AI Use-Case Strategy & Investment Case
Build and prioritise an AI portfolio against business value, feasibility, data readiness, control requirements and total cost.
AI & Automation
AI Model Risk & Governance
Establish AI oversight that fits the use case and the institution.
Overview
What this means for your business.
AI governance connects accountability and risk assessment with the way models are developed, acquired and used. It needs to cover the applications and decisions within the organisation's scope.
Capmark helps clients extend model-risk and AI governance arrangements across machine learning, generative AI, copilots and agents. Our work includes inventory, tiering, validation, evaluation, human oversight and monitoring.
We connect these arrangements with existing model-risk, third-party, privacy and operational controls so responsibilities and evidence remain clear.
What we do
The expertise we bring.
We extend model risk policy, tiering, validation standards, monitoring and review cadence to cover AI systems that learn, drift or resist simple explanation. The framework is designed with second line and tested against live use cases.
We define what belongs in the inventory, including AI assistants, vendor-embedded tools and agents that act across systems. Each use case receives a tier, owner, review cycle and control path proportionate to its impact.
We build evaluation methods for LLM and AI systems, including curated test sets, accuracy and error measurement, adversarial testing, change testing and reproducible evidence. The standard is designed to support second-line review.
We map applicable AI, model risk, third-party and operational-resilience obligations into the control framework. The aim is a single practical control set, not overlapping governance that slows delivery.
We review individual models, AI use cases and full frameworks with documented methodology. Where findings already exist, we sequence remediation and build the evidence needed to close them.
What to expect
A team shaped around the work.
A Senior Practitioner leads from day one. The first weeks review the model inventory, AI use cases, policies, validation capability and open findings, with particular focus on AI systems operating outside the current framework.
We then draft the policy, tiering and validation standards with second line, test them against live use cases, and establish the monitoring and review cadence.
Engagements range from a framework review to independent validation of a model, AI use case or model family.
Engagements may cover governance design, independent review or the oversight requirements of a specific AI application.
01
Assess
Establish the current state, the constraints, the risks and the value at stake.
02
Define
Shape the target model and the business case with the executives who own the outcome.
03
Mobilise
Stand up the team, the plan and the governance around the outcome.
04
Implement
Design, build and test the change, with the business alongside.
05
Sustain
Cutover, hypercare and handover, so the business runs it under its own control.
The same five stages on every engagement, led by senior practitioners end to end. How we work
Client result
Relevant client experience.
Asset & Wealth Management · Financial Crime
Financial-crime controls rebuilt for the world's largest alternative asset manager
The World's Largest Alternative Asset Manager · Financial Crime Transformation
The firm partnered with us to convert regulatory findings on AML control deficiencies into a resilient financial-crime framework: customer risk assessment, screening, enhanced due diligence and AI-assisted alert reduction, remediated to completion under independent scrutiny.
- 100%
- AML findings closed and evidenced to the independent monitor
- 3
- Control domains rebuilt: customer risk assessment, name screening and enhanced due diligence
- 5
- Interlocking initiatives directed
Read the case study
Insights
Related perspectives.
Get in touch
Discuss AI Model Risk & Governance.
Tell us what you are working through and the expertise you need.