Authors:
Dominic Strube
1
;
Christian Daase
2
and
Jennifer Schietzel-Kalkbrenner
3
Affiliations:
1
Hochschule Wismar, University of Applied Sciences, Technology, Business and Design, Wismar, Germany
;
2
Institute of Technical and Business Information Systems, Otto-von-Guericke University, Magdeburg, Germany
;
3
Berufliche Hochschule Hamburg, Hamburg, Germany
Keyword(s):
Credit Risk, Artificial Intelligence, ESG Assessments, Data Analysis, Sustainability.
Abstract:
This article addresses the evolving dynamics of sustainability risks in the banking sector, with a particular focus on the integration of artificial intelligence (AI) in risk assessment and management. The impact of environmental, social, and governance (ESG) factors on creditworthiness evaluation is examined and highlights the complexities and challenges that financial institutions face in adapting their risk management frameworks to accommodate these sustainability risks. The paper underscores the difficulties banks face in effectively incorporating ESG considerations, primarily due to the absence of standardized methodologies and the intricate interplay between ESG components and banking risk elements. In this context, the potential of AI applications is critically assessed, especially those utilizing large datasets, to identify complex patterns and correlations that often elude human analysts. This investigation includes both the opportunities AI presents in enhancing the precisi
on of risk assessments and the associated challenges, including issues related to the opacity and control of complex, self-learning AI models, as well as regulatory and privacy concerns. Finally, the article presents a schematic approach through which banks can actively integrate sustainability risks into their risk management strategies, emphasizing the need for ongoing research and development in this crucial area.
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