Comparative analysis and implementation of credit risk prediction through distinct machine learning models

Abstract

Predicting the risk while lending money has always been a challenge for financial institutions. To make such decisions many banks or financial organizations follow different techniques to analyze a set of data. Manual prediction and analysis of credit risk can not only be very hectic but also quite time-consuming. To solve this issue, what is needed is a system that ensures high predictive accuracy and optimality. Machine Learning algorithms such as various Regression models, Gradient Boosting, Deep Learning, Neural Networks, Support Vector, Random Forest and others can be used to anticipate whether a consumer is eligible for taking a loan with high accuracy. In this thesis, an attempt has been made to find a good ML algorithm that shall help various banks and/or financial institutions to reliably predict the credit risk on an individual by analyzing appropriate datasets. Following that, a highly accurate result for said institutions can be ensured, which they can use to determine whether a consumer requesting credit should be allotted credit or not.