FR Fraud prevention

Fraud.net

AI fraud detection and risk scoring that runs inside your Zenoo onboarding and payment flows.

At a glance

Overview

Fraud.net overview

Fraud.net is an AI-driven fraud detection and risk-scoring platform with KYC and KYB checks aimed at fintechs and financial services. It suits US and EU focused teams with straightforward, lower-volume verification needs that value quick integration. It offers documented paths for common stacks such as Node.js and Python.

Company
Fraud.net
Category
Fraud prevention
Strongest in
Quick setup for US and EU checks
Best fit
Lower-volume US and EU fintechs

Where it is strongest

Key strengths

What Fraud.net does well.

01

Quick initial setup

Community reports describe fast setup for smaller teams, with sub-2-second verification for most US users on basic flows.

02

Workable for basic EU KYB

Some reports cite high success rates for straightforward EU business checks with a simple dashboard.

03

Documented common stacks

Forum mentions note reasonable documentation for Node.js and Python integrations.

Who it suits

Best for

Where Fraud.net 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
  • US and EU focused fintechs with lower-volume KYC and KYB needs
  • MVP builds where quick integration matters most
Considerations
  • Coverage outside US and EU. Some reports suggest accuracy drops for international entities, particularly APAC business checks and some passports. Running Fraud.net inside a Zenoo flow lets you route those geographies to a specialist provider as a fallback.
  • Peak-load reliability. A few reports mention rate limiting and intermittent issues during peak hours. Orchestration through Zenoo gives you automatic failover to a second provider so onboarding continues.
  • Pricing transparency at scale. Some reports note per-verification costs that rise with volume. Zenoo lets you call it selectively and mix in other sources where it fits.
You

Open the customer's case

What your team sees

The Fraud.net 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
Fraud.netRecorded
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
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.