Machine Learning Implementation for Prediction of Probability of Default in Credit Risk

Sammanfattning

Probability of Default (PD) models can be used for various purposes within the banking and insurance sector. The models classify an observation as the probability of that observation defaulting within a future time span, often a year. Within banks, this type of model is used to calculate risk grades and capital requirements and needs to be accepted for implementation by Finansinspektionen. Due to requirements from Finansinspektionen regarding transparency and explainability, logistic regression is primarily used today when constructing these models. However, there is an interest in exploring how more advanced machine learning models would perform in this field. On behalf of Länsförsäkringar, this study focuses on how three models built using three different machine learning algorithms perform when trained on the same data as Länsförsäkringar's current model. The algorithms used are Random Forest, XGBoost, and Artificial Neural Networks, and the dataset used consists of private customers holding loans between the years 2007 and 2019. In addition, the study also covers current literature in the field, feature analysis, variable selection, and training of hyperparameters for model optimization. The model that performs the best according to the selected performance measures AUC, Brier score and log loss is the XGBoost model, which is in accordance with findings from several previous studies. The transparency and explainability of this model are found to be inferior to that of logistic regression, but the model does not lack transparency altogether. The study suggests further analysis of how these models could be implemented in the field of PD modelling and how the requirements from Finansinspektionen and EU could be interpreted and changed in order to make reality of the implementation machine learning in risk management.