Comparative study of Machine Learning Approaches for Loan Approval Prediction in financial sector

The surge in loan demand in India highlights the necessity for banks to refine their assessment methodologies to ensure accurate evaluation of applicants’ creditworthiness. As financial institutions address this demand, leveraging data analytics presents an opportunity to enhance loan approval processes. This paper aims to address this need by exploring various data analysis techniques tailored to comprehensively assess applicant backgrounds and contextual factors. To optimize loan approval processes, a comparative analysis of five prominent machine learning models - Logistic Regression, Decision Tree, K Nearest Neighbour (KNN), Random Forest Classifier, and Gaussian Naive Bayes is conducted. Through a detailed examination of the models’ performance, the study seeks to determine the most efficient method for accurately predicting loan approval outcomes. The findings unveil the exceptional performance of both Decision Tree and Random Forest models, with an impressive accuracy rate of 100%. Integrating these models into current bank loan approval systems can lead to significant improvements in efficiency and reliability. Specifically, these models can make the decision-making process more efficient, reduce human errors, and speed up loan processing. These enhancements benefit both financial institutions and applicants by improving the accuracy of loan approvals, increasing customer satisfaction, and ensuring more efficient and reliable lending decisions.