an ensemble learning framework for credit card fraud detection using machine learning and deep learning

The rapid growth of digital payment systems has heightened the need for accurate and scalable methods to detect credit card fraud. this study evaluates a range of machine learning and deep learning algorithms, including logistic regression, decision tree, random forest, k-nearest neighbors (knn), xgboost, convolutional neural networks (cnn), baseline mlp (multi-layer perceptron), and long short-term memory (lstm), to identify effective approaches for detecting fraudulent transactions. based on comparative analysis, random forest and lstm achieved the strongest individual performance, with accuracies exceeding 96%. building on these findings, a stacking ensemble model was constructed by integrating random forest and lstm as base learners and logistic regression as the meta-classifier. the framework incorporates convolutional autoencoder (cae) for feature extraction and random undersampling (rus) with three resampling ratios (1:1, 1:5, and 1:10) to address class imbalance. experimental results show that the ensemble model provides improved predictive accuracy compared with individual algorithms, achieving an accuracy of 99.98%, precision of 99.86%, and recall of 99.89% under a 1:10 resampling ratio. rather than proposing a new algorithmic architecture, this study contributes a systematic and unified evaluation of widely used ml and dl approaches and demonstrates the effectiveness of integrating cae, rus, and a random forest–lstm stacking ensemble in enhancing fraud detection performance.