Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/195833 
Erscheinungsjahr: 
2018
Quellenangabe: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 6 [Issue:] 2 [Publisher:] MDPI [Place:] Basel [Year:] 2018 [Pages:] 1-16
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.'
Schlagwörter: 
operational risk
artificial intelligence
machine learning
heuristics
machine reasoning
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Erscheint in der Sammlung:

Datei(en):
Datei
Größe
2.02 MB





Publikationen in EconStor sind urheberrechtlich geschützt.