Machine Learning‐Based Loan Approval Automation: Enhancing Efficiency, Accuracy and Fairness in Credit Decision‐Making

ABSTRACT Traditional loan approval processes are manual, time‐consuming and susceptible to human bias. This research develops a machine learning‐based system to automate loan eligibility assessment while enhancing efficiency, accuracy and fairness in credit decision‐making. We developed and compared multiple supervised ML models—including Random Forest, XGBoost, stacking and voting ensembles—on a publicly available loan dataset (614 instances, 13 features). To address class imbalance (68.7% approved, 31.3% rejected), we applied the Synthetic Minority Over‐sampling Technique (SMOTE). Hyperparameter tuning was performed using GridSearchCV with 5‐fold cross‐validation, optimising for F1‐score. Model performance was evaluated using accuracy, precision, recall, F1‐score and Area Under the Curve (AUC). Both Local Interpretable Model‐Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were applied to ensure transparency. A tuned Random Forest classifier achieved the best performance with an accuracy of 85.96%, F1‐score of 87.17% and recall of 95.32%, outperforming XGBoost and ensemble methods. Cross‐dataset validation on the Statlog German Credit dataset (1000 instances, 20 features) confirmed the framework's generalisability, with comparable AUC values (0.90 vs. 0.91). Overfitting analysis confirms moderate generalisation gaps, and a gender‐based fairness evaluation shows all models exceed the 0.80 disparate impact threshold. This study demonstrates that a systematic framework combining rigorous hyperparameter tuning, class imbalance handling and explainable AI can create accurate, transparent and equitable loan approval systems, providing a practical blueprint for responsible AI deployment in credit decision‐making.