Abstract: Credit card fraud detection has emerged as a crucial area of study, especially with the rise in online transactions coupled with increased financial losses from fraudulent activities. In this regard, a refined framework for identifying credit card fraud is introduced, utilizing a stacking ensemble model along with hyperparameter optimization. This paper integrates three highly effective algorithms—XGBoost, CatBoost, and Light-GBM—into a single strategy to improve predictive performance and address the issue of unbalanced datasets. To enable a more efficient search and adjustment of model parameters, Bayesian Optimization is employed for hyperparameter tuning. The proposed approach has been tested on a publicly accessible dataset. Results indicate notable enhancements over established baseline models in essential performance metrics, including ROC-AUC, precision, and recall. This method, while effective in fraud detection, holds significant promise for other fields focused on identifying rare occurrences.

El Bazi Abdelghafour, Chrayah Mohamed, Aknin Noura and Bouzidi Abdelhamid, “Enhancing Credit Card Fraud Detection Using a Stacking Model Approach and Hyperparameter Optimization” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01510110

@article{Abdelghafour2024,
title = {Enhancing Credit Card Fraud Detection Using a Stacking Model Approach and Hyperparameter Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01510110},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01510110},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {El Bazi Abdelghafour and Chrayah Mohamed and Aknin Noura and Bouzidi Abdelhamid}
}