Context. An international fintech operator running multiple product lines, hitting onboarding bottlenecks as it expanded into new jurisdictions. The customer is anonymised.

The problem

Hard-coded automation, too rigid for edge cases. Pass or fail logic could not handle anything unusual. Roughly 10 percent of high-value applications fell into manual review.

Revenue delayed by review queues. High-value customers waited while their files were read by hand. Time-to-yes lengthened as volume grew.

Changes needed engineering. Adjusting a risk parameter or swapping a data provider required a release. That agility gap blocked fast responses to regulatory change.

What we did

An orchestration layer. Replace pass or fail logic with multi-step reasoning. Cases were assessed through reasoning across the whole file rather than a single rule set.

Assess edge cases with multi-step reasoning rather than fixed rules.

Route only genuine exceptions to a human reviewer.

Change risk parameters without an engineering release.

The AI Data Room. Automate the edge-case review itself. Documents such as passports and bank statements were analysed autonomously, with recommendations aligned to the company's risk appetite.

Classify and read documents across the whole application.

Produce a recommendation aligned to the defined risk appetite.

Keep the evidence and reasoning attached to every case.

A copilot, not a decision engine. Keep the decision with the risk team. Risk Llama recommends and the team decides, so automation did not remove accountability.

Return a scored case with the reasoning visible.

Leave the final approval with the risk team.

Keep a full audit trail on every automated review.

The impact

70 percent reduction in manual case reviews. The queue of stuck applications largely cleared.

Analysts refocused on strategic risk assessment. Time moved from data gathering to judgment.

A shorter time-to-yes for high-value customers. Revenue arrived sooner and customers waited less.

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