AI Model Risk & Validation
For the people who have to approve the models. A clear method to validate AI without needing to be the one who built it.
Someone has to approve the AI and machine-learning models a firm relies on — and that person often didn’t build them and can’t see every line of how they work. The job is to challenge, validate and sign off with a process that holds up under independent review. This full-day workshop gives model-risk and second-line teams a clear, defensible method to do exactly that.
Model-risk, validation, data-science oversight and second-line teams.
Challenge, validate and sign off AI and machine-learning models with a clear, defensible process.
Established model-risk supervision — the principles behind guidance like SR 11-7, which call for independent validation and effective challenge of any model used in decisions — increasingly applies to AI and machine-learning models, not just traditional ones. Supervisors across major markets have signalled the same, so the validators who sign off now need a method that stretches to models that learn and drift.
- Judge whether an AI or machine-learning model is fit for its intended use without having built it
- Validate the data, training and performance behind a model in plain language
- Assess bias, drift and the monitoring a model needs once it's live
- Put effective-challenge questions to a model team — the independent scrutiny supervisors expect
- Document validation work so it holds up when an auditor or regulator reviews it
- Reuse a validation checklist and sign-off template for every model that comes for approval
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What makes an AI model fit (or unfit) for use
- Judging conceptual soundness: is the model right for the problem it's solving
- Matching model type and complexity to the use and its risk
- Assumptions and limitations that make a model unfit for its purpose
- Why opaque 'black-box' models are harder to validate, and what to demand
- Applying model-risk principles (such as those behind SR 11-7) to AI and machine learning
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Validating data, training and performance in plain language
- Checking the data: quality, sources, representativeness and consent
- How a model was trained and tested, and why that matters for validation
- Reading performance results without being a data scientist
- Testing on data the model hasn't seen, and watching for overfitting
- Spotting where strong demo results won't hold in the real population
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Bias, drift and monitoring after a model goes live
- How bias enters a model and how to test for it
- Model drift: a model quietly getting worse as the world changes
- The monitoring needed once real decisions depend on the model
- Thresholds and triggers for revalidation or retirement
- Ongoing validation, not just a one-time sign-off
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Challenging a model team: the questions that matter
- What 'effective challenge' means and why supervisors expect it
- Staying independent of the team that built the model
- The specific questions that surface weak assumptions and gaps
- Pushing on limitations the model team may downplay
- Knowing when to withhold sign-off and what to require first
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Documenting validation so it holds up under review
- What a defensible validation record contains
- Capturing tests run, results, limitations and conclusions
- Recording the challenge process and how concerns were resolved
- Keeping documentation an auditor or regulator can follow later
- Aligning records with model-risk and audit expectations
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A validation checklist and sign-off template
- Walking through a reusable validation checklist step by step
- A sign-off template that records the decision and its basis
- Adapting the checklist to different model types and risk levels
- Setting conditions, limitations and review dates at sign-off
- Embedding the template into your approval workflow
Bring "AI Model Risk & Validation" to your team.
A short conversation about your team, your risk, and the session that would move them. No pitch deck — just the right scope and dates.
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