Credit Card Fraud Detection: A Selection of A Few Machine Learning Algorithms

The increasing dependence on digital financial transactions has coincided with a rise in credit card fraud, which necessitates the development of advanced detection mechanisms that surpass traditional, static rule-based systems. The study undertakes a comparative analysis of six supervised machine learning algorithms, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, k-Nearest Neighbours, and XGBoost, to detect fraud using a publicly available dataset. A central methodological focus was mitigating the extreme class imbalance, where fraudulent instances constitute less than 0.5% of the data, managed by applying the Synthetic Minority Over-sampling Technique (SMOTE). Ensemble methods, notably Random Forest and XGBoost, achieved superior and statistically significant results, nearing perfect classification, concluding that tree-based ensemble learners, when coupled with effective imbalance handling strategies, represent a highly dependable solution for modern credit card fraud detection.

Y. Zakari, A. A. Sidiq, H. Balogun, J. Magaji, I. A. Sadiq, A.S. Mohammed, A. Usman, J.Y. Kajuru, R. M. Raji, A. Hassan, M. Lukman, A.I. Ishaq, M. Tasiu (2026). Credit Card Fraud Detection: A Selection of A Few Machine Learning Algorithms. Journal of Statistical Sciences and Data Analytics. https://jossda.com.ng/journals/jossda.202601.00002