Credit Card Fraud Detection Using Random Forest and XGBoost on a Public Kaggle Dataset
Keywords:
Credit Card Fraud, Random Forest, Xgboost, Class Imbalance, Ensemble LearningAbstract
Credit card fraud detection remains a critical challenge in digital financial ecosystems characterized by extreme class imbalance and evolving attack patterns. This study adopts an empirical experimental design to evaluate and compare Random Forest and XGBoost models using the publicly available Kaggle Credit Card Fraud Detection dataset. A controlled pipeline incorporating stratified data splitting, SMOTE-based imbalance mitigation, and GridSearchCV hyperparameter optimization was implemented to ensure methodological consistency and reproducibility. Performance was assessed through precision, recall, F1-score, ROC-AUC, confusion matrix analysis, and computational efficiency metrics. Results indicate that XGBoost outperformed Random Forest in recall, F1-score, and ROC-AUC, demonstrating enhanced minority-class discrimination and reduced false negatives under optimized conditions. Random Forest exhibited competitive precision and interpretability transparency, though with slightly lower sensitivity. Scalability evaluation confirmed that both models maintained low inference latency suitable for near-real-time deployment. The findings highlight the critical role of imbalance handling and parameter optimization in ensemble-based fraud detection and support boosting-oriented approaches as strategically advantageous for operational financial security systems.
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