Using data mining predictive models to classify credit card applicants

Abstract

Credit scoring using predictive models can help in the process of assessing credit worthiness during the credit evaluation process. The objective of credit scoring models is to assign credit risk score to determine if a customer is likely to default on the financial obligation. Construction of credit scoring models requires data mining techniques. Using historical data on payments, demographic characteristics and statistical techniques, credit scoring models can help identify the important demographic characteristics related to credit risk and provide a score for each customer. This paper illustrates the construction and comparison of three credit scoring models: logistic regression (LR) model, classification and regression tree (CART) model and neural network (NN) model to discriminate between rejected and accepted credit card applicants of a bank. Results show that Neural Network model has a slightly higher validation predictive accuracy rate (LR = 74.56%, NN = 76.46%, CART = 73.66%).