Credit Card Default Prediction based on Machine Learning Techniques

Abstract

In recent years, with the development of society and economy, credit cards have been popularized due to their low interest rate and easy payment. However, with the advent of the epidemic era, the unemployment rate has increased, making the probability of credit card defaults rising. The prediction of credit card default helps banks and financial institutions balance the risk and economic interests, contributes to the stable and healthy development of the financial industry, and plays an important role in bank credit control. Therefore, this paper addresses the credit card default prediction problem by using Random forest, Decision tree, LightGBM, XGBoost, Logistic regression, and Adaboost models to make predictions and compare the results. The outcomes demonstrate that LightGBM algorithm has the most outstanding prediction score, and its AUC value can reach 0.78 and recall rate reaches 0.95.