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

Christophe Hurlin and Christophe Pérignon
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Christophe Pérignon: HEC Paris

No 1480, HEC Research Papers Series from 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.

Keywords: Banking; Machine Learning; Artificial Intelligence; Internal models; Prudential regulation; Regulatory capital (search for similar items in EconPapers)
JEL-codes: C10 C38 C55 G21 G29 (search for similar items in EconPapers)
Pages: 43 pages
Date: 2023-06-25
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-eec, nep-fle and nep-rmg
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Working Paper: Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1480

DOI: 10.2139/ssrn.4483793

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