Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques
P. Shyam · International Journal for Research in Applied Science and Engineering Technology · 2025
Credit card fraud remains a critical challenge in the financial industry due to the highly imbalanced nature of fraud detection datasets and the evolving tactics of fraudsters. This study proposes a robust framework for Credit Card Fraud Detection Using Ensemble (Stacking and Voting Classifiers) with Hybrid Techniques, integrating advanced resampling strategies with ensemble learning to enhance the detection of minority fraud cases.We evaluated various machine learning models combined with hybrid oversampling and undersampling methods, including Simple Minority Oversampling Technique(SMOTE)-Tomek, SMOTE Edited Nearest Neighbour(ENN), and Borderline-SMOTE (BSMOTE) with Tomek. Traditional classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LGBM) were benchmarked against ensemble approaches employing stacking and voting classifiers. Experimental results demonstrate that Voting Classifier consistently outperforms individual models, achieving the highest F1- score of 0.8634 and AUC of 0.9763 on the CreditCard dataset, and an F1-score of 0.8808 with AUC 0.9961 on the PaySim dataset. The Stacking Classifier also exhibits strong performance, particularly in reducing false positives, evidenced by its superior precision. These findings confirm that integrating hybrid sampling with ensemble models significantly enhances fraud detection capabilities, making the proposed approach effective for real-world financial fraud prevention systems. These results confirm that ensemble classifiers, when combined with appropriate hybrid resampling techniques, can significantly boost fraud detection performance by effectively balancing sensitivity and specificity. The proposed framework showcases the effectiveness of stacking and voting classifiers as part of a hybrid ensemble strategy, providing a reliable, scalable, and adaptable solution for real-world fraud detection systems where early and accurate identification of fraudulent transactions is paramount.