Whitepapers
US Cross-Border eCommerce Report
PPRO examines how global eCommerce is changing and outlines what obstacles U.S. merchants need to overcome to ensure cross-border success.
Buy Online Pick Up In Store (BOPIS)
Signifyd takes a detailed look at the boom behind the fulfillment option buy online, pick up in store, including the risk challenges it poses to retailers and seven key best practices to minimize fraud losses while ensuring a strong experience for genuine customers.
A User-Centric Approach to Preventing Threats Beyond Account Takeover
Castle examines how developing a user-centric model allows companies to focus on enabling the good in addition to stopping the bad.
Five Things You Need to Know Before Accepting Payments from Russia
Ingenico ePayments reviews key facts about the Russian eCommerce landscape and reveals essential tips merchants should consider before doing business in this large, complex market.
Leading the Digital Trust & Safety Transformation -- Unlock New Revenue Without Risk
Sift explores the need for businesses to be agile and adaptable in this changing digital economy.
Synthetic Identity Fraud in the U.S. Payment System
The Federal Reserve explains what synthetic identity fraud is and examines why this type of financial crime is becoming a pervasive problem in the U.S. payments ecosystem.
How to Create a Fraud Prevention Unit
The Merchant Risk Council, in collaboration with a subgroup of the MRC Fraud Community, shares best practices on how to set up a fraud prevention department at a company where no such unit previously existed.
2018 Voice Intelligence Report
Pindrop discusses the growing problem of fraud originating from the voice channel and which industries are particularly impacted by this threat.
The 7 Habits of Successful "New" Payments Leaders
SafeCharge and Edgar, Dunn & Company share key findings and best practices from their survey of a dozen payments leaders across disparate industries and geographies.
Why Machine Learning for Fraud Detection?
Ravelin examines the process of moving to a machine learning platform, including the methodology behind scoring and risk thresholds, and discusses the impact machine learning is having on fraud analysts.