ESTIMATING AND EVALUATING THE PROBABILITY OF DEFAULT – A MACHINE LEARNING APPROACH

Abstract: In this thesis, we analyse and evaluate classification models for panel credit risk data. Variables are selected based on results from recursive feature elimination as well as economic reasoning where the probability of default is estimated. We employ several machine learning and statistical techniques and assess the performance of each model based on AUC, Brier score as well as the absolute mean difference between the predicted and the actual outcome, carried out with cross validation of four folds and extensive hyperparameter optimization. The LightGBM model had the best performance and many machine learning models showed a superior performance compared to traditional models like logistic regression. Hence, the results of this thesis show that machine learning models like gradient boosting models, neural networks and voting models have the capacity to challenge traditional statistical methods such as logistic regression within credit risk modelling.