New Account and Referral Fraud | Fetch Rewards Case Study

Fetch achieves 500% growth while reducing manual reviews by 90%, enabled by Kount’s AI-driven Fraud Prevention Platform.

No Good Order Left Behind: Leading the Digital Rewards Revolution

Fetch Rewards was born in 2013 with a plan to revolutionize rewards and discounts in local and digital shopping. Today, with offices across the country, the Fetch app delivers the fun of coupon discounts without the frustration, and it works by rewarding daily purchases logged via a simple scan of receipts. Customers flocked to the app, eager to cash in on hassle-free rewards. But, along with rapid adoption, Fetch also experienced a spike in new account and referral fraud, as users opened numerous fake accounts in order to collect valuable gift card offers and large referral bonuses.

Because Fetch places a premium on accepting every good order, the manual review rate skyrocketed. The fraud team logged hours of overtime, at one point reviewing 20% of all orders. And with 600,000 active monthly users at the time, the effort began to affect team morale and company revenue, stalling growth campaigns that would have overwhelmed the review team’s capacity.

To reduce the manual review burden and accelerate growth, Fetch began searching for a fraud protection platform that could scale with rapid expansion, accurately automate approvals, and reduce false positives immediately.

Kount really won us over with the data transparency. The extra detail is really helpful for a team like ours. — Bethany Morgan, Fetch Rewards Fraud Manager


Fetch Selects Kount’s Advanced AI and All-in-One Platform

In its wide-ranging search for a solution, Fetch had three clear requirements in mind: accurate automation to maximize approvals, an all-in-one tool to increase efficiency, and data transparency. Bethany Morgan, the Fraud Manager at Fetch Rewards, summarized their priorities:

“We wanted to take advantage of machine learning to better automate the good events, the good sign ups, the good redemptions. But we also had to have a means of controlling it to really protect ourselves from false positives. And we wanted a tool that provided everything in one—I didn’t want to piece together rules and reporting by creating more internal tools.”

For Fetch, only Kount checked all the boxes. Kount’s AI-driven Fraud Protection Platform is built on the largest network of trust and risk data, the Identity Trust Global Network, which links 32 billion annual signals in its network to stop fraud and deliver highly accurate decisioning. The Platform also provides open access to unique data and reporting. Said Morgan, “Kount really won us over with the data transparency. The extra detail is really helpful for a team like ours.”

With that data, Fetch was able to optimize its automation through Kount’s Customer Experience Engine: enabling automated decisions and reducing manual reviews on one side, while on the other allowing the flexibility and control to fine tune customer experiences through customizable policies and rules, ultimately helping Fetch to reduce its fraud rate by 70%.

Email Age & Transparent Data Drive Down Manual Reviews

With Kount’s AI screening out high-risk accounts and transactions, the fraud team quickly used their new bandwidth to focus on accepting more good orders. Said Morgan, “the biggest thing with our app, if you ask anyone at the company, is that we want it to be easy and fast. If a user’s redemption is held, that immediately slows them down.”

Fetch needed to access accurate, high-volume automation to free up time to review questionable transactions rapidly. Kount’s platform provided an array of tools to increase accuracy, led by Email Age. According to Morgan, “we had new account creation and referral fraud happening all over the place, but as soon as we got email age in there, it was beautiful: all the ages are zero,” a major indicator of fraud. Email Age also helped Fetch reduce friction for good users. Said Morgan, “once we were able to add Email Age as a condition to our policies, it immediately dropped our review rate. We didn’t have to worry that a user was part of a random fraud cluster – we’ve got more trust with this user.”

Fetch used email age, transaction velocity policies, and Omniscore – Kount’s AI-generated transaction safety rating – to enhance its automated protection. And as they gained more data on interactions throughout the app, they customized Kount’s User Defined Fields to achieve an unprecedented order approval rate. With Kount, Fetch dropped its manual review rate to under 2%.

Real-Time Risk Data Improves Marketing Efficiency

Once Fetch had manual reviews firmly under control, they looked for more ways to optimize growth. One surprising opportunity appeared in collaboration with marketing. With real-time access to their trust and risk data within Kount, the fraud team was able to improve promotional campaigns. In one example, Fetch saw a sudden increase in new account fraud. Using Kount’s data, they pinpointed TikTok as the source, where users had exploited a loophole to acquire free Amazon gift cards. By taking this data to marketing, Fetch was able to improve campaign targeting and optimize marketing spend.

Fetch Accelerates Growth With Scalable Protection

The outcome of Fetch’s work to decrease manual reviews and minimize false positives has been swift and remarkable: since joining Kount, 600,000 active monthly users has grown into 3 million. And while the user base has increased by 500%, the fraud team has grown only modestly – and the overwhelming pressure to review transactions is entirely gone. Kount’s unmatched accuracy, scalability, and data transparency is helping Fetch speed up the customer experience and its own success.


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