Online fraud detection platform Ravelin, created for the demanding on-demand digital economy, has raised a £1.3m round from a number of high-profile VCs and angels.
The Barclays Techstars Fintech alumnus attracted cash from previous VC investor Passion Capital, which led a March seed round for an undisclosed sum, along with Amadeus Capital Partners and Playfair Capital, plus Wonga’s Errol Damelin and Indeed.com’s Paul Forster.
Other angels who joined the round are Gigi Levy, former CEO of 888 Holdings, now an investor in marketing software firm Kenshoo, and Jeremy Millar, a partner at M&A consultancy Magister Advisors.
“We started Ravelin with the insight that the on-demand economy was poorly served when it comes combatting fraud,” said Martin Sweeney, CEO at Ravelin and previously a founding engineer at the Hailo taxi app firm.
“We’re delighted with the market reception to our private beta, and this funding round from such prestigious investors is further validation of our original insight.”
Asked how the company will win against established firms, Gerry Carr, new business at Ravelin, told Tech City News: “We’ll achieve this by doing things differently. We want to tackle fraud for our customers using many of the techniques and skills that for revenue protection are only available to the largest online companies like eBay and Amazon but make it available at SAAS pricing.
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“In summary, Ravelin will be easier to deploy and produce more accurate results more quickly than the established firms.”
The company says it has already invested significantly in its team, several of whom are former Hailoers, in order to develop its use of data science and machine learning to offer fast fraud decisions for its customers.
With the new funding, the team and customer-base is set to grow. The first disclosed client is Hailo but Ravelin says its other clients already include on-demand retail, food delivery, gambling, transport and mobile wallet companies.
It will also enable the company to invest in further developing the technology behind the product. This combines using machine learning to match existing customer data and detect whether behaviour looks fraudulent, using social graphing and third-party data to interrogate fraudulent behaviour and verify users, along with implementing simple rules to stop specific or recurring fraud.