Credit card fraud poses an escalating threat to the global financial ecosystem, causing billions of dollars in annual losses and eroding consumer trust. Effective automated fraud detection must contend with severe class imbalance, evolvingattack patterns, and the practical need for explainable, actionable predictions. In this paper, we present a rigorous comparative study of five machine learning classifiers—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and XGBoost—applied to a dataset of 50,000 credit card transactions exhibiting a realistic fraud rate of 0.34%. We evaluate the impact of two class-imbalanceremediation strategies (SMOTE oversampling and random undersampling), conduct threshold optimisation to align classification decisions with business economics, and employ SHAP (SHapley Additive exPlanations) values to provide model-level and instance-level interpretability. Our best model, Gradient Boosting, achieves a ROCAUCof0.9995, aPR-AUCof0.9421, andanF1scoreof0.7805underacost-optimised decision threshold of 0.75, translating into an estimated net business benefit of $4,228 per 10,000 transactions compared to a no-model baseline. Feature analysis identifies V27 (importance = 0.397) and V2 (0.213) as the dominant fraud signalsamong the PCA-derived features. This work demonstrates that ensemble gradient-boosted trees, combined with principled threshold tuning and SHAP explainability,constitute a production-ready solution for real-world fraud detection.