Credit Card Application Management System

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.