A Review of Machine Learning Approaches for Loan Approval in Banking

Abstract

Banker loans were slow, did not accept applications from the general public, were utilized through a limited banker’s circle of confidants, and were largely a wasteful and biased waste of time.   As happens with almost every natural language processing subject, the judgment done by the machine learning (ML) in the last few years has come a long way and making the loan process a lot more automated. This study examines the use of the loans approval system and the application of the machine learning techniques used for solving the case on the other hand of benefits, drawbacks and impact on the financial industry. The clustering algorithms as well as logistic regression, decision trees, support vector machines and other supervised and unsupervised learning techniques are discussed in the study. It also presents newer methods, including deep learning and reinforcement learning. It provides mention of fairness, ethical issues, and important issues such as data quality, model interpretation, etc., and transparent, unbiased systems. Additionally, the paper also shows some cases that banks have used machine learning for practical uses of loan approval system and real-world examples of how machine learning is applied. This is a well-known problem in the sector where loan acceptance forecast is a long-standing problem. Historically, the subjective criteria and manual methods have been the only means of measuring loan applications such that lenders had always had to rely always use subjective criteria and manual techniques to come to a default decision on a loan and are more likely to wind up at opposite ends. Nowadays, newer, more accurate prediction models of machine learning techniques have emerged, which may be used by financial institutions to make their lending decisions as fast as possible.