Web-Based Credit Card Allocation System Using Machine Learning

Abstract

In this chapter, we have focused on allocating a virtual credit card based on individual credit score and their individual profile. The credit card allocation system issues a digital credit card to the person who is applying after checking their eligibility. There is no system till now which can issue a virtual credit card also based on an individual’s credit details. In the current situation, we can see everything is getting digitalized, and customers have to wait too long to get a credit card issued by the bank, so our idea is to ease the process by digitalizing it. There are different kinds of machine learning algorithms, which help in predicting the eligibility of a user to get a credit card. In this chapter, we have used machine learning algorithms such as KNN (K Nearset Neighbors Classifier) and achieved an accuracy of 88.4%. Also, we have compared different classifications such as logistic regression, random forest algorithm, KNN, Naive Bayes, and Decision Tree Classifier, which can be used in this system. The findings of this chapter are very useful for banks and their employees, which will reduce the workload of manual processes of collecting and verifying all the details of customers and then allotting them a physical credit card. The users themselves can easily check for their eligibility for credit card allocation entertain them through this system. There is no previous research on allocating a virtual credit card to the user. The system has a huge scope in banking and business sectors. The data set used for prediction has more than 650 entries to get more accurate results. The web-based methodology of our proposed system a is combination of machine learning algorithms for prediction and web development tools such as HTML, CSS, JavaScript, and Flask for the user interface part.