudulent, there is a problem of class imbalance. Hence, an effective fraud-detection methodology is required to sustain the reliability of the payment system. Machine learning has recently emerged as a viable substitute for identifying this type of fraud. However, ML approaches have difficulty identifying fraud with high prediction accuracy, while also decreasing misclassification costs due to the size of the imbalanced data. In this research, a soft voting ensemble learning approach for detecting credit card fraud on imbalanced data is proposed. To do this, the proposed approach is evaluated and compared with numerous sophisticated sampling techniques (i.e., oversampling, undersampling, and hybrid sampling) to overcome the class imbalance problem. We develop several credit card fraud classifiers, including ensemble classifiers, with and without sampling techniques. According to the experimental results, the proposed soft-voting approach outperforms individual classifiers. With a false negative rate (FNR) of 0.0306, it achieves a precision of 0.9870, recall of 0.9694, f1-score of 0.8764, and AUROC of 0.9936.