2018 Whitepapers

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How Intelligent Customer Data Drives Revenue and Reduces Risk

Neustar details ways in which smart applications of customer data can help companies improve operational efficiencies and ultimately enhance the customer experience.

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Three Ways to Address Regulations and Stay Compliant Without Increasing Consumer Friction

Neustar suggests a three-part approach for companies to achieve compliance while minimizing friction as part of their identity and authentication processes.

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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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