How a GCC bank cleared its AI model review backlog in 30 days

The challenge

A retail and corporate bank in the GCC had moved fast on AI. More than 40 models were in use or in pilot across credit, fraud, collections and customer service. The appetite was not the problem. The problem was governance: there was no single register, no consistent control mapping, and no audit trail the regulator would accept at the next supervisory review.

The bank's own teams had stalled. Risk could map the frameworks but could not run the model-by-model review. Technology had built the models but could not evidence the controls. Each function waited on the other, and the backlog grew.

What we did

We ran a fixed-scope review on a 30-day clock. One principal owned delivery end to end.

We built a single model register, then mapped each model to the controls the regulator expects: data lineage, validation, human oversight, and a defined escalation path. Where a control was missing, we wrote the remediation as a dated action with a named owner, not a recommendation. Every decision was logged.

The output was an audit trail, not a slide deck: a model register, a control mapping, and an audit log the bank could hand to its supervisor.

The outcome

At the end of 30 days, the 40+ models were back in a governed pipeline. The audit trail was accepted at the next supervisory review without a follow-up finding on process. The engagement ended on its defined stop point. There was no open-ended retainer.

This is a representative engagement pattern drawn from how we work, not a named client reference. The numbers describe the shape of the mandate, fixed scope, fixed price, one principal, a defined stop point, so you can judge whether the same approach fits your function.