Blackhawk Network is one of the premier distributors of prepaid card and gift card programs, with more than 700 partners across its networks in more than 28 countries. The company has grown rapidly and has engaged in partnerships and created innovative solutions to enable gift card use in the digital realm. It is trusted by retailers and consumers alike.
“The Arkose Labs team is flexible, easy to work with, and a true partner in helping us solve fraud issues. They’re always available whenever needed and willing to work with us on any use case we have.”
The Business Problem
As one of the largest players in the prepaid industry space, Blackhawk Network’s security team is focused on minimizing fraud to protect its partners and consumers alike. That requires constant innovation of its defensive strategies as well as partnerships with third parties who also focus on addressing and defending against fraud.
The company had been using another provider for authentication on its websites, but the legacy solution proved lacking in several areas. Its static, automated images could be bypassed by bots using machine vision technology. At the same time, good users faced friction.
Blackhawk Network needed a more robust solution, one that would not only stop even the most cutting-edge bots and persistent humans, but that also provided data insight across its network of sites.
The Arkose Labs Solution
After much due diligence, Blackhawk Network selected Arkose Labs to assist with its global fraud defense needs and saw immediate reduction in both automated and human driven fraud with no impact to good customer throughput.
That’s because the Arkose Labs platform seamlessly combines best in class intelligence and analytics with adaptive step-up Arkose Matchkey challenges that are designed against the latest in machine vision technology, meaning even the most advanced bots can’t solve. At the same time, human fraud rings that attack sites are identified and fed increasingly complex challenges, so that they give up their effort entirely.
Beyond that, Arkose Labs provided Blackhawk Network with actionable insights into fraud patterns, how to stop them and where best to focus resources in order to be able to change parameters quickly as threats evolved. Arkose Labs was also able to deliver custom metrics to the Blackhawk Network team as requested, and was responsive to meet new requests. The Blackhawk Network security team was also able to easily share data with its internal risk team. This allowed the company to operate in a much more agile manner to combat fraud attacks. This success led Blackhawk Network to further the engagement and deploy Arkose Labs on additional sites.
Demonstrated Results Without Harming the Customer Experience
Mere weeks after being deployed, Arkose Labs had already stopped the amount of successful automated attacks getting through by more than 98%. Human-driven fraud was also reduced by more than 60%.
Just as importantly, good users were not faced with an onerous authentication experience that required them to meticulously click on different images. The end result was a safer and more user-friendly experience across the Blackhawk Network ecosystem.
The Arkose Labs Advantage
Challenges on the Arkose Labs platform can not be solved by automated scripts, even those using advanced machine vision technology.
Utilizing machine learning, the Arkose Labs solution adapts to new threats.
Arkose Labs works with businesses as true partners in fighting fraud, delivering custom insights.
Seamless Customer Experience
Good users are never blocked, which eliminates the false positives that hinder customer experience.
Protect Against Any Attack
The platform defends your business against spam, scraping, fake reviews, denial of inventory, credential testing, payment fraud, new account origination, account takeover and other complex attacks.
“We needed a more robust solution; we weren’t getting any insights into the data with our old solution. Arkose Labs provided us with custom data and insights that we can share across teams internally.”
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