Study on Predictive Modeling for Loan Approval

Abstract

In the financial business, authorizing a loan is a critical decision-making step that often necessitates a human review of applicants' financial information and credit history. However, as massive datasets become more readily available and computing technology advances, machine learning approaches will become more powerful tools for improving the precision and effectiveness of loan approval processes. This study investigates the use of machine learning techniques combined with predictive modeling to automate and optimize loan approval processes. Multiple algorithms analyze crucial features such as job status, credit score, income, and loan amount to determine if a loan will be accepted or rejected. These approaches include logistic regression, decision trees, random forests, and neural networks. The goal of the research is to create an automated loan approval system.