TY  - JOUR
A1  - Bücker, Michael
A1  - van Kampen, Maarten
A1  - Krämer, Walter
T1  - Reject inference in consumer credit scoring with nonignorable missing data
JF  - Journal of Banking & Finance
Y1  - 2013
U6  - http://dx.doi.org/10.1016/j.jbankfin.2012.11.002
VL  - 37
IS  - 3
SP  - 1040
EP  - 1045
ER  - 
TY  - JOUR
A1  - Bücker, Michael
A1  - Krämer, Walter
A1  - Arnold, Matthias
T1  - A Hausman test for non-ignorability
JF  - Economics Letters
Y1  - 2012
U6  - http://dx.doi.org/10.1016/j.econlet.2011.08.025
VL  - 114
IS  - 1
SP  - 23
EP  - 25
ER  - 
TY  - JOUR
A1  - Hoops, Christian
A1  - Bücker, Michael
T1  - Determinants, Moderators and Consequences of Organizational Interaction Orientation
JF  - Journal of Entrepreneurship Management and Innovation
Y1  - 2014
VL  - 9
IS  - 4
SP  - 73
EP  - 100
ER  - 
TY  - JOUR
A1  - Krämer, Walter
A1  - Bücker, Michael
T1  - Probleme des Qualitätsvergleichs von Kreditausfallprognosen
JF  - AStA Wirtschafts- und Sozialstatistisches Archiv
Y1  - 2011
U6  - http://dx.doi.org/10.1007/s11943-011-0096-0
VL  - 5
IS  - 1
SP  - 39
EP  - 58
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  -