An alternative statistical framework for credit default prediction

Abstract

The purpose of this study is to introduce a gradient-boosting model that is robust to high-dimensional data and can produce a strong classifier by combining the predictors of many weak classifiers for credit default risk prediction. Therefore, this method is recommended for practical applications. This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification and regression tree (CART), logistic regression (LR), multivariate adaptive regression splines (MARS) and a random forest (RF). Six real-world credit data sets are used for model validation. The performance of each model is compared using six performance measures, and a receiver operating characteristics (ROC) curve is plotted for the best classifiers of each data set. The empirical findings confirm that the gradient-boosting model is reliable and efficient across all of the performance criteria. In addition, LR and CART exhibit superior performances. The contributions of this study have theoretical and practical implications, as credit default risk prediction is a complicated and always contemporary issue.