Literaturverzeichnis

Baesens, B.; Rösch, D.; Scheule, H. (2016): Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS. Wiley and SAS Business Series. 

Chen, T.; Guestrin, C. (2016): XGBoost: A scalable tree boosting system, in: Proceedings of the International Conference on Knowledge Discovery and Data Mining (22nd), S. 785–794. 

European Banking Authority (2021): Discussion Paper on Machine Learning for IRB Models, EBA/DP/2021/04, https://eba.europa.eu, 30. März 2025. 

European Banking Authority (2023): Follow-up on the Discussion Paper on Machine Learning for IRB Models, EBA/REP/2023/28, https://eba.europa.eu, 30. März 2025. 

Gunnarsson et al. (2023): Deep learning for credit scoring: Do or don’t?, in: European Journal of Operational Research, 308(2), S. 658–676. 

Kelp, T.; Schneider, M. (2023): KI für Kreditscoring: Wenn ein Algorithmus über den Kredit entscheidet, https://www.bafin.de/ref/19617498, 30. März 2025. 

Lundberg, S. M.; Lee, S. I. (2017): A unified approach to interpreting model predictions, in: Advances in Neural Information Processing Systems (NeurIPS), S. 30. 

Lundberg, S. M.; Erion, G., Lee, S. I. (2018): Consistent individualized feature attribution for tree ensembles, in: arXiv.