Controls built for slower money

Solutions

Common questions

What operators ask before the first call

How long before an AI agent in AML or fraud triage pays for itself?

Investigator time is the expensive constraint, and most queues run 90%+ false positives, so the payback clock starts as soon as narrative drafting and evidence assembly move off analyst desks — typically within one or two exam cycles. The bigger driver of timing isn't the model, it's how fast your model risk team can review and approve the control before it touches production alerts.

What data do we need before we can start?

Read access to the alerting system, a sample of disposed alerts with investigator rationale, and the case management system where narratives get filed. You don't need a unified data lake. What you do need is clarity on which fields your MRM function will require to be logged for every automated recommendation, because that determines the integration scope more than the data itself does.

How do we keep an agent auditable for regulators and examiners?

Every alert triage or narrative draft carries its source evidence, the rule or model output that produced it, and a record of what the analyst did with it — accept, override, escalate. That trail has to exist before the agent goes live, not be reconstructed for an exam. We build documentation to the standard your model risk management function already applies to other decision systems, so it slots into existing governance rather than requiring a new one.

Will this pass model risk management review?

It should, if it's built to the standard — documented development, independent validation, ongoing monitoring, and a defined owner. Most AI agent vendors aren't built with MRM in mind and stall at review. We design for SR 11-7-style scrutiny from the first workflow, including drift monitoring and override tracking, because a control that can't clear MRM never reaches production regardless of how well it performs in testing.

Can an agent ever make a final decision without a human?

On consequential outcomes — a suspicious activity report filing, a credit decision, a customer-facing fraud block — no, not without an explicit, governed exception process. Agents draft, score, and prioritize; a human retains the final call on anything with regulatory or customer-facing consequence. Full automation is reserved for lower-stakes, reversible actions like reconciliation break classification.

Perspectives