Whitepapers

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.

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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.

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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.

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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.

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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.

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Making the Move to Dynamic Multifactor Authentication

iovation takes a detailed look at multi-factor authentication and explains that it can, when used in a dynamic context, offer enhanced security and a better user experience.

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Staying a Step Ahead of the Fraud Footprint

Acapture and Sift Science explore top fraud threats for online merchants and discuss how machine learning can be leveraged to combat these challenges.

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Fighting Fraud Without Wounding Customer Experience

Simility explores the intersection of fraud prevention and customer experience, and examines the impact risk mitigation can have on customer trust.

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Five Emerging eCommerce Fraud Threats and How to Stop Them

Whitepages examines key eCommerce and mobile commerce risk trends, and ways online businesses can help protect themselves.

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Mission Critical Artificial Intelligence & Machine Learning

Discover the advantages and drawbacks of key machine learning and artificial intelligence techniques, and the implications these characteristics have for fraud and risk mitigation.

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