Credit Card Fraud Detection Based on Hyperparameters ‎Optimization Using the Differential Evolution

Due to the emigration of world business to the internet, credit ‎cards have become a tool for ‎payments for both online and outline purchases. However, fraudsters try ‎to attack those systems ‎using various techniques, and credit card fraud has become dangerous. To ‎secure credit cards, ‎different methods are proposed in the academic paper based on artificial ‎intelligence. The proposed ‎solution in this paper aims at combining the robustness of three methods: ‎the differential evolution ‎algorithm (DE) for selecting the best hyperparameters, a resampling ‎technique for handling ‎imbalanced data issues, and the XGBoost technique for classification. Finally, ‎the fraudulent ‎transactions are classified using the optimized XGBoost algorithm. The proposed ‎solution is ‎evaluated using two real-world datasets: the European dataset and the UCI dataset. The ‎evaluation ‎in terms of accuracy, sensitivity, specificity, precision, and F-measure shows the ability and ‎the ‎superiority of the proposed approach in comparison with the state-of-the-art machine learning ‎‎models.‎