Scalable Fraud Prevention for Retail 2.0

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Merchants

shopping_carts

Platforms

market_places

Marketplaces

gaming

Online Gaming

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Digital Goods

travel

Travel

Stop Fraud in Real Time. Every Time.

Feedzai for Ecommerce is a machine learning based loss prevention platform
that helps merchants proactively detect fraud before it happens.

The Feedzai Solution

Feedzai is a simple to use yet sophisticated fraud detection platform that integrates a high-performance scoring engine with an easy-to-use case management interface and online activity monitoring console.

Technology That Learns™

Feedzai fraud detection models learn from every single transaction in real-time. Constantly updating models help you proactively identify fraud and eliminate reactive manual analysis and rules coding.

Segment of One

Feedzai uses aggregated metrics based on transactional data to build hyper-granular profiles of user behavior. Better understanding of normal and abnormal transactions for each profile result in fewer manual reviews and reduce false positives.

 

Built For Your Entire Risk Team

Manual reviews are time consuming.  Period.

You need a easy to use, intuitive solution that reduces false positives and help you make informed decisions, fast.

Manage your queue easily.
Alert Manager is an easy to use, intuitive interface that let’s you approve multiple cases at a click of a button.

Investigate high risk cases.

Case Manager is your one-stop page to gain greater visibility into detailed historic and real time data to research suspicious transactions.

Make decisions with confidence.

Whitebox scores with simple text explanations for each score let’s you make decisions with greater clarity into model reasons.

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Keeping fraud and chargeback rates under control without increasing resources can be challenging.

You need a predictive fraud prevention system that can uncover hidden connections and deceptive fraud patterns as they occur.

Manage fraud your own way.
Customizable fields let you track risk factors that are unique to your business.

Say goodbye to canned reports.

Customizable reports let you report on key fraud metrics without the need for consolidating data from multiple sources.

Get help when you need it.

Enterprise level account management gives you 1-on-1 support when needed to customize or optimize your fraud solution.

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Commerce evolves and so does the fraud landscape.

As a risk leader, you have to manage not just payment-related fraud but also fraud that extends beyond to internal and external threats.

Protect brand identity and experience.

Use cases beyond payment fraud help you protect your brand experience by identifying policy violations and account takeovers.

Monitor online and offline risk.

Omnichannel intelligence lets you keep your eye on the entire risk landscape across all channels – online and offline.

Keep an eye on key drivers.

Drag-and-drop dashboards lets you monitor and report on key metrics and trends on demand.

 

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Meets All of Your Fraud and Loss Prevention Needs

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Payment Fraud

Identify and prevent fraud for CNP transactions with real time machine learning.

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Policy Violation

Manage internal risk arising from employees abusing corporate policies.

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Account Opening

Keep your platform and marketplace safe by blocking fraudsters from transacting on your website.

How It Works

Feedzai is a robust platform that can be deployed as an on-premise or cloud solution.

Integration is easy via a full REST API. Send transaction information and receive risk scores along with detailed explanations of each score to make decisions in real-time.  The API call can be invoked anytime before the payment pre­-authorization stage or post-­authorization stage, or at both stages.

Transaction scoring happens in four phases:

1

Seed & train

Send data to the system

Each business is unique. Fraud models can be trained using data from historic transactions, black/white lists and other data that is specific to your business. This could also include non-transactional data like SKU, employee lists, POS data etc.

2

Track user activity

Track User device and browser information

In addition to receiving transactional data for fraud scoring, Feedzai also helps merchants track behavior before and after login to detect and prevent identity fraud. Integrating with Feedzai’s device fingerprinting technology is simple and can be invoked by embedding code snippet in all the webpages where customer interactions are expected.

3

Score

Score transaction in real time

Feedzai can help you risk score at any point in the transaction journey, either in the pre- or post-payment authorization flow. Once the customer completes the order, the transaction is sent to Feedzai through the main scoring API call.  Depending on the score and recommendation provided by Feedzai, you can choose to accept or decline the transaction.

4

Send feedback

Send your feedback to Feedzai

Feedback cycle is a critical step in a fraud prevention process.  The more feedback the models receive, the more accurate they become and the more fraud they detect.  When analysts make a decision using the alert manager,  feedback about that particular order is automatically sent to Feedzai. However, when a future event on an order changes its state, there will be no alarm to trigger in real-time.   This information can be still be transmitted to Feedzai manually in the alert manager interface or can be automated through an API.

Watch the Video: Life of a Transaction in 3ms

 

Check out the API

Take advantage of our fraud prevention capabilities by integrating your online store’s data with our simple REST API.

View API Documentation

Contact us today to schedule a demo.

Our technology is interesting to read about, but it’s fascinating to see it work in real-time on your own business data.

Contact us today, and we’ll schedule a demo using your own data. 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 immediately demonstrate the value of our machine learning approach.

Learn More