Decisions_on_Demand <> Running_at_Big_Data_Scale

Whether you are a bank, acquirer, merchant or any player in the commerce value chain, Feedzai is all you need to manage your entire payment risk. Online or offline, card or non-card, Feedzai offers an Any Data. Any Payment.™ risk engine that helps prevent fraud using machine learning.

Unblock Payment and Keep Commerce Moving


Combating Account Opening Fraud using Machine Learning

Watch the Webinar

Watch Aite’s Julie Conroy and Feedzai’s Phong Q.Rock discuss how FIs can apply advanced analytics to the vast amount data at their disposal and turn it into intelligence,which can help to not only reduce fraud,but also remove friction.

Why orchestration of authentication is antidote to account takeover fraud?

Choosing a Machine Learning Platform for Risk(Banking)

Get the Ebook

A Guide for Banks and Payment Processors

Download this report to peek into the future of fraud.Learn more about:
1)Explainable Whitebox AI
2)Precise segment-of-one profiling
3)Future-proof data ingestion and
4)Better enterprise fraud protection

Genome Demo

Feedzai Genome Demo for Banks

Watch the Demo

Watch this demo to see how risk analysts and investigators at banks can:

  • Spot and stop suspicious customer behavior patterns
  • Identify complex criminal schemes such as money laundering and account takeover
  • Increase efficiency

Watch the Demo

The Future is Feedzai

Speed_Scale_and_Performance <> For_Your_Entire_Organization

We’ve built a powerful platform with foresight into next-generation fraud technology that gives real utility to data feeds that come from new and unexpected sources. Feedzai is looking at commerce transactions as an ecosystem model, and we’re disrupting the conventional thinking about risk and pioneering a new approach to risk management.

Ready to clean up your commerce stream?
Schedule a demo with us today.

Our Partners



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With the ground truth of actual chargebacks and other fraudulent behavior as the benchmark, a head-to-head comparison of Feedzai’s detection performance using past transaction logs can demonstrate the value of our machine learning approach.