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AI Governance & Risk for Financial Services

Regulator-grade AI governance using your real workflows and failure modes — not stock demos. Built around the controls and risk your team owns.

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Compliance, risk, model-risk and audit teams are now expected to govern AI systems the business has already bought or built — often without a clear framework for doing so. “AI governance” can sound abstract until a regulator asks how a specific model was approved, monitored and documented. This workshop turns it into concrete controls your team can actually run, using your real workflows rather than stock demos.

Who it’s for

Compliance, risk, model-risk and internal-audit teams inside regulated financial firms.

What your team walks away with

Assess and govern an AI system against your regulator’s expectations — model risk, explainability and the failure modes that draw attention.

Why this matters now

Supervisors have set out clear expectations for how financial institutions manage AI across its whole life cycle — in financial-regulator guidance, in long-standing model-risk supervision, and in the EU AI Act. The pressure now is to show a working framework, not a policy document that no one maintains.

What you’ll learn
  • Describe what 'AI governance' means as concrete controls and records, not an abstract policy
  • Find AI use across your business lines and grade each system by the risk it carries
  • Set clear demands for third-party and embedded AI and verify vendor claims instead of trusting them
  • Explain explainability, bias and model drift in terms your whole team can act on
  • Document AI decisions so the record survives a regulator's questions later
  • Stand up an AI risk register your team will actually keep up to date
Curriculum

  1. What "AI governance" really means inside a regulated firm

    • The people, controls and records a supervisor expects to see, not a glossary
    • Governing AI across its whole life cycle: design, deployment, monitoring, retirement
    • How global expectations (EU AI Act, model-risk principles, financial-regulator guidance) line up
    • Roles and responsibilities across the three lines of defence
    • Turning a governance policy into controls people actually run

  2. Spotting and grading AI risk across your business lines

    • A simple method to discover where AI is being used, including 'shadow' AI no one logged
    • Building an inventory of AI systems and what each one is used for
    • Risk-tiering each system by impact, autonomy and who it affects
    • Flagging the high-risk use cases that need the most oversight
    • Keeping the inventory current as new tools appear

  3. Third-party and vendor AI: what to demand and how to check it

    • The questions and evidence to require from an AI vendor before you rely on them
    • Auditing embedded AI inside tools you bought rather than built
    • Testing vendor claims about accuracy, bias and security instead of taking them on trust
    • Contract terms, data handling and the right to review or audit
    • Where responsibility stays with your firm even when the model is someone else's

  4. Explainability, bias and model drift in plain terms

    • What explainability really means and how much you can reasonably demand
    • How bias enters a model through data and design, and how to test for it
    • Model drift: how a model quietly gets worse over time as the world changes
    • Monitoring signals that tell you a model is degrading or behaving unexpectedly
    • Communicating these concepts to non-technical colleagues and committees

  5. Documenting AI decisions so they survive a regulator's questions

    • What a defensible record of an AI decision looks like
    • Capturing why a system was approved, how it was tested and who signed off
    • Logging model versions, changes and the data behind a decision
    • Keeping evidence retrievable months or years after the fact
    • Aligning documentation with what supervisors actually ask for

  6. Building a practical AI risk register your team will actually maintain

    • What belongs in an AI risk register and what's just noise
    • Linking each AI system to its risks, owners and controls
    • Setting review cadences so the register stays alive after the audit
    • Connecting the register to escalation and reporting
    • Tailoring a register template to your team's real workflows

Bring "AI Governance & Risk for Financial Services" 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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