Enhancing credit card fraud detection using traditional and deep learning models with class imbalance mitigation

A key novelty of this work is the integration of focal loss within the deep learning framework, enabling the model to focus on hard-to-classify fraudulent transactions. Unlike many prior studies limited to the Kaggle dataset, our approach was validated on both the Kaggle credit card dataset and the PaySim synthetic mobile money dataset, demonstrating robustness and cross-domain generalizability. These findings highlight the effectiveness of combining data preprocessing, resampling techniques, and model optimization for robust fraud detection.