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 - https://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 - https://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 - 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 - https://doi.org/10.1007/978-3-642-10745-0_13 SP - 127 EP - 135 PB - Springer 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 - 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 - 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 - Journal of the Operational Research Society Y1 - 2021 U6 - https://doi.org/10.1080/01605682.2021.1922098 ER - TY - INPR A1 - Bücker, Michael A1 - Schlüsener, Niels T1 - Fast Learning of Dynamic Hand Gesture Recognition with Few-Shot Learning Models T2 - arXiv Y1 - 2022 U6 - https://doi.org/10.48550/arXiv.2212.08363 SP - 1 EP - 9 PB - arXiv.org ER - TY - CHAP A1 - Bücker, Michael A1 - Kayser, Franz A1 - Mayer, Thomas T1 - Addressing Challenges in a Dangerous World: Developing a Design Science Artifact for Advancing Open Source Intelligence (OSINT) Research T2 - Proceedings of the 58th Hawaii International Conference on System Sciences N2 - Open Source Intelligence (OSINT), deriving intelligence from public data, has gained scrutiny since the Russian invasion of Ukraine. Despite numerous attempts at standard definitions, research around technology-driven intelligence gathering and analysis remains ambiguous. This paper uses a Design Science Research (DSR) approach to categorize the technology-driven intelligence construct. Analyzing sixty studies via structured literature review, three domains were identified: maturity, Intelligence Cycle phase, and use case. The resulting framework, developed into a trend radar, was evaluated with expert interviews, revealing technological gaps in planning/direction and dissemination/integration phases. While intelligent support technologies were noted, practical implementation lags behind theory. The human factor remains central to OSINT. Findings suggest future research should develop applications for underserved phases and examine why proven applications are not widely adopted, considering legal, ethical, political, and social factors. This study contributes to technology-driven intelligence literature as a knowledge base, research gap identifier, and guide for further research. Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:836-opus-186518 UR - https://hdl.handle.net/10125/109494 SN - 978-0-9981331-8-8 SP - 5370 EP - 5379 PB - University of Hawaiʻi at Mānoa Library ER -