KO Fraud prevention

Kount

Transaction fraud prevention with machine learning risk scoring, run inside your Zenoo flows.

At a glance

Overview

Kount overview

Kount is a fraud prevention platform that scores transactions and account activity using machine learning to spot high-risk behaviour. It suits high-volume merchants and platforms that want accurate fraud decisioning at scale. Inside Zenoo, it runs as a risk-scoring step alongside your identity and screening checks.

Company
Kount
Category
Fraud prevention
Strongest in
High-volume transaction fraud scoring
Best fit
High-volume merchants and platforms

Where it is strongest

Key strengths

What Kount does well.

01

Fraud detection at scale

Community reports cite strong detection accuracy for high-volume transaction processing once integrated.

02

High core availability

Community reports cite around 99% API uptime for the core service after go-live.

03

Support after onboarding

Community reports describe responsive support that helps resolve issues once you are live.

Who it suits

Best for

Where Kount fits, and what to plan for.

Honest notes on the trade-offs, each with a way to cover the gap inside a Zenoo flow.

Best for
  • High-volume merchants and platforms that need accurate transaction fraud decisioning
Considerations
  • Integration effort. Some reports describe documentation gaps and SDK quirks across platforms. In Zenoo, our AI builds and maintains the connector so your team calls one API rather than the native SDKs.
  • Onboarding lead time. Some reports mention a multi-week onboarding and provisioning process. Running Kount in a Zenoo flow with a fallback provider keeps you covered while you configure it.
You

Open the customer's case

What your team sees

The Kount result, beside everything else.

The result sits with screening and review on one case, so nobody checks a second tool to see the whole customer.

LA
Lorraine AshcombeSample case · Personal
In review
KountRecorded
Name and date of birthAgrees
PEP screeningSuggested
Name matches a list entry, but the date of birth differs. Suggest you clear as a false positive.Zenoo suggests, your team decides
Zenoo

Hands it to your team

You

Tomorrow, your case

Bring us a case that went wrong.

We will run it through Zenoo while you watch: the evidence, the decision, and the record your MLRO would want to defend.

30 minutes. Your data. No slides.