Payments Fraud Detection and Prevention Solution | Kount

Digital enablement solutions for a frictionless customer journey

Kount combines digital identity trust data with Equifax’s physical identity attributes to help businesses identify fraudsters, assess risks, engage with good customers, grow their revenue potential, and ensure regulatory compliance.


Identify solutions
Fuse digital and physical identity attributes to gain a true 360-degree view of your customers.

Assess solutions
Use advanced AI and machine learning to prevent criminal fraud and deflect customers disputes and friendly fraud.

Engage solutions
Collect, analyze, and leverage
data from interactions across the customer journey to create personalized, streamlined experiences.

Grow solutions
Anticipate and customize exchanges with your customers using data-driven up-sell and cross-sell recommendations based on their known preferences.

Comply solutions
Minimize risks by equipping entities to avoid penalties, follow data privacy regulations, verify identities, and perform due diligence checks.

Ensuring trusted interactions across the customer journey

Kount’s technology solves a variety of fraud use cases

Kount’s Identity Trust Global Network™ is the largest data network of fraud and trust-related signals. It links unique fraud and trust identifiers from billions of annual interactions and decisions from thousands of fraud analysts to block or challenge fraud in real time.

Kount’s advanced AI uses two types of machine learning to produce reliable transaction safety ratings in real time. Supervised machine learning evaluates past interaction decisions, and unsupervised machine learning detects emerging fraud attacks. Quickly and accurately weigh fraud risks against a customer’s value.

Kount’s technology also includes:

  • Trusted device management capabilities
  • Robust email insights and analytics
  • Customizable fields to further automate and refine order decisions

>> Explore Kount’s technology

Get the right fraud solutions for your business


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