A Comparative Analysis of Machine Learning, Ensemble Learning, Deep Learning, and Explainable Artificial Intelligence Techniques for Credit Card Fraud Detection
Keywords:
Credit Card Fraud Detection, Machine Learning, Graph Neural Network, Federated Learning, Explainable AI, Class Imbalance, Real-time DetectionAbstract
The rapid digitization of financial transactions has led to a parallel surge in credit cardfraud, causing annual global losses exceeding $32 billion. Traditional rule-basedsystems are inadequate for detecting adaptive fraud patterns, while machine learningand deep learning models struggle with severe class imbalance, lack of transparency, and data privacy constraints.
References
1. A. C. Bahnsen et al., "Feature engineering strategies for credit card fraud detection," Expert Syst. Appl., vol. 51, pp. 134-142, 2016.
2. A. Srivastava and R. Gupta, "Credit Card Fraud Detection Using Neural Network and XGBoost," Proc. ICMLDS, pp. 45-52, 2019.
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Published
2024-08-20
How to Cite
Andrews Felicita S Dr. Grasha Jacob. (2024). A Comparative Analysis of Machine Learning, Ensemble Learning, Deep Learning, and Explainable Artificial Intelligence Techniques for Credit Card Fraud Detection. Journal of Computational Analysis and Applications (JoCAAA), 33(4), 1159–1172. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5740
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