AI Risk for Boards & Compliance Teams

Boards, audit committees, CROs, Heads of Compliance and MLROs at banks, payments, asset managers and digital-asset firms.

Challenge an AI or crypto risk decision in your own firm — and ask the questions that surface real exposure before it becomes a headline.

Regulators have moved from broad principles to concrete expectations. Across major markets, supervisors increasingly name the board and senior management as the ultimate owners of AI risk, holding them to principles of fairness, accountability and transparency. The same theme runs through the EU AI Act and established model-risk supervision. “We left it to the tech team” is no longer a defensible answer in the room where sign-off happens.

• Tell apart the AI risks that genuinely sit with a regulated firm from the vendor hype around them
• Ask the handful of probing questions that expose weak AI controls before you approve a system
• Recognise what regulators expect when AI touches financial crime, fraud and customer decisions
• Understand the extra risks your board signs off on when the firm holds, moves or services digital assets
• Draw clear accountability lines between the board, the risk function and the business for any AI decision
• Run your committee's AI and crypto sign-offs against a repeatable oversight checklist

1. Where AI actually creates risk in a regulated firm (and where the hype is wrong)

   • How AI shows up in a regulated firm today: customer decisions, credit, fraud, financial crime, productivity tools
   • The real failure modes: biased or wrong decisions, opaque 'black-box' models you can't explain, data leakage
   • Generative AI versus traditional models, and why each carries a different kind of risk
   • Separating genuine exposure from vendor marketing and 'AI-washing'
   • What the board can see and own, versus what stays inside the technical detail

2. The questions a board should ask before approving any AI system

   • The short set of challenge questions that work for any AI proposal
   • Probing whether a use case is high-risk and what would happen if the model is wrong
   • Asking how a decision can be explained, evidenced and reversed
   • Checking that a human is accountable, not just the system
   • Spotting when a confident vendor pitch is standing in for real scrutiny

3. AI in financial crime, fraud and customer decisions: what regulators expect

   • Why supervisors expect AI decisions to be explainable, fair and documented
   • Fairness and bias when AI affects customers, lending and access to services
   • The expectation that a person, not the model, owns a financial-crime or customer decision
   • Record-keeping that lets the firm show how an AI outcome was reached, months or years later
   • How global frameworks (EU AI Act, model-risk principles) frame board and senior-management responsibility

4. Digital assets and crypto: the extra risks your board is signing off on

   • Custody, on-chain exposure and what 'holding' or 'moving' digital assets actually means for the firm
   • The financial-crime rules that apply to crypto activity, including the FATF Travel Rule
   • Wallet, transaction and counterparty risks that traditional controls don't catch
   • Where AI is being used in crypto compliance and where it gives false confidence
   • The reputational and regulatory stakes specific to digital-asset activity

5. Who owns AI risk — drawing clear lines between the board, risk and the business

   • The three lines of defence applied to AI, in plain terms
   • What the board owns versus the risk function versus the business that deploys the tool
   • Avoiding the 'we left it to the tech team' gap that regulators no longer accept
   • How AI oversight fits into existing risk committees and reporting
   • Setting escalation triggers so serious AI issues reach the board in time

6. A simple oversight checklist your committee can adopt straight away

   • Walking through a board-ready AI risk oversight checklist line by line
   • Tailoring it to your firm's AI and digital-asset footprint
   • Using it consistently for every future AI or crypto approval
   • What evidence to ask for before signing off, and what to record after
   • Reviewing AI risk on an ongoing basis, not just at approval