Wirtschaft (MSB)
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Faculty
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.
Toward a notation for modeling value driver trees: Classification development and research agenda
(2024)
Timingstrategien - Zeitoptimale Ausgestaltung von Produktentwicklungsbeginn und Markteintritt
(1998)
The UBC ecosystem: putting together a comprehensive framework for university-business cooperation
(2017)
The risk sensitivity of Basel risk weights and loan loss provisions: evidence from European banks
(2021)
The Relevance of Problem-based Learning for Policy Development in University-Business Cooperation
(2016)
The rate of convergence to stationarity for M/G/1 models with admission controls via coupling
(2015)
The Bill, Please! Households’ Real Returns on Financial Assets Since the Introduction of the Euro
(2019)
Textanalysen im Controlling
(2015)
Purpose
The purpose of this paper is to investigate the relationships between technology orientations and export performance of small and medium-sized enterprises (SMEs).
Design/methodology/approach
A quantitative research design was adopted for this study. The paper formulates hypotheses from the literature review. These hypotheses are tested using structural equation modeling with data collected from 231 SMEs in Uganda. Data were analyzed using SPSS version 23 and AMOS.
Findings
The findings of this study showed technology orientation has a positive and significant relationship with the performance of Ugandan SMEs and that supply chain agility moderates technology orientation and export performance.
Research limitations/implications
The study discusses the findings, advances limitations and managerial implications. It also suggests future research avenues. It proposes some recommendations to help Ugandan SMEs to form flexible supply chains, use the latest technology and create strong relationship ties with their partners in the supply chain.
Practical implications
The study suggests that managers of Ugandan SMEs should use the latest technology in production, marketing, logistics and supply chain management which will enable them to respond quickly to customer tastes and preferences leading to higher levels of export performance.
Originality/value
This study contributes to the literature on strategic management showing the reliability of scales used and the confirmatory of the factor structure. This study shows that in strategic management technology, orientation is critical in increasing export performance. This study has extended the resource-based view (RBV) and dynamic capabilities theories.
Systematischer Überblick über ausgewählte Regelwerke zur Nachhaltigkeitsberichterstattung (Teil 2)
(2023)