Big Brother is watching you. That’s the reality for an increasing number of online loan recipients, thanks to the advent of what I call “network lending”. What does this mean? In short: to offer seamlessly accessible credit by leveraging the vast information flows that are contained within an active, transaction-based network of customers. Zopa, MarketInvoice, Finexkap, Capify, Iwoca, Ezbob, Lending Club and Basware are engaged in such a style of lending. The enablers – the networks – are the likes of Uber, Sage, Xero, Alibaba and Google.
Let’s take the Uber example. Zopa joined forces with the revolutionary taxi app in May of this year, in order to make a cheaper form of purchase finance available to Uber drivers. The loans are at present exclusively funded by P2PGI – the marketplace lending sector’s pre-eminent investment trust. Once Zopa is satisfied that the risk return profile of these deals is palatable, they may be made available to the platform’s community of private investors.
But how do these Uber loans hold up as an investment proposition? Just how well does Zopa really know these customers? Let’s review. If you're one of these borrowers...
Fair to say, then, that Zopa knows quite a lot indeed. It’s much the same story with many of the existing examples of network lending. MarketInvoice is able to draw upon the real-time accounts of prospective borrowers via software systems Sage and Xero, allowing the platform’s credit decisioning process to be executed in minutes, perhaps even seconds. Similar, indeed, to the functionality of Finexkap’s integration with BDO France’s accounting software platform.
Lending Club, Iwoca, Ezbob and Capify are all plugged into the Alibaba e-commerce network. The purpose of these arrangements is to supply finance to small businesses in the US, UK and Australia that wish to purchase from Chinese suppliers via the Alibaba network – the largest e-commerce marketplace in the world. Bundled up within Alibaba.com is an enormity of user data, pertaining to everything from activity, to trustworthiness.
If we look further afield – to China, for instance – network data is just about the only thing driving credit decisions. The likes of Tencent Tenpay – China’s leading online payments platform – are hugely important to the functionality of the Chinese peer-to-peer lending space. A vast number of financially active people in China are not covered by credit bureaus. But Tencent and its c. 800 million users have stepped into the breach. Those users leave an enormous digital footprint on the platform, and many Chinese platforms are able to harness that information in order to fuel lending decisions. China Rapid Finance, for instance, launched a mobile-based pre-approved borrower acquisition campaign in April of this year. Within a month, a mind-boggling 300,000 loans had been approved by the platform.
I mentioned “seamless access” in my compact definition of the "network lending" concept. E-invoicing network Basware launched “Virtaus” in early November, with the intention of providing businesses with access to short-term working capital. Basware Advance is the first product to have been spun out of Virtaus. SMEs that are plugged into the Basware Commerce Network may use Basware Advance in order to receive early payment on outstanding invoices. These advances are funded by Arrowgrass Capital. 100 million invoices with a combined worth of over $500 billion have been processed by the network to date. At the point of issuing an invoice through the network, Basware customers now have the option of accessing up-front capital in just a few clicks. This is what I mean by seamless access. The data is there, in spades. And the option of accessing credit is made available at the touch of a button, at a time in the business process when it is most sorely needed.
So what’s the potential downside of network lending? For investors, tough to say. There is a potential risk, I suppose, that total clarity of data breeds complacency in credit – and that network lenders thus de-emphasise human interaction, to the detriment of their overall risk assessment process. Bear in mind, however, this quote from Hendrik Brackmann, Credit Analytics Lead at MarketInvoice – which addresses this fear (bear in mind also that the quote may not make sense if you haven't read the article in full):
“At MarketInvoice, we deeply believe in a hybrid approach to credit underwriting. We believe that credit scores provide the baseline for every decision made. But we also put considerable effort in making sure that the machine communicates the reasons for a given decision. This allows us to learn from and teach the machine – to become one entity, just like Robocop.”
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