TY  - CHAP
A1  - Bücker, Michael
A1  - Szepannek, Gero
A1  - Biecek, Przemyslaw
A1  - Gosiewska, Alicja
A1  - Staniak, Mateusz
ED  - Crook, Jonathan
T1  - Transparency of Machine Learning Models in Credit Scoring
T2  - CRC Conference XVI Papers
N2  - A major requirement for Credit Scoring models is of course to provide a risk prediction that is as accurate as possible. In addition, regulators demand these models to be transparent and auditable. Thus, in Credit Scoring very simple Predictive Models such as Logistic Regression or Decision Trees are still widely used and the superior predictive power of modern Machine Learning algorithms cannot be fully leveraged. A lot of potential is therefore missed, leading to higher reserves or more credit defaults. This talk presents an overview of techniques that are able to make “black box” machine learning models transparent and demonstrate how they can be applied in Credit Scoring. We use the DALEX set of tools to compare a traditional scoring approach with state of the art Machine Learning models and asses both approaches in terms of interpretability and predictive power. Results show that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.
Y1  - 2019
UR  - https://crc.business-school.ed.ac.uk/wp-content/uploads/sites/55/2019/07/C13-Transparency-of-Machine-Learning-Models-in-Credit-Scoring-B%C3%BCcker.pdf
SP  - 1
EP  - 1
PB  - Credit Research Center, University of Edinburgh
CY  - Edinburgh
ER  - 
TY  - JOUR
A1  - Bücker, Michael
A1  - Szepannek, Gero
A1  - Gosiewska, Alicja
A1  - Biecek, Przemyslaw
T1  - Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
JF  - arXiv
N2  - A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential is therefore missed, leading to higher reserves or more credit defaults. This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable. Following this framework, we present an overview of techniques, demonstrate how they can be applied in credit scoring and how results compare to the interpretability of score cards. A real world case study shows that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.
Y1  - 2020
UR  - https://arxiv.org/abs/2009.13384
VL  - 2009.13384
SP  - 1
EP  - 30
ER  - 
TY  - JOUR
A1  - Bücker, Michael
A1  - Szepannek, Gero
A1  - Gosiewska, Alicja
A1  - Biecek, Przemyslaw
T1  - Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring
JF  - Journal of the Operational Research Society
Y1  - 2021
U6  - http://dx.doi.org/10.1080/01605682.2021.1922098
ER  -