Credit Card Fraud Detection based on Ensemble Machine Learning Classifiers

Credit card is considered as one of the most popular paying methods for online and regular purchases, due to the advancement in communication and electronic commerce systems. Thus, the fraud associated with these transactions increased significantly. The great utilization of electronic payment is highly affected by this fraudulent transactions, which requires urgent detection to solve this issue. Therefore, effective and efficient approaches to detect fraud in credit card transactions are needed. To catch the fraudulent transaction, a good fitting model is needed, hence researchers recommends the use of various Machine Learning (ML) techniques, because of its beneficial characteristics. The main aim of the research work is to implement an ensemble based ML techniques for Credit Card Fraud Detection (CCFD). The strength of our model is a combination of the forces of the three subsystems; Recursive Feature Elimination (RFE), CCFD's using ensemble classifiers, and Synthetic Minority Oversampling (S MOTE) to deal with the problem of unbalanced data to identify the most effective prediction features. The proposed model run typical tests on two real databases of public credit card transactions, including fraudulent and official ones. Based on the comparison of other ML methods, the extra tree classifier has performed better and achieved high efficiencies such as 96% of accuracy and 57.95% of F1-measure.