Block the 0.97s.
Wave through the 0.03s.
Adaptive models score every authorization in 12ms — inside the auth flow, not after it. Card testing, cloning, first-party abuse and account takeover, caught before the response leaves the edge.
- Score latency
- 12ms
- Score latency
- Fraud loss rate
- 0.02%
- Fraud loss rate
- Losses after switch
- −91%
- Losses after switch
Trained on the whole network.
Tuned to your programme.
400+ features per auth
Velocity, device, geography, merchant history, amount patterns, network reputation — computed at the edge, in-line.
Learns per programme
A travel card and a teen card have different "normal". Models adapt to each programme's traffic without manual rules.
Decline is the last resort
Approve, step up to 3DS, hold for review, or decline — graduated responses keep good customers moving.
Every score, itemized
Feature-level contributions on every decision — for your risk team, your auditors and your cardholder support.
Rules where you want them
Hard limits, geo-blocks and MCC policies run alongside the models — deterministic where regulation demands it.
Disputes close the loop
Chargeback outcomes feed straight back into training — the model gets better with every case you win or lose.
“Fraud AI paid for the platform in month two.”
Karta moved 800k cards to Rigid and turned on adaptive scoring with zero manual rules. Losses fell 91% in eight weeks — while approval rates went up 2.3 points.
Aline Souza · Head of Risk, Karta
fraud losses −91%
Run your last month's traffic through it.
We'll replay your historical auths against Fraud AI and show you what it would have caught.