AI Governance Costs in EU Banking: Meta-Analysis

Coming December 2nd

European banks are facing significant expenses to govern high-risk AI models under emerging regulations. This meta-analysis will bring together findings from EU studies, consulting firms, and industry reports to show what compliance really costs and where AI Governance costs come from.

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  • Tymoteusz Olszewski Head of AI

    We are working on this report to cut through speculation and provide clarity. For EU banks, governing AI is a six-figure annual OPEX per high-risk model – the price of trust, safety, and regulatory permission to scale. What I would suggest is to treat governance as core infrastructure for AI at scale, not an afterthought.

Sneak preview

    • Run rate per high risk model

      €116k–€137k per model / year (steady state) – personnel & oversight, documentation, testing & validation, monitoring & maintenance, data/infrastructure, audit & certification included.

    • Portfolio planning heuristic

      For 10 highrisk models, annual governance OPEX ≈ €1.16–€1.37m; for 20 models, ≈ €2.32–€2.74m (multiply the low/high permodel figures by the number of models). 

    • Conformity assessment choices

      External third‑party audit typically costs €16.8k–€23k per model (per major version). Internal QMS route: €193k–€330k set‑up + €71.4k/year upkeep (amortisable across the portfolio).

    • Where the money goes

      Largest shares typically sit in personnel & oversight and testing/validation, with ongoing post‑market monitoring a frequent driver of variance.

    Who this edition is for

    • CFOs & COOs budgeting AI portfolios and amortising fixed governance overheads.
    • Chief Risk/Compliance & Model Risk leaders aligning three lines of defence to AI Act, ISO 42001 and sector standards.
    • Heads of AI/ML & Data planning validation, monitoring and documentation pipelines that scale with model count.

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