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Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations

Author

Listed:
  • Hurlin, Christophe

    (University of Orleans)

  • Pérignon, Christophe

    (HEC Paris)

Abstract

This survey proposes a theoretical and practical reflection on the use of machine learning methods in the context of the Internal Ratings Based (IRB) approach to banks' capital requirements. While machine learning is still rarely used in the regulatory domain (IRB, IFRS 9, stress tests), recent discussions initiated by the European Banking Authority suggest that this may change in the near future. While technically complex, this subject is crucial given growing concerns about the potential financial instability caused by the banks' use of opaque internal models. Conversely, for their proponents, machine learning models offer the prospect of better measurement of credit risk and enhancing financial inclusion. This survey yields several conclusions and recommendations regarding (i) the accuracy of risk parameter estimations, (ii) the level of regulatory capital, (iii) the trade-off between performance and interpretability, (iv) international banking competition, and (v) the governance and operational risks of machine learning models.

Suggested Citation

  • Hurlin, Christophe & Pérignon, Christophe, 2023. "Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations," HEC Research Papers Series 1480, HEC Paris.
    • Handle: RePEc:ebg:heccah:1480
    DOI: 10.2139/ssrn.4483793
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    Keywords

    Banking; Machine Learning; Artificial Intelligence; Internal models; Prudential regulation; Regulatory capital;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G29 - Financial Economics - - Financial Institutions and Services - - - Other

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