Enhancing Credit Card Fraud Detection Using a Stacking Model Approach and Hyperparameter Optimization

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.

@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},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {El Bazi Abdelghafour and Chrayah Mohamed and Aknin Noura and Bouzidi Abdelhamid},
  doi       = {10.14569/IJACSA.2024.01510110},
  url       = {https://doi.org/10.14569/IJACSA.2024.01510110}
}