Predicting loan default with XGBoost: an examination of strength and application

Abstract

This thesis explores the application of the XGBoost algorithm for predicting loan defaults, a vital aspect of credit risk management. By leveraging advanced machine learning techniques, the study aims to improve the accuracy and reliability of default predictions over traditional methods. We begin with an overview of fundamental machine learning concepts, including loss functions and tree-based models, which sets the stage for a detailed examination of gradient boosting and its implementations. The focus then shifts to XGBoost, where we delve into its objective function, optimization process, and hyperparameters. Using a publicly available dataset from Bondora, we conduct thorough data preprocessing, followed by careful hyperparameter tuning using grid search and cross-validation. Our results highlight XGBoost’s ability to handle complex, real-world data effectively, resulting in significant improvements in prediction performance. This study illustrates the importance of sophisticated algorithms in advancing the field of financial predictive analytics.