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How Sift helped Wanelo create a successful fraud solution from the ground up

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A global marketplace for Gen Z

Wanelo¬†(creatively encompassing the “Want, Need, Love” mentality of shoppers) is a shopping app built to connect people with merchants. Through the mobile-focused marketplace, consumers can connect, discover, and buy millions of fashion and lifestyle products directly from global sellers. Wanelo is where Generation Z shops, providing a unique shopping experience that is as much about community and conversation as it is about buying.


What began as a social shopping site evolved into a marketplace last year and has seen the number of sellers grow 5x since its inception. The company is headquartered in San Francisco with 90% of its user base in the U.S., but Wanelo also has remote teams globally to support its marketplace around the clock. Currently, fraud falls under the Marketplace Operations team, which executes all manual order review and order disputes.


Stopping spammers and scammers

When Wanelo first found Sift, they were looking for a solution to combat spammers. With a small and agile fraud team, they were able to move off of hard-coded rules with Sift in order to quickly disable the fraudulent users polluting the social space they were creating. But as Wanelo’s capabilities moved from pure social site to marketplace, they needed a solution to the payments fraud they were experiencing.

With the transformation to e-commerce platform and arrival of in-app transactions, Wanelo at first attempted to catch malicious users with manual review and basic rules. The payments fraud showed up in the form of disputes, wherein both friendly and scammy customers demanded ‚Äúcharge not authorized‚ÄĚ chargebacks. Nearly 70% of their chargebacks could be attributed to friendly fraud, which provided a unique challenge to address because such customers often look like trusted and valuable users‚ÄĒuntil they decide that they don’t want to pay. Wanelo’s job then is to convince the bank that the customer is committing chargeback fraud. As more fraudsters attempted bad activity and Wanelo’s chargeback rate crept up to 0.87%‚ÄĒincluding friendly fraud‚ÄĒCourtney Fahrer, Marketplace Operations Manager, turned to the system that had worked so effectively for the social side of the company.


Sifting through the good to stop the bad

Courtney decided to apply Sift’s machine learning solution to their new challenge. With the launch of the Sift Formulas feature, the Wanelo team adopted this automation tool and used it as the foundation of their fraud prevention system. As existing Sift users, Wanelo turned to their Sift Account Manager to assist with reshaping their business needs of the solution. In about one week, a pair of engineers fully integrated the additional APIs necessary to connect Sift Formulas with Wanelo’s internal order management system. After training with Sift’s Solution Engineers and overhauling their label history, Wanelo was able to immediately see useful and reliable Sift Scores.


High-fashion efficiency

The Wanelo fraud disputes team uses Sift on a daily basis. With a drastic drop in dispute rate by 77% and an estimated 100-150 manual review hours saved monthly, Wanelo’s dispute rate now hovers around 0.20%. Sift Formulas, Wanelo’s favorite feature, allows the fraud prevention team the ability to create and manage automation without needing engineering resources, which is always a tough task in small companies. This tool empowers Wanelo to immediately update their automation flow to identify and respond to new patterns of suspicious behavior.

While Wanelo started with no experience in fraud, their workflows are now seamless and their chargeback rate is exceptionally low. They can easily weed out the malicious fraudsters based on Sift Scores‚ÄĒand have done so successfully without a jump in false positives. In fact, the Wanelo team has begun to leverage Sift’s findings in their analysis of suspicious users contributing to a 52% reduction in order decline rate. In the first quarter of 2016, there were only five cases of obvious malicious fraud that were not prevented and resulted in chargebacks. Happy customers and successful merchant relationships are always in style.

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