The EBA's Report on Big Data and Advanced Analytics (EBA/REP/2020/01), published January 13, 2020, is the EU's foundational statement of what supervisors expect from bank use of machine learning. It sets out four pillars needed to roll out advanced analytics — data management, technological infrastructure, organisation and governance, and analytics methodology — plus a set of 'elements of trust' including ethics, explainability and interpretability, fairness and bias avoidance, traceability and auditability, data protection, consumer protection and security.
| Document | EBA Report on Big Data and Advanced Analytics (EBA/REP/2020/01) — Report on Big Data and Advanced Analytics |
| Issued by | European Banking Authority |
| Type | Report |
| Status | Final |
| Published | Jan 13, 2020 |
| Applies to | EU credit institutions and payment institutions using big data, advanced analytics and machine learning (thematic report, not binding) |
| Official source | eba.europa.eu ↗ |
| Use cases | Model risk management · AI governance (general) · Credit scoring & underwriting · Fraud detection · AML / KYC |
What are the key points of EBA Report on Big Data and Advanced Analytics (EBA/REP/2020/01)?
- Published January 13, 2020 as EBA/REP/2020/01 after an EBA survey of institutions on BD&AA use.
- Four key pillars for BD&AA roll-out: data management, technological infrastructure, organisation and governance, analytics methodology.
- Elements of trust: ethics, explainability and interpretability, fairness and bias avoidance, traceability and auditability, data protection, consumer protection, security.
- Observes that most reported use cases at the time were in fraud detection, AML, credit scoring and customer engagement, with many still at pilot stage.
- Explicitly framed as supporting technology-neutral regulation and supervision rather than proposing new rules.
- Formed the basis for later EBA work on ML in IRB models (2021–23) and for the AI Act mapping exercise (2025).
What did EBA Report on Big Data and Advanced Analytics (EBA/REP/2020/01) change for banks?
Before 2020 there was no EU-wide banking-supervisory articulation of expectations for machine learning. This report gave national supervisors and the ECB a common vocabulary — explainability, traceability, bias avoidance — that later reappears in the ML-for-IRB papers, the ECB's model-approval practice, and the EBA's reading of the AI Act. Banks still cite it as the origin of EU 'trustworthy AI in banking' expectations.
What are the EBA's 'elements of trust' for advanced analytics?
Ethics, explainability and interpretability, fairness and bias avoidance, traceability and auditability, data protection, consumer protection, and security — set out in the EBA's January 2020 Report on Big Data and Advanced Analytics (EBA/REP/2020/01).
Is the 2020 EBA big data report binding on banks?
No. It is a thematic report describing trends and key considerations, intended to support technology-neutral supervision. It is not guidelines under Article 16 of the EBA Regulation, but supervisors draw on it in dialogue with banks.
| Date | Document | Status |
|---|---|---|
| Jul 31, 2026 | ESA Statement on ICT risks from frontier AI models (JC 2026 25) — ESA Statement: Toward a consistent and risk-based approach for ICT risks from frontier AI models | In force |
| Nov 21, 2025 | EBA factsheet: AI Act implications for the EU banking and payments sector — AI Act: implications for the EU banking and payments sector | Final |
| Nov 21, 2025 | EBA Chair letter to the Commission on the AI Act mapping exercise (EBA/2025/D/5384) — Outcome of EBA's AI Act mapping exercise — letter to DG FISMA and DG CNECT | Final |
| Oct 1, 2025 | EBA Work Programme 2026 — EBA Work Programme 2026 — AI Act implementation and digital-finance priorities | In force |
| Sep 25, 2025 | EBA report: Rising application of AI in EU banking and payments (Sep 2025) — Rising application of AI in EU banking and payments sector | Final |
| Aug 4, 2023 | EBA follow-up report on machine learning for IRB models (EBA/REP/2023/28) — Machine Learning for IRB Models — Follow-up report from the consultation on the Discussion paper on machine learning for IRB models | Final |
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