A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection

This research introduces an innovative method for identifying credit card fraud by combining the SMOTE-KMEANS technique with an ensemble machine learning model. The proposed model was benchmarked against traditional models such as logistic regression, decision trees, random forests, and support vector machines. Performance was evaluated using metrics, including accuracy, recall, and area under the curve (AUC). The results demonstrated that the proposed model achieved superior performance, with an AUC of 0.96 when combined with the SMOTE-KMEANS algorithm. This indicates a significant improvement in detecting fraudulent transactions while maintaining high precision and recall. The study also explores the application of different oversampling techniques to enhance the performance of various classifiers. The findings suggest that the proposed method is robust and effective for classification tasks on balanced datasets. Future research directions include further optimization of the SMOTE-KMEANS approach and its integration into existing fraud detection systems to enhance financial security and consumer protection.