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 n 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.

Automated summary

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. Different methods based on artificial intelligence are proposed to secure credit cards in the academic paper. The proposed solution 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. The optimized XGBoost algorithm is used to classify fraudulent transactions, and the evaluation shows the superiority of the proposed approach compared to state-of-the-art machine learning models.