Identifying Fraudulent Credit Card Transactions Using Ensemble Learning

Abstract

One major challenge for banks and financial institutions is identifying fraudulent credit card transactions, as criminals can also masquerade as legitimate cardholders. To address the inherent class imbalance problem in a fraud detection problem, resampling techniques such as oversampling, undersampling, and SMOTE are applied to the datasets that include European Data and Sparkov Data. To improve the classification, ensemble learning is employed to select different algorithms to make it more accurate and reliable. The study indicates the use of an ensemble-based method with advanced resampling techniques to improve the training and forecasting of the models. The comparison of several classification models is done in great detail and the best model is found to be the Stacking Classifier. It combines multiple base models to enhance the accuracy, precision, recall and F1 score of all techniques. This can prove to be a major step in enhancing fraud detection systems, providing accurate identification of fraudulent transactions and fewer false positives. The proposed solution highlights the importance of ensemble methods and data balancing techniques for tackling the complexity in financial fraud detection.