AI Transformation & Model Governance
We build AI capability across the institution, with the governance and controls needed to support it with a regulator.
Readiness and strategy
Establishing what you have, what AI can do for you and in what order.
Readiness and strategy
Establishing what you have, what AI can do for you and in what order.
- Data mapped and classified across the organisation with owners and sensitivity levels assigned
- Infrastructure, tooling, skills and organisational appetite assessed
- Use cases identified with your teams and ranked on impact against feasibility
- A roadmap covering budget, ownership and sequencing
Proof of concept to production
Testing one use case before committing to it.
Proof of concept to production
Testing one use case before committing to it.
- Scope, success criteria and tooling agreed in advance
- Built and run in a controlled environment against real data
- Measured against the criteria set at the start
- A recommendation to scale, refine or stop
Enterprise AI deployment
Making general-purpose AI tools available to staff and supporting day-to-day use.
Enterprise AI deployment
Making general-purpose AI tools available to staff and supporting day-to-day use.
- Knowledge systems that make internal reports, presentations and documents searchable
- Internal assistants covering policies, products, procedures and regulation, available on intranet, portal or messaging
- Agents for routine drafting, reporting and research
- Local model deployment where material cannot leave the institution
- Delivered with the training and change management that adoption requires
Training and capability building
Building the skills to run AI in-house.
Training and capability building
Building the skills to run AI in-house.
- Executive sessions on AI strategy, digital roadmaps and responsible AI decisions
- Functional programmes for credit, risk, underwriting and digital finance teams
- Data and AI foundations for all staff
- GenAI and prompting workshops with banking use cases
- Delivered through an online hub, workshops and a train-the-trainer model so it continues without us
AI governance framework
The policies, committees and controls for using AI in decisions that affect customers.
AI governance framework
The policies, committees and controls for using AI in decisions that affect customers.
- Enterprise AI policy, standards and controls aligned to regulatory expectations including CBUAE guidance
- A central model inventory with models tiered by materiality and potential impact
- Lifecycle standards from scoping through design, validation, deployment, monitoring, change control and retirement
- Committee structure, decision rights, accountability and human oversight
Validation, monitoring and audit
Independent testing of models before deployment, while in production, and in review.
Validation, monitoring and audit
Independent testing of models before deployment, while in production, and in review.
- Pre-deployment and periodic validation for accuracy, stability, robustness and fairness
- Explainability methods so risk, compliance and model owners can see how a decision was reached
- Bias testing and drift monitoring with thresholds, alerts and escalation routes
- Independent review of models already in use, covering where a model could cause harm: bias, mis-scoring, misclassification, compliance failure
- Models mapped against Basel III, IFRS 9, ICAAP, GDPR and the EU AI Act
- Assessment of emerging threats including data poisoning and post-quantum exposure
- Reports and evidence packs prepared for supervisory review, with remediation tracked
Who it's for
CROs, CCOs, Heads of Internal Audit, CIOs, Chief Data Officers