# EBA discussion paper on machine learning for IRB models: Discussion Paper on machine learning for IRB models

Source: https://www.bankingnewsai.com/ai-regulation/documents/eba-ml-irb-discussion-paper-2021
Last updated: Aug 26, 2026

On November 11, 2021 the EBA published a discussion paper on machine learning for internal ratings-based (IRB) models, open for comment until February 11, 2022. It set out how sophisticated ML models can coexist with the Capital Requirements Regulation when used in IRB models and proposed principle-based recommendations to ensure prudent use — keeping models explainable, understood by staff and management, validated, and supervisable. It was the first EU supervisory statement on ML in regulatory capital models and was followed by the August 2023 follow-up report.

## At a glance

| Field | Value |
| --- | --- |
| Authority | [EBA](https://www.bankingnewsai.com/ai-regulation/eba) |
| Type | Consultation |
| Status | Superseded |
| Published | Nov 11, 2021 |
| Comment deadline | Feb 11, 2022 |
| Applies to | EU credit institutions using or planning to use machine learning in internal ratings-based (IRB) credit-risk models for regulatory capital under the CRR |
| Official text | https://www.eba.europa.eu/publications-and-media/press-releases/eba-consults-machine-learning-internal-ratings-based-models |

## Key points

- Published November 11, 2021; consultation closed February 11, 2022.
- Aim: set supervisory expectations on how ML models can adhere to CRR requirements for IRB models (Articles 142–191 CRR) — not to change the rules.
- Identifies challenges: interpretability and explainability, model complexity and overfitting, data quality and representativeness, staff skills, and the CRR's requirements on human judgement and documentation.
- Identifies opportunities: better risk differentiation, use of unstructured data, ML as challenger models in validation, collateral valuation.
- Principle-based recommendations: ensure adequate understanding of the model by management and validation, document the model and data, guard against overfitting, keep a consistent model over time, and be able to explain individual outputs where needed.
- Applies equally to PD, LGD and EAD models and to ML used in supporting steps such as data preparation and risk differentiation.

## What changed for banks

Before this paper, EU banks had no supervisory statement on whether ML could sit inside capital models; most assumed the ECB would not approve it. The paper confirmed ML is permissible within the IRB framework if it meets existing CRR requirements, and it defined the tests — explainability, understanding, documentation, stability — that the ECB now applies in model approvals.

## Use cases it governs

- [Model risk management](https://www.bankingnewsai.com/ai-regulation/by-use-case#model-risk)
- [Credit scoring & underwriting](https://www.bankingnewsai.com/ai-regulation/by-use-case#credit-underwriting)

> Superseded by [EBA follow-up report on machine learning for IRB models (EBA/REP/2023/28)](https://www.bankingnewsai.com/ai-regulation/documents/eba-rep-2023-28-ml-irb-follow-up).

## FAQ

### Can machine learning be used in IRB capital models in the EU?

Yes, in principle. The EBA's November 2021 discussion paper and its August 2023 follow-up report say ML can be used in IRB models provided CRR requirements are met — in particular that management and validation understand the model, that it is documented and explainable, and that overfitting and instability are controlled.

### When did the EBA consultation on ML for IRB models close?

February 11, 2022. The EBA published the feedback and its conclusions in a follow-up report on August 4, 2023 (EBA/REP/2023/28).

## Related documents

- [EBA follow-up report on machine learning for IRB models (EBA/REP/2023/28)](https://www.bankingnewsai.com/ai-regulation/documents/eba-rep-2023-28-ml-irb-follow-up) — Machine Learning for IRB Models — Follow-up report from the consultation on the Discussion paper on machine learning for IRB models (Aug 4, 2023)
- [ESA Statement on ICT risks from frontier AI models (JC 2026 25)](https://www.bankingnewsai.com/ai-regulation/documents/esas-jc-2026-25-frontier-ai-statement) — ESA Statement: Toward a consistent and risk-based approach for ICT risks from frontier AI models (Jul 31, 2026)
- [EBA factsheet: AI Act implications for the EU banking and payments sector](https://www.bankingnewsai.com/ai-regulation/documents/eba-ai-act-factsheet-2025) — AI Act: implications for the EU banking and payments sector (Nov 21, 2025)
- [EBA Chair letter to the Commission on the AI Act mapping exercise (EBA/2025/D/5384)](https://www.bankingnewsai.com/ai-regulation/documents/eba-2025-d-5384-ai-act-mapping-letter) — Outcome of EBA's AI Act mapping exercise — letter to DG FISMA and DG CNECT (Nov 21, 2025)
- [EBA Work Programme 2026](https://www.bankingnewsai.com/ai-regulation/documents/eba-work-programme-2026) — EBA Work Programme 2026 — AI Act implementation and digital-finance priorities (Oct 1, 2025)
- [EBA report: Rising application of AI in EU banking and payments (Sep 2025)](https://www.bankingnewsai.com/ai-regulation/documents/eba-ai-adoption-report-2025) — Rising application of AI in EU banking and payments sector (Sep 25, 2025)
- [EBA/GL/2022/15 (remote customer onboarding)](https://www.bankingnewsai.com/ai-regulation/documents/eba-gl-2022-15-remote-customer-onboarding) — EBA/GL/2022/15 Guidelines on the use of Remote Customer Onboarding Solutions under Article 13(1) of Directive (EU) 2015/849 (Nov 22, 2022)
- [EBA Guidelines on loan origination and monitoring (EBA/GL/2020/06)](https://www.bankingnewsai.com/ai-regulation/documents/eba-gl-2020-06-loan-origination-monitoring) — Guidelines on loan origination and monitoring (May 29, 2020)

Last reviewed Aug 26, 2026. Cite the official text (https://www.eba.europa.eu/publications-and-media/press-releases/eba-consults-machine-learning-internal-ratings-based-models) for the rule and this page for the summary and dates.

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