The risk hides in the tail.

Maati hardens the AML and risk models banks deploy, surfacing the rare failures a model has never seen before regulators or attackers find them.

25k synthetic edge cases
per assessment
0 customer records
or PII required
+33% average coverage
improvement
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The problem

Models only learn what they see.

An AML model trained on last year's patterns has no signal for a novel attack vector — and it never will, because that case was never in the data.

The gap

Rare patterns are invisible at training time.

A laundering ring moving funds across a handful of transactions a year never appears in a training split. The model has nothing to learn from.

The method

Synthetic adversaries fill the gap.

Maati generates rare, tail-of-distribution scenarios per assessment: adversarial behaviors your model has never encountered, on synthetic data alone.

The output

Audit-ready evidence. Not a slide deck.

Every failure mode mapped to a control, the mislabeled ground truth corrected, and a documented robustness report a supervisor can read.

The result

Deploy with confidence.

Model governance that holds up in a supervisory review, a board discussion, or a live incident. Proof you hardened the tails before anyone else found them.

Request access

Harden a model before it ships.

For model risk and AML teams at banks and fintechs. Tell us about the model you need to defend, and we will show you where it breaks on synthetic data, with no procurement delay.

We reply within two business days. No data leaves your environment.