Active Management of Operational Risk in the Regimes of the “Unknown”: What Can Machine Learning or Heuristics Deliver?

Udo Milkau and Jürgen Bott
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Udo Milkau: DZ BANK AG, Platz der Republik, 60265 Frankfurt, Germany
Jürgen Bott: University of Applied Sciences, Kaiserslautern—Zweibrücken, Amerikastrasse 1, 66482 Zweibrücken, Germany

Risks, 2018, vol. 6, issue 2, 1-16

Abstract: 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.”

Keywords: operational risk; artificial intelligence; machine learning; heuristics; machine reasoning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2018
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