MACHINE LEARNING-BASED APPROACHES FOR CREDIT CARD DEBT PREDICTION

Abstract

The primary concern in the stock market and banks that offer credit cards has been a problem over time. Regardless of their capacity to pay, most card users abuse their credit cards and accrue debt from cash cards. The most significant issue facing cardholders and banks alike is this calamity. Predicting credit card customers' default payments became vital to lowering this risk. Data mining approaches, including decision tree, logistic regression, and Naïve Bayes with feature selection methods, were applied to secondary credit card debt data to identify the significant factors that impact credit card default and to enhance the prediction of credit card default. As a result, the decision tree with Gini index splitting criteria forward selection wrapper method was identified as the best model with the highest percentages of accuracy, precision, sensitivity, and area under ROC of 76.39%, 72.02%, 85.08%, and 0.891 respectively. Additionally, the significant factors that impact credit card default are gender, education level, repayment status in July 2005, repayment status in August 2005, status of repayment in September 2005, and the amount paid in June 2005 and May 2005. This study may help financial institutions assess creditworthiness and give consumers insights into their financial behaviors.