Mitigating Risk of Bank Loans by Predicting Maximum Loan Amount and Approval using Machine Learning

Abstract

ABSTRACT In current financial system banks play major role, they are engaged in provision of liquidity to the entire economy. Bank loan represent the significant source for these bank and financial institutions and loan approval process plays, a critical role in economic stability and growth. Accurate bank loan prediction is crucial for financial institutions to assess customer creditworthiness and minimized the default risk. Rejecting a loan request without providing the maximum approvable amount can lead to customer loss. The aim of this study is to mitigate the risk of loan approval process in a bank by training machine learning models to predict the maximum loan amount and supporting the decision making in the loan approval process. Datasets (two) are download from Kaggle and combined. The research applies classification models - Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression, and Artificial Neural Networks to predict the loan approval. Also regression models - Linear Regression, Random Forest Regression, Gradient Boosting Regression, Support Vector Machine (SVM), and Decision Tree Regression which used to predict the maximum loan amount. The models are evaluated using 5-flod cross validation, whit classification performance access using accuracy, precision, recall, F1- score, confusion matrix, and ROC curve. While regression models are analyzed using Mean Absolute Error (MAE) and Mean Squared Error (MSE). The Random Forest model is the best performer in loan approval process with accuracy of 95%, The Random Forest Regression model is the best performer in maximum loan amount prediction model with minimum MSA (0.1272) & MAE (0.1558) values. Based on evaluation results Random Forest classifier selected as suitable model for bank loan approval prediction which have best performance compared with other models.