Comparative study of credit risk evaluation for unbalanced datasets using deep learning classifiers

Abstract

Credit risk assessment deals with calculating the risk of a loan not being repaid. For this reason, a lot of research effort is directed at credit risk analysis. In this study, machine learning models such as Light Gradient-Boosting Machine and Neural Networks are utilized for credit risk assessment. These machine learning models are trained and tested using The Home Credit Default Risk dataset that was obtained from a competition on the website kaggle.com. Resampling techniques were also implemented to tackle the class imbalance problem in the dataset. Moreover, various preprocessing techniques were also utilized to deal with missing values and outliers in the dataset. The study presents the results of experiments with different parameters and preprocessing techniques and showcases the optimal configuration for the best results. The performance metrics of the machine learning models that are implemented in the experiments are compared to the performance metrics of a baseline system that used the Light Gradient-Boosting Machine model without applying preprocessing techniques.