WHITEPAPER

Reducing Identity Fraud in Ecommerce

Balancing Fraud Losses & Customer Experience

With both consumers and fraudsters flocking online during the pandemic, online identity verification and the balance of fraud prevention and customer friction are the top two challenges facing ecommerce companies today. They must find new ways of reducing friction across the entire customer journey while detecting fraud seamlessly and cost-efficiently. 

Learn how an effective fraud solution that incorporates identity intelligence will help you make fast, accurate fraud decisions with a higher degree of confidence. 

Download our report for insights on:

  • Common attack scenarios cybercriminals capitalize on in the ecommerce customer journey
  • Why a more thorough understanding of consumers’ risk is critical – and more possible than ever
  • How to incorporate predictive risk scored based on analysis for recaptured underground data in your control framework to prevent more fraud while safeguarding the customer experience

Get the Whitepaper

Reducing Identity Fraud in Ecommerce
Download the PDF version of the whitepaper to print or share with others.

With SpyCloud, you get enterprise-level, automated account takeover and ransomware prevention powered by Cybercrime Analytics based on actionable darknet insights.

SpyCloud offers the largest collection of recaptured darknet data in the world, combined with the earliest possible recovery. Our proprietary engine quickly ingests data from breaches, malware-infected devices, and other underground sources, then cleanses and enriches the data – adding context to the records so you understand the severity of the exposures (the source, breach description, and the actual password in plaintext). Our customers get notifications of compromised accounts and passwords far sooner with SpyCloud than any other provider.

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Billion
Recaptured
Assets
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Billion

Total
Passwords

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Billion

Email
Addresses

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Unique
Data Types

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See your real-time account takeover exposure details powered by SpyCloud data.