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
A starting agenda — every session is shaped around your team, your tools and the risks you’re managing.
- What makes an AI model fit (or unfit) for use What actually makes an AI model fit — or unfit — for the use it’s being put to, and how to judge that without having built it.
- Validating data, training and performance in plain language How to validate the data behind a model, how it was trained and how it performs, explained in plain language.
- Bias, drift and monitoring after a model goes live Bias, drift (a model quietly getting worse over time) and the monitoring needed once a model is live and decisions depend on it.
- Challenging a model team: the questions that matter The specific questions to put to a model team to provide effective challenge — the independent scrutiny supervisors expect before sign-off.
- Documenting validation so it holds up under review How to document your validation work so it holds up when an auditor or regulator reviews it later.
- A validation checklist and sign-off template A validation checklist and sign-off template your team can reuse for every model that comes for approval.
Every team’s needs are different. We’re happy to talk it through and tailor the session to yours — let’s talk →
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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