Fraud detection platform Ravelin secures £3m just nine months after launch
Playfair Capital-backed start-up has already signed Deliveroo, YPlan and US company Via Taxis to its service...
Ravelin, an early-stage UK start-up specialising in real-time fraud detection for online businesses, has closed a £3m funding round with investment from Playfair Capital, Amadeus Capital, and Passion Capital.
Wonga founder Errol Damelin and Indeed.com founder Paul Foster were also involved in the funding round.
Launched officially in January 2016 following several paid trials, Ravelin analyses customer behaviour and transactions using proprietary data science and machine learning technologies, and works with merchants to provide “extremely accurate” fraud detection.
According to its website, its service reduces chargebacks to 0.1% while increasing conversion on site or in app.
In May, the company launched graph network technology which it says has worked to shut down many thousands of fake accounts and prevented millions of pounds worth of potential fraud.
The start-up plans to use the funding – which adds to an earlier £1.3m round – to attract more customers from across the world; having already attracted the likes of Deliveroo, YPlan, Karhoo and US-based Via Taxis to its service.
Ravelin CEO, Martin Sweeney, commented:“We have focused squarely on growing the company by doing a great job for our clients. This investment enables us to continue to grow our team and invest in the infrastructure and technology we need to give the same great experience to even more clients based around the world.”
Building a website for your business idea is easier than you might think. Our online tool ranks the top website builders that offer free trials.
Nathan Benaich, partner at Playfair Capital, continued:
“Ravelin has achieved impressive market traction by building fraud detection technology from the ground up to address the ongoing issue of payment fraud. This problem is growing faster than online commerce and Ravelin’s machine learning approach brings the requisite speed, scale and performance to solve it efficiently.”