Active management of operational risk in the regimes of the "unknown": What can machine learning or heuristics deliver?

  • Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it comes to the “known unknown” or even the “unknown unknown.” While machine learning has been tested successfully in the regime of the “known,” heuristics typically provide better results for an active operational risk management (in the sense of forecasting). However, precursors in existing data can open a chance for machine learning to provide early warnings even for the regime of the “unknown unknown.”

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Metadaten
Author:Udo Milkau, Jürgen Bott
URN:urn:nbn:de:hebis:30:3-515458
DOI:https://doi.org/10.3390/risks6020041
ISSN:2227-9091
Parent Title (English):Risks
Publisher:MDPI
Place of publication:Basel
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/04/23
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2019/10/24
Tag:artificial intelligence; heuristics; machine learning; machine reasoning; operational risk
Volume:6
Issue:41
Note:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
HeBIS-PPN:455707197
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0