Wirtschaft (MSB)
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Faculty
Working Capital in der Kreditanalyse: Cashflow-Effekte erkennen und Risikosignale identifizieren.
(2024)
What sparks academic engagement with society? A comparison of incentives appealing to motives
(2021)
Vereinfachtes Ertragswertverfahren: Maßvolle Differenzierung anstelle einheitlicher Werte notwendig
(2009)
Planungsunsicherheiten gehören zum unternehmerischen
Alltag von KMU. Mit welchen Planungsinstrumenten
Unternehmen Planungsrisiken bewältigen können und wie Planungsunsicherheiten bei der Unternehmensbewertung zu berücksichtigen sind, zeigt
der folgende Beitrag. Es handelt sich um den dritten
und letzten Beitrag der dreiteiligen Aufsatzreihe zur
Unternehmensbewertung und Planung nach IDW S 1.
University-Industry Linkage Evolution: An Empirical Investigation of Relational Success Factors
(2013)
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.