Credit risk modelling and prediction: Logistic regression versus machine learning boosting algorithms

The use of machine learning methods in credit risk modelling has been proven to yield good results in terms of increasing the accuracy of the risk score as- signed to customers. In this thesis, the aim is to examine the performance of the machine learning boosting algorithms XGBoost and CatBoost, with logis- tic regression as a benchmark model, in terms of assessing credit risk. These methods were applied to two different data sets where grid search was used for hyperparameter optimization of XGBoost and CatBoost. The evaluation metrics used to examine the classification accuracy of the methods were model accuracy, ROC curves, AUC and cross validation. According to our results, the machine learning boosting methods outperformed logistic regression on the test data for both data sets and CatBoost yield the highest results in terms of both accuracy and AUC