Bank Loan Default Prediction through Machine Learning Algorithms

Majority of banks and lending institutions are updating their business models because of modern technology. For decision-making to be transparent, credit risk forecasts, and efficient loan processing are essential. This research examines different predictive models for default prediction of loans using the comprehensive dataset from Kaggle and looks at the predictability of five machine learning algorithms. Various demographic and loan-specific characteristics that have a substantial effect on loan default rates are studied. Using Random Forest, K-Nearest Neighbor, Naïve Bayes, Logistic Regression, and Decision Tree classification methods, this paper assesses their performances based on metrics like accuracy, F1 score, recall, precision, area under the receiver operating characteristic curve, and root mean square error. We used qualitative exploratory data analysis to examine 255,347 loan applications with different borrower’s indices, including encoding categorical variables, normalizing predictors, and handling the inherent class imbalance via SMOTE-ENN. Based on the above-mentioned evaluation metrics, Random Forest emerged as the most robust model achieving highest accuracy and least root mean square error. These results highlight certain attributes in predicting loan defaults, offering worthwhile information to credit risk managers to enhance default risk prediction accuracy through machine learning. This study provides valuable and efficient policy implications that would significantly help banks and financial institutions in mitigating loan default risk.