Nilson Report

Issue 1068 | Jul 2015

FEATURED COMPANIES

Companies featured in this issue include:

Card Fraud Worldwide 1993 - 2014

Card Fraud Projected - Global Losses

Market Capitalization & Revenue for Payment Companies

U.S. Visa & MasterCard Commercial Cards

Largest U.S. Issuers of Consumer Visa & MasterCard Cards 2014

Growth in Fraud vs. Total Card Volume Worldwide

Card Fraud in Basis Points

Merchant Acquirers in Middle East/Africa 2014

Merchant Acquirers in Middle East/Africa 2014

Ranked on page 11 are the 30 largest merchant acquirers in the Middle East/Africa region. Those acquirers are based in 12 countries.

Absa
610.9 mil. V/MC Transactions
First National Bank
446.2 mil. V/MC Transactions
Standard Bank
280.8 mil. V/MC Transactions
Nedbank
211.7 mil. V/MC Transactions
Network International
146.2 mil. V/MC Transactions

Full access to the Merchant Acquirers in Middle East/Africa 2014 is available when you subscribe to The Nilson Report. 


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POSTED AUG 4, 2015 | PRINT

Sift Science Ecommerce Fraud Protection

Large-scale, real-time, machine-learning-based ecommerce fraud protection is the business of Sift Science. Its technology lets merchants create rules for automated decisions aimed at reducing challenges to authorizations of card payments that result in lost sales. Sift Science says its platform lowers the false positive rate at ecommerce merchants to about 20%, significantly reducing manual reviews. These merchants also receive lower chargeback rates and catch fake account, account takeover, and referral fraud. 

The company started fighting ecommerce fraud four years ago. Today it collects data from 2,000 online sellers, about 20% that comes from outside the U.S. Payment data, including remittance, digital wallet, digital prepaid card, and digital currency types, is pooled to benefit all of its merchant customers. Sift Science’s merchants recently gained access to an application programming interface (API) that lets them control their customers’ device IDs. The new tool supports automation of fraud management decisions, including providing the ability to take immediate action for and against known devices. Sift Science has used device fingerprinting since its launch, but hadn’t allowed merchants to control that feature.

Machine learning is supervised, meaning the system learns from fraud analysis of those transactions that are “bad” and “not bad.” This gives algorithms the opportunity to identify commonalities in behavioral and identity characteristics from previous fraud activity.  

Sift Science recently received $18 million in Series B funding, led by Spark Capital. 

Jason Tan is CEO at Sift Science in San Francisco, California, (206) 412-8046, jason@siftscience.com, www.siftscience.com.



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