TY  - BOOK
A1  - Bücker, Michael
T1  - Lokale Diskrimination diskreter Daten
Y1  - 2008
CY  - Dortmund
ER  - 
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  - CHAP
A1  - Bücker, Michael
A1  - Szepannek, Gero
A1  - Weihs, Claus
T1  - Local Classification of Discrete Variables by Latent Class Models
T2  - Classification as a Tool for Research, Studies in Classification, Data Analysis, and Knowledge Organization
Y1  - 2010
SN  - 978-3-642-76307-6
U6  - http://dx.doi.org/10.1007/978-3-642-10745-0_13
SP  - 127
EP  - 135
PB  - Springer
ER  - 
TY  - BOOK
A1  - Bücker, Michael
T1  - Statistische Modelle mit nicht-ignorierbar fehlender Zielgröße und Anwendung in der reject inference
Y1  - 2011
U6  - http://dx.doi.org/10.17877/DE290R-555
CY  - Dortmund
ER  - 
TY  - JOUR
A1  - Bücker, Michael
A1  - Krämer, Walter
T1  - Statistischer Qualitätsvergleich von Kreditausfallprognosen
JF  - Discussion Paper SFB 823
Y1  - 2011
VL  - 30
ER  - 
TY  - JOUR
A1  - Bücker, Michael
A1  - Krämer, Walter
T1  - Reject inference in consumer credit scoring with nonignorable missing data
JF  - Discussion Paper SFB 823
Y1  - 2011
VL  - 1
ER  - 
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  -