An Optimized Ensemble Framework with Explainable AI for Proactive Credit Card Fraud Detection in Banking
: This study proposes an optimized ensemble framework for highly imbalanced credit card fraud detection that jointly maximizes predictive sensitivity and model interpretability. While machine learning offers significant potential for high-dimensional financial data analysis, its adoption is often hindered by extreme data sparsity and the inherent “black-box” nature of complex ensemble architectures. To bridge this gap, we develop a comprehensive pipeline incorporating six high-performance classifiers, fine-tuned via the Optuna Bayesian optimization framework. Leveraging a benchmark European dataset with a 0 . 172% fraud rate, our methodology employs a hybrid strategy of synthetic oversampling and cost-sensitive learning, integrated with SHapley Additive exPlanations (SHAP) to deconstruct complex decision boundaries. Empirical findings indicate that XGBoost delivers a superior Precision-Recall Area Under the Curve (PR-AUC) of 0 . 8755, while the Random Forest (RF) model achieves an optimal F 2 -Score (0 . 8673), effectively minimizing undetected fraudulent events. Our analysis further identifies time-of-day signals in the engineered temporal representation as relevant indicators of malicious activity. This work provides a mathematically grounded benchmarking framework for integrating Explainable Artificial Intelligence (XAI) into fraud detection pipelines, aligning high-accuracy analytics with the transparency requirements expected in regulated financial environments.