Comparative Analysis of Customer Loan Approval Prediction using Machine Learning Algorithms

Abstract

In today’s increasingly competitive market, estimating the risk involved in a loan application is one of the most crucial challenges for banks’ survival and profitability. The banks receive many loan applications from their customers and other individuals daily. Not every applicant is accepted. Most banks employ their credit scoring and risk assessment procedures to examine loan applications and make credit approval decisions. Despite this, many incidents of people failing to repay loans or defaulting on them occur every year, causing financial institutions to lose a significant amount of money. In this study, Machine Learning (ML) algorithms are used to extract patterns from a common loan-approved dataset and retrieve patterns in forecasting future loan defaulters. Customers’ past data, such as their age, income, loan amount, and tenure of work, will be used to conduct the analysis. To determine the maximum relevant features, i.e. the factors that have the most impact on the prediction outcome, various ML algorithms such as Random Forest, Support Vector Machine, K-Nearest Neighbor and Logistic Regression, were used. These mentioned algorithms are evaluated with the standard metrics and compared with each other. The random forest algorithm achieves better accuracy.