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The joint consultation paper of BaFin and the Deutsche Bundesbank of 15 July 2021 states that, as machine-learning models become more complex, their calculations become less transparent for modellers, users, validators and supervisors, making the modelling harder to comprehend and the validity of model output harder to check. Copy claim link
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Date 2021-07-15 · publication Records dated 2021 → Role Articulation Register (kind of text) Nonfiction Scope and limits The paper also states that the black-box characteristic can be justified by higher predictive ability and that explainable-AI methods are promising; it concerns risk models under Pillars 1 and 2.
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Machine learning in risk models – Characteristics and supervisory priorities (consultation paper) ↗ Deutsche Bundesbank · 2021-07-15
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Source excerpt “the details of the calculations are less transparent for modellers, users, validators and supervisors. As a result, it is more difficult to comprehend the modelling and, if applicable, to check the validity of the model output as well.” Location in source PDF p. 13, chapter III.3 ‘Focus on explainability’
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Machine-checked · 2026-11-06
Source details and check notes Authors BaFin and Deutsche Bundesbank Version Retrieved for the candidate pack on 2026-11-04 (SHA-256 3dae0409deb26f8b8e0778b2eee0bf3f07d629aef04df4803023cb3354e8e9a1). Retrieved again at admission on 2026-11-06 (same file). PDF of the consultation paper as hosted by the Bundesbank (“This version: July 2021”; page footers “BaFin – Consultation 11/2021”) Canonical address Open the canonical source ↗ Accessed 2026-11-06 Language English (en) Attribution BaFin and Deutsche Bundesbank, “Machine learning in risk models – Characteristics and supervisory priorities (consultation paper)”, 2021-07-15. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI and money (research/ai-money). The quotation (38 words) matched a fresh retrieval of 2026-11-06 (the same file the candidate pack read). Matching ignored differences of whitespace, case, quotation marks, dashes, line-break hyphens and Unicode compatibility forms. No human editorial review. primary source Context only
Machine learning methods ↗ Deutsche Bundesbank · Publication date unknown
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Source excerpt “The following joint consultation paper of BaFin and Deutsche Bundesbank deals with the concrete use of machine learning (ML) in risk models of Pillars I and II of the regulatory frameworks for banks and insurers.” Location in source Section ‘Consultation 11/2021’
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Machine-checked · 2026-11-06
Source details and check notes Authors Deutsche Bundesbank Version Retrieved for the candidate pack on 2026-11-04 (SHA-256 48a7f82c7e165a25459f1473afb5cfb58ce8493bc170869f48451e57841606d3). Retrieved again at admission on 2026-11-06 (SHA-256 3d6683677ecdc9d31e93b650af6561bb7bec50b51c278cbaf76340d5494274ec; web pages are served with new markup on each request). Bundesbank web page as served on 2026-11-04; gives the joint authorship and the date of the consultation paper Canonical address Open the canonical source ↗ Accessed 2026-11-06 Language English (en) Attribution Deutsche Bundesbank, “Machine learning methods”. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI and money (research/ai-money). The quotation (35 words) matched a fresh retrieval of 2026-11-06. Matching ignored differences of whitespace, case, quotation marks, dashes, line-break hyphens and Unicode compatibility forms. No human editorial review. Status and meaning Editorial status. Machine-checked . Machines found this record and checked it against its sources; no person accepted it individually. Each evidence record above shows how it was checked.
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Record ID am-u04-buba-2021-ml-risk-models-consultation