Credit Card Application Management System

Abstract

In the modern banking industry, efficient and accurate credit card application processing is crucial. Traditional
methods rely heavily on manual verification and rule based systems, which can be time-consuming and prone to errors. This
research explores the use of machine learning (ML) techniques to improve the accuracy and efficiency of credit card application
management. Various ML algorithms, including decision trees, logistic regression, and neural networks, are analyzed for their
predictive capabilities in determining the creditworthiness of applicants. The study demonstrates how ML can enhance decision
making, reduce fraud, and streamline the application process.