Loan Eligibility Prediction Using Machine Learning

Abstract

Banks and other financial institutions  compete for customers by providing a wide range of  services and products. Most banks, however, make the vast  majority of their money from their credit portfolio. Loans  accepted by borrowers might lead to interest charges. The  loan portfolio, and customers' repayment habits in  particular, can have a substantial impact on a bank's bottom  line. The financial institution's Non-Performing Assets can  be reduced if it can accurately predict which borrowers are  likely to default on their loans. Therefore, there is  substantial scholarly value in exploring the prediction of  loan endorsement. In order to make accurate predictions, it  is crucial to use Machine Learning methods. Based on a  person's past loan qualification history, this research uses a  machine learning methodology to predict the person's  likelihood of consistently making loan repayments. The  primary aim of this research is to foretell how likely it is  that a given individual will be granted a loan.