Using Machine Learning Methods to Forecast Credit Card Approvals


Authors : Mohammad Haseeb Dar; Neerendra Kumar

Volume/Issue : Volume 7 - 2022, Issue 7 - July

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3A0g1NE

DOI : https://doi.org/10.5281/zenodo.6987858

Abstract : As we all know, Every day, commercial banks get a large number of credit card applications. Many of them are turned down for a variety of reasons, including large loan amounts, insufficient income, or too many queries on a person's credit record. Manually assessing these programs is tedious, time-consuming, and error-prone. Fortunately, machine learning can automate this operation, and almost every commercial bank does it nowadays. In this paper, we have used machine learning techniques to create a prediction system for automated credit card approvals, much like actual banks do.

Keywords : Machine Learning, Credit Cards, Logistic Regression

As we all know, Every day, commercial banks get a large number of credit card applications. Many of them are turned down for a variety of reasons, including large loan amounts, insufficient income, or too many queries on a person's credit record. Manually assessing these programs is tedious, time-consuming, and error-prone. Fortunately, machine learning can automate this operation, and almost every commercial bank does it nowadays. In this paper, we have used machine learning techniques to create a prediction system for automated credit card approvals, much like actual banks do.

Keywords : Machine Learning, Credit Cards, Logistic Regression

Video Explanation for Published paper

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