A voting ensemble machine learning based credit card fraud detection using highly imbalance data

Long gone is the time when people preferred using only cash. In recent years, cashless transactions have gained much popularity, be it using UPI apps or credit and debit cards. The same has even led to a significant increase in the number of credit card fraud cases. Detecting fraudulent transactions is a challenging task as the fraudsters disguise the ordinary conduct of clients in order to perform fraud. Automated intelligent credit card fraud detection can be employed for detecting fraudulent transactions. In this paper, we proposed a credit card fraud detection approach involving an arrangement of supervised machine learning algorithms called ensemble learning. One of the difficulties looked at during the time spent to distinguish fraud transactions in datasets is the imbalanced class distribution. In this work, we employed an ensemble learning model in combination with two data-level techniques for handling class imbalance problems. The proposed approach is the ensemble of three base classifiers including random forest, logistic regress and K-nearest neighbour along with two data-level algorithms namely random oversampling and random undersampling. To combine the predictions of the base classifiers, the weighted voting ensemble approach is used. The proposed approach is evaluated using a highly imbalanced credit card transaction dataset. The proposed approach is evaluated using various sets of weights in order to identify the best possible outcomes in terms of accuracy and minimise the misclassification of fraudulent transactions.