Bank Customer’s Credit Score Prediction Using Feature Selection and Data Mining Algorithm

Abstract

Data mining techniques of classification and prediction model are used for analyzing the customer of bank through their real data, customer is a performing as asset or non-performing asset for the bank. Every year each bank faces a similar problem that most of them customers do not refunding loan installment on time and many precisely become defaulter for the bank. So every bank needs a system that can predict in future any customer in future will be profitable or not profitable asset for the bank. If any customer found as non-performing asset means, it has bad credit score. In such case if a customer further requests for new loan, bank can easily identify him as defaulter and reject his/her request on the basis of our new proposed models. In this way, bank extracts their non-performing assets. Besides, bank can also identify their new customers for having good credit score and may offer them other services for the beneficial of bank. There are many as such models that are used for prediction. This paper compares different classification and prediction algorithms to developed best suitable model to analyze loan requesting customer’s data set using their credit score. Mainly in this paper, there is a comparison between random forest algorithms and logistic regression which is best suitable for prediction of credit scores of customers with apply k best feature selection on data set. The aim of proposed model is to reduce bankruptcy, non-performing assets and the losses of bank.