Why Feedzai Exists: Fighting Fraud at Speed and Scale
May 3, 2018
ONE SINGLE RISK PLATFORM
FOR END-TO-END FRAUD PREVENTION
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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
Resources
Webinar
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?
Ebook
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
Report
Operationalizing Machine Learning For Fraud
Get the Report
Key Considerations For Evaluating AI For Fraud Prevention
Read this free 11-page report and get actionable insights to help you create a strategy for taking charge of the AI disruption that’s well underway.
The Future is Feedzai
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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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Schedule a Demo
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.
