Industry
VPN provider
Use Cases
Chargebacks
Automation and efficiency
Overview
Enabling an additional layer of security over your connection is in high demand. It is a service VPN or Virtual Private Networks offer, and the market is growing at a swift pace. According to a report by Market Research Future, it is predicted to reach $106B by 2022, at a CAGR of 13%.
Buffered is one of these providers, delivering a private, safe and fair use of the Internet for all their users. Combining high speed servers and a unique military-grade encryption protocol, their technology has been praised by CNET, PCMag US, BEST VPN, The Sydney Morning Herald, and Wired, among others.
Before SEON
With SEON
Challenges
Creating a safe environment for Internet users doesn’t protect VPN companies from cybercrime themselves. Chargeback fraud is especially frequent, and in the case of Buffered, a threat to their existence. “We knew our military-grade encryption was one of our USPs,” says Gergely Kalman, CEO at Buffered. “But fighting against fraud was eating into our margins, which was seriously going to damage us in the long term”
After trying several fraud prevention systems, they settled on SEON for their platform:
Solution
Integrating SEON’s IP address analysis, paired with machine learning to create custom rules and block high-risk locations, into their platform helped Buffered reduce chargeback rates by 91%, increase their bottom line, and remain competitive.
Moreover, the number of manual queries and fraud catch rates continued to improve after the initial 30 days as the system adjusted itself – part of the benefits of deploying a machine-learning algorithm to fine-tune accuracy.
Results
- Drastically reduced chargeback rates
- Decreased manual queries
- Increased fraud catch rates
- Delivered results after 30 days only
- Machine learning system improved results after 6 months
91%
Reduction in chargebacks
30
Days to ROI
On top of the 91% reduction rate, Buffered was especially pleased with how seamless the integration process was.
Implement our solution within 30 days to improve revenue, with the help of machine-learning algorithm to help fine-tune accuracy.