AI Consulting for Financial Services in the UAE

Chief Risk Officer (Regional Bank)

Our business side wants to move quickly on generative AI in customer service and credit memo drafting. I am the one who has to answer for it. What should I be asking of any consulting partner before they touch a regulated process?

AI Leadership Agent — Governance Advisory

Ask them to describe the control environment they will build before they describe the use cases they will deliver. If the second answer is longer than the first, they are not a financial services partner, they are a technology one.

1. How will each system be classified by risk, and what changes at each tier?
2. What evidence will exist for every output that influenced a customer outcome, and for how long will it be retained?
3. How will they validate performance independently of the team that built it?
4. What happens to data — where does it sit, who processes it, and does anything leave the jurisdiction?
5. How will they demonstrate that a human remained accountable for the decision, not merely present at it?

In a supervised institution the question is never whether the model is good. It is whether you can prove, months later, why a specific customer received a specific answer.

Which of the two proposed use cases worries you more?

Chief Risk Officer (Regional Bank)

Credit memo drafting, because it feeds a decision that affects a customer's access to finance. Customer service feels lower risk but I am aware that is where the volume is. How do I let one move fast without the other moving fast by association?

AI Leadership Agent — Governance Advisory

By separating them structurally: different approval routes, different monitoring, different owners, and an explicit statement that approval of one grants nothing to the other. Drift between tiers usually happens through precedent, not through decision.

1. Credit memo drafting: treat the model as a drafting assistant with mandatory analyst authorship. Require that the analyst's reasoning is captured separately from the generated text, or you cannot later show independent judgement.
2. Keep the model away from the recommendation itself at first. Summarising a file and proposing a rating are different acts with different consequences.
3. Customer service: constrain to retrieval from approved content, log every interaction, and set a hard escalation rule on complaints, hardship, disputes and vulnerable customers.
4. Monitor both weekly against the same set of measures so you can compare behaviour across tiers.

Volume is not the same as risk, but volume is how a small error rate becomes a supervisory finding.

Do you have the internal validation capacity to review these at the pace the business wants?

Chief Risk Officer (Regional Bank)

No. That is the honest bottleneck. Our validation team is small and already stretched by traditional models. Where would you spend consulting money to fix that?

AI Leadership Agent — Governance Advisory

Spend it on building validation capability and tooling rather than on more use cases. It is a less exciting line item and it is the constraint that determines everything downstream.

1. Build reusable evaluation sets per use-case family, owned by validation, so each new system is tested against an existing standard rather than a bespoke exercise.
2. Automate the routine parts of validation — regression testing, drift detection, sampling — so scarce human review goes to judgement, not to collection.
3. Train two validators deeply on generative systems rather than training everyone shallowly.
4. Agree a service standard with the business: a defined review time per tier, published, so pressure is applied to the process rather than to individuals.

In regulated institutions the speed of AI adoption is set by the speed of assurance. Everything else is negotiation.

Fund the bottleneck first and the business will get its use cases sooner than if you fund the use cases.