AI Governance & Responsible AI for Financial Institutions
Financial institutions already know how to govern models — AI governance is model risk management grown teeth: wider scope, harder law, and board-level accountability for what the machine decides.
Format
Classroom · Virtual
Upcoming sessions
Pick a session to applyADMISSIONS OPENThe programme
Credit scoring, fraud detection, pricing, chat-based service, generative drafting — AI is now inside the decision chain of every serious financial institution, and regulation has caught up: the EU AI Act classifies credit scoring and similar uses as high-risk with obligations biting from 2026, and supervisors from the Gulf to Africa are issuing parallel guidance. This programme turns those obligations into an operating governance system: the AI inventory and risk classification that everything else depends on; governance structures — board accountability, three lines, the approval gates an AI use case passes through; the control set for high-risk uses: data quality, documentation, human oversight, monitoring, incident response; validation and testing extended from familiar model-risk practice to opaque and generative models; and third-party AI — the vendor, cloud and foundation-model dependencies where most institutions are most exposed. Regulation is read as a design specification, not recited. For risk, compliance, data and technology leaders who must make AI both usable and defensible.
What you will do
Who attends
Chief risk, compliance and data officers; model risk and validation teams; heads of AI, digital and technology; internal audit; board members and executives accountable for AI decisions in banks, insurers and other supervised institutions.
Programme agenda
Built for the decisions no textbook prepares you for
I.The obligation map
- What regulators now require: the EU AI Act read as a design spec, and the parallel guidance wave
- The AI inventory: finding every model, including the ones nobody registered
- Risk classification: which uses are high-risk and what that triggers
II.The governance system
- Board accountability and the three lines applied to AI
- The control set: data quality, documentation, human oversight, monitoring, incidents
- Validation beyond familiar models: opaque, generative, continuously learning
III.The exposed edges
- Third-party AI: vendor contracts, cloud dependencies, foundation models
- Fairness, explainability and the customer-facing duties
- Your roadmap: sequencing compliance before the deadlines bite
Frequently asked
Who should attend the AI governance programme?
It is built for chief risk, compliance and data officers, model risk and validation teams, heads of AI, digital and technology, internal audit, and the board members and executives accountable for AI decisions in banks, insurers and other supervised institutions.
Does the programme cover the EU AI Act?
Yes — the EU AI Act is read as a design specification rather than recited, including the high-risk classification of credit scoring and similar uses with obligations biting from 2026, alongside the parallel guidance supervisors from the Gulf to Africa are issuing. The programme turns those obligations into an operating system: inventory, risk classification, governance structures, the high-risk control set, validation and third-party AI.
Is this legal advice on AI regulation?
No. This is professional training that treats regulation as a design specification for governance — it does not constitute legal advice. Institutions should engage their own counsel on how specific obligations apply to their models and jurisdictions.
Can the programme be delivered in-house and in French?
Yes. BIZENIUS delivers it in English and French, and an in-house edition can be tailored to your model inventory, supervisory context and AI adoption roadmap. Sessions run on a rolling calendar with dates confirmed on request; fees and quotations are provided on enquiry.
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