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Modifying model risk management practice in the era of AI/ML

Author

Listed:
  • Brotcke, Liming

    (Sr Director — Model Validation Risk — MRM, Ally, USA)

Abstract

The model use of artificial intelligence (AI) and machine learning (ML) has caused unprecedented sensation around the wide applicability of these techniques. The rapid adoption of those alternative tools and methodologies by the heavily regulated financial sector, in areas that are outside the conventional credit lending and market participation, has posed significant challenges for model risk management professionals, including correctly defining AI and ML, properly establishing a governance framework, and, most importantly, effectively challenging AI/ML models. In this paper, the author attempts to describe the history of AI/ML, the evolution of key mathematical theories and modelling, commonalities and distinctions between statistical models and ML algorithms, and challenges of evaluation of some ML models. She discusses plausible solutions to practically address those challenges.

Suggested Citation

  • Brotcke, Liming, 2020. "Modifying model risk management practice in the era of AI/ML," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 13(3), pages 255-265, June.
    • Handle: RePEc:aza:rmfi00:y:2020:v:13:i:3:p:255-265
    as

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    More about this item

    Keywords

    mathematical model; AI; ML; model risk; governance;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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