Credit Card Fraud Detection Using Stacking Ensemble of Deep Learning Model
Mrs. R. Jayalakshmi · International Journal for Research in Applied Science and Engineering Technology · 2025
Financial protection through credit card fraud detection demands sophisticated techniques to properly identify fraudulent payments among all transactions. Modern fraudulent activities create substantial hurdles for existing detection systems because fraudulent transactions remain sparse in relation to ordinary transactions. This research paper puts forth an improved fraud detection method by implementing a hybrid SMOTEENN resampling approach within a stacking ensemble system. A stacking ensemble model integrates Long Short-Term Memory (LSTM) networks together with Random Forest as its base learners, while utilizing a Multi-Layer Perceptron (MLP) to serve as the meta-learning model. The proposed detection system produces enhanced results through time pattern analysis and efficient treatment of unbalanced data distribution. The experimental trials prove the system's resilience and its result exceeds traditional machine learning models for reliable fraud act detection.