Predicting loan acceptance is a critical task for financial institutions, directly influencing risk management and decision-making processes. Traditional models often fall short in balancing predictive accuracy with interpretability, a challenge that becomes more pronounced in high-stakes environments like finance. This study employs XGBoost, a powerful gradient-boosting algorithm, to predict loan acceptance, achieving high accuracy while addressing the need for transparency in model predictions. To enhance interpretability, this study leverages Explainable AI (XAI) techniques, including SHAP, LIME, and ELI5, to provide a clear understanding of the factors driving the model’s decisions. Our approach not only improves predictive performance but also ensures that the decision-making process is transparent and trustworthy, thereby offering significant value to financial institutions in assessing credit risk.