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 m...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2024-01, Vol.15 (10)
Main Authors: Abdelghafour, El Bazi, Mohamed, Chrayah, Noura, Aknin, Abdelhamid, Bouzidi
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Computer science
Credit card fraud
Datasets
Fraud prevention
Genetic algorithms
International finance
Machine learning
Optimization
Optimization techniques
Parameter identification
Performance measurement
Regression analysis
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Summary: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2024.01510110