Adaptive Ensemble Technique Based Robust Loan Risk Prediction for Financial Systems

Precisely evaluating loan risk is essential for maintaining financial stability and profitability. This study enhances risk modeling through a comprehensive analysis that is not reliant on particular datasets, thus ensuring broader applicability. Machine learning and ensemble learning techniques are assessed using accuracy, precision, recall, ROC score, and F1 score to identify the most adequate model for forecasting loan defaults. This study identifies critical financial and personal characteristics necessary for effective risk assessment, thereby improving the storage and processing of borrower data. SHapley Additive exPlanations (SHAP) enhance model interpretability by providing insights into feature significance and fostering transparency in financial decision-making. The results underscore the importance of feature selection in improving data efficiency and preserving model effectiveness. This study sets a standard for effective and transparent loan risk assessment by integrating strong preprocessing, performance evaluation, and interpretability frameworks, thus fostering equitable and efficient lending practices in resource-limited settings.