REVIEW OF SUPERVISED MACHINE LEARNING FOR PREDICTIVE LOAN APPROVAL IN THE BANKING SECTOR

Abstract

The rapid evolution of technology has significantly impacted the banking industry, particularly in the realm of loan approval processes. This review explores the application of supervised machine learning techniques for predictive loan approval, highlighting their potential to enhance decision-making, reduce processing time, and improve risk management. Supervised learning models, such as decision trees, logistic regression, and neural networks, are increasingly being utilized to analyze historical data and predict the likelihood of loan approval based on various financial indicators, including credit scores, income levels, and employment history. These models offer several advantages over traditional methods, including higher accuracy, scalability, and the ability to process complex datasets. However, their effectiveness is contingent upon the quality of the data used for training and the careful selection of appropriate algorithms. This review also addresses the challenges associated with model interpretability, data privacy, and the need for continuous model adaptation to changing economic conditions. By examining the current state of supervised machine learning in loan approval prediction, this review aims to provide insights into the benefits and limitations of these techniques and to identify areas for future research to further enhance their application in the banking sector.