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

Author

Listed:
  • Udo Milkau

    (DZ BANK AG, Platz der Republik, 60265 Frankfurt, Germany
    House of Finance, Goethe University, Theodor-W.-Adorno-Platz 3, 60323 Frankfurt, Germany)

  • Jürgen Bott

    (University of Applied Sciences, Kaiserslautern—Zweibrücken, Amerikastrasse 1, 66482 Zweibrücken, Germany)

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.”

Suggested Citation

  • Udo Milkau & Jürgen Bott, 2018. "Active Management of Operational Risk in the Regimes of the “Unknown”: What Can Machine Learning or Heuristics Deliver?," Risks, MDPI, vol. 6(2), pages 1-16, April.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:2:p:41-:d:142597
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    References listed on IDEAS

    as
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