A Comparative Study of Machine Learning Algorithms for Predicting Loan Default and Eligibility

Abstract

Loosening up credits to corporates and individuals for the smooth working of creating economies as is INDIA unpreventable. As a growing number of customers apply for credits in the banks and non-banking financial companies (NBFC), it is really pursuing banks and NBFCs with confined financing to contraption a standard objective and safe framework to credit money to its borrowers for their financial necessities. Also, starting late NBFC inventories have persevered through a basic ruin similarly as the stock expense. It has added to a sickness that has also spread to other money related stocks, horribly impacting the benchmark of late. In this paper, an endeavour is made to assemble the risk related to picking the sensible person who could repay the credit on time thus keeping the bank’s non-performing assets (NPA) on hold. This is refined by dealing with the records of the customer who secured credits from the bank into a readied man-made intelligence model which could yield a definite result. The prime point of convergence of the paper is to choose whether or not it will be ensured to allocate the loan to a particular person.