How to Stop Bad Actors While Maintaining a Seamless UX — It’s Possible!

As we fast approach year three of the global pandemic, consumers continue to transact online, rapidly expanding the digital economy. While in-person transactions rebounded somewhat last year, U.S. ecommerce sales increased 9.4% year-over year in November 2021, according to Mastercard SpendingPulse™. This measures overall retail sales across all payment types, including cash and check. These findings reflect how consumers appreciate the convenience of online transactions, with more consumers turning to hybrid experiences; a combination of online and offline interactions, such as buy online, pickup in store (BOPIS). As trends change, it is important to stop fraudsters and bad actors in the process.

 

Unfortunately, fraudsters are taking advantage of this transformed consumer behavior. As even more consumers shift their interactions with retailers online, fraudsters are deploying new tactics designed to evade standard fraud detection. According to NuData, sophisticated automated attacks made up the majority of attack volume in retail and ecommerce in the first half of 2021.

 

As fraud increases, companies often respond by implementing more stringent fraud protection methods. However, this can degrade the customer experience to the point where they become frustrated. Customer loyalty is crucial for any business, which means customer experience must be prioritized. And herein lies the challenge; the constant tradeoff between preventing fraud and reducing friction for good customers, a balancing act that all companies must master. Adding additional data into decision workflows reduces the magnitude of this tradeoff because companies are able to label good and bad customers more accurately.

 

Ekata has a variety of lightweight APIs for early use in the customer journey to identify and flag riskier customers. The most applicable is our Account Opening Solution for ecommerce companies and marketplaces. This solution takes phone, email and the IP address as inputs to provide back metadata; online behavior of the identity elements and machine learning scores. This data can then be used to categorize customers into different buckets of risk, so that step-up friction or heavier weight checks are implemented where relevant.

 

By flagging and grouping riskier users early in the customer journey, companies can confidently send low-risk users through a sign-up workflow that reduces friction. This provides a much more seamless overall transaction experience. In turn, increasing customer loyalty and retention, increasing lifetime value.

 

To learn how Ekata can help your company stop fraudsters while maintaining a frictionless experience for good customers, contact Ekata.[/vc_column_text][/vc_column][/vc_row]

Author

Elisa Ahern

Senior Field Data Scientist, Seattle

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