Use Case: NFR—HR Risk

Harro Dittmar (), Arne Schmüser () and Farah Skaf ()
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Harro Dittmar: ifb SE
Arne Schmüser: ifb SE
Farah Skaf: ifb SE

A chapter in The Digital Journey of Banking and Insurance, Volume II, 2021, pp 51-72 from Springer

Abstract: Abstract The management of financial risks is already well researched and established. In contrast, risk management and methods for non-financial risks (NFR) are often much less well developed and networked. The main challenge with non-financial risks lies in the nature of the risks. These may have a low probability of occurrence, but if they do occur, they can have far-reaching consequences. Therefore, new methods are needed which allow event- and scenario-oriented analyzes. The higher uncertainty and more dynamic changes (VUCA) highlight the need for a uniform and comprehensive framework for non-financial risks and underline once again the acute need for action at the institutions. In this article, we will closely analyze one of the most important/connected risk categories among non-financial risks, which is human resources (HR) risk, while focusing on the risk of resignation. We will also show how this risk can be quantified and evaluated, which is essential for its management. The possibilities illustrated can be easily digitalized and automated, which means that artificial intelligence (AI) algorithms or machine learning (ML) algorithms can be used to support them. As an example, the training of an algorithm is documented that classifies the data records of employees into “Resignation: yes/no”.

Keywords: NFR; HR risk; Impact graph; Indicators for resignation; Predicting resignation; Machine learning (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-78829-2_4

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DOI: 10.1007/978-3-030-78829-2_4

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