Wirtschaft (MSB)
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§ 296 HGB
(2020)
§ 294 HGB
(2020)
Unser Wirtschaftssystem stößt an planetarische Grenzen, wie beispielsweise durch den immer schneller voranschreitenden menschgemachten Klimawandel deutlich wird. Es stellt sich die Frage, ob das auch anders geht: Wie kann ein Wirtschaftssystem aussehen, dass mit den Grenzen unseres Erdsystems kompatibel ist? Welche Ansätze gibt es und welche werden bereits praktisch umgesetzt? Kann das funktionieren, ohne dass unser Wohlstand abnimmt?
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.
Study programme development is one of the most challenging processes
at universities since all faculty is involved. And in our experience, the redesign of already existing programmes seems to be even more difficult: Whereas innovative forces want to pick up new trends (e.g. digitalisation or other new teaching concepts) more conservative forces emphasises on values and refer to existing experience. Both positions are important and contextually right. Thus, the presented format provides a gradual framework to bridge the gap between both sides in an interactive and creative process.
Both sides are invited to negotiate the best possible result by using an unusual approach for university discussions, the benefit analysis method known e.g. from economics. After the negotiating activity, it should be obvious that a change of perspective is also helpful, if not necessary, to create a new or updated study programme. The practiced approach helps as well to recognise which limits for study programme development remain when visionary ideas are measured against reality.
Robotic Process Automation (RPA) – Welche Prozesse lassen sich mit Software-Robotern automatisieren?
(2020)
While the service sector is growing rapidly, the purchasing of services has not yet received significant attention in theory or practice. Service purchasers face serious challenges, and existing purchasing practices for services are often non-strategic. We choose an exploratory–qualitative research approach to investigate the purchasing of IT, logistics and Maintenance, Repair, and Overhaul (MRO) services. In particular, we focus on the role of visibility and analyze how service purchasers can benefit from extensive knowledge about their service networks. We determine that visibility indeed adds significant value to service purchasing and can help service purchasers to decrease costs, mitigate risks and maintain competitiveness.