The successful IPO of Lending Club is testament to the growth of P2P online lending, a market that is said to be doubling every year as borrowers seek an alternative to the banks and other traditional lenders, and in turn investors look for a better return on their money. Not only does this mean that VC interest in the sector shows now signs of slowing, but we’re beginning to see startups crop up who offer add-on services as part of the wider P2P lending ecosystem.
One such company is LendingRobot, an automated investment service for online lending on the two leading P2P platforms, Lending Club and Prosper. The Seattle, U.S.-based startup has just raised approximately $3 million in a Series A round led by European VC Runa Capital, money it’ll use to further develop its product and to ‘accelerate’ growth.
The problem that LendingRobot has set out to solve is an interesting one, and shines a little light on the way P2P lending has already matured. Using what it calls “high-speed automation software and machine-learning algorithms”, the service helps investors automate the lending process, based on pre-selected criteria, so that LendingRobot is able to select and invest in loans less than one second after they become available.
Why is this significant? As so-called professional lenders, including hedge funds, have stepped into the P2P lending space, competition amongst investors means that the most attractive loans (or “notes”) are snapped up incredibly quickly, making it time-consuming to manage your P2P lending portfolio or, worse still, meaning that you only have access to any leftover scraps.
It’s a problem that two of LendingRobot’s founders, Emmanuel Marot and Gilad Golan, realised in 2012 was better solved by software rather than humans. In fact, they initially developed the basic idea to automate their own personal investments, taking inspiration from the ‘sniper’ software that enables automated last-minute bids on eBay.
In addition to the speed advantage that automation affords, LendingRobot supports more than 40 different filtering criteria for Lending Club and Prosper Marketplace, and harnesses machine-learning/artificial intelligence algorithms to help select investments, such as taking into account how quickly and how much other investors have invested into a particular note. That way it’s possible to ride the coattails of those professional investors who have potentially put you at a disadvantage in the first place.
Along with solving a genuine problem on the part of investors on Lending Club and Prosper, the market opportunity beyond the U.S. appears to be decent too. The UK, for example, has a pretty mature but still fast growing P2P lending market also.