Your Social Networking Credit Score
“Big data” can help determine who really deserves a loan. But there are dangers.
Photo by Johannes Simon/Getty Images
The buzzword tsunami that is “big data"—a handy way of describing our vastly improved ability to collect and analyze humongous data sets—has dwarfed “frictionless sharing” and “cloud computing” combined. As befits Silicon Valley, “big data” is mostly big hype, but there is one possibility with genuine potential: that it might one day bring loans—and credit histories—to millions of people who currently lack access to them. But what price, in terms of privacy and free will (not to mention the exorbitant interest rates), will these new borrowers have to pay?
In the not so distant past, the lack of good and reliable data about applicants with no credit history left banks little choice but to lump them together as high-risk bets. As a result, they either were offered loans at prohibitively high rates or had their applications rejected.
Thanks to the proliferation of social media and smart devices, Silicon Valley is awash with data. While much of it has no obvious connection to finance, some of it can still be used to make accurate predictions about the user's lifestyle and sociability. As a result, a new generation of companies is beginning to deploy algorithms that sieve through these data to separate trustworthy borrowers from those likely to default and to price their loans accordingly.
Some—like the Hong Kong-based Lenddo, which currently operates in the Philippines and Colombia—do so by scrutinizing the applicants' connections on Facebook and Twitter. The key to getting a successful loan from Lenddo is having a handful of highly trusted individuals in your social networks. If they vouch for you and you get the loan, your select friends will also be notified of your successes in repaying the loan. (In the past, Lenddo even threatened to notify them—exerting maximum peer pressure—if you had problems repaying the loan.)
Similarly, the U.S.-based LendUp, which hands out short-term loans with high interest rates while allowing its most trusted established clients to move to more attractive longer-term packages, looks at social media activity to ensure that factual data provided on the online application matches what can be inferred from Facebook and Twitter. Not surprisingly, most such startups—including Lenddo and LendUp—are financed by the same venture capitals who have backed much of the social media boom. (Accel Partners, one of the first investors in Facebook has funded Lenddo; LendUp has received support from Google Ventures and Andreessen Horowitz.)
Social media is just the tip of the iceberg. Wonga, an extremely ambitious online payday-lending company based in London, even considers the time of the day and the way a candidate clicks around the site in determining whether to grant a loan. It rejects two-thirds of all first-time applicants. Kreditech, a Germany company that seeks to provide "scoring as a service," looks at 8,000 indicators, such as "location data (GPS, micro-geographical), social graph (likes, friends, locations, posts), behavioral analytics (movement and duration on the webpage), people’s e-commerce shopping behavior and device data (apps installed, operating systems)."
Those without smartphones or Twitter accounts need not despair. Even simple cellphones are sources of data with great predictive value. Thus, Safaricom, Kenya’s largest mobile operator, studies how often its customers top up their airtime, how regularly they use the voice service, and how frequently they use the mobile money function. Once their trustworthiness has been established, Safaricom would gladly lend them money. But mobile operators moonlighting as banks aren’t the only ones leveraging these data: A Cambridge-based startup called Cignifi is using the length of calls, the time of the day, and the location of the call to guess the lifestyle—and hence the reliability—of loan applicants in the developing world.
All these efforts are premised on the sensible idea that current models of assessing creditworthiness focus on too few indicators, shutting off many potential borrowers who pay their bills on time but don't have good (or any) credit histories. “Big data” can separate the lazy slackers from those who truly deserve better loan terms. The goal, then, is to get as many data as possible, perhaps even nudging potential applicants to pre-emptively disclose as much information about themselves as possible. In yet another puzzling paradox of the modern age, the rich people are spending money on expensive services that protect their privacy and improve their standing in Google's search results, while the poor people have little choice but to surrender their privacy in the name of social mobility.
Evgeny Morozov a contributing editor at the New Republic and the author of the forthcoming To Save Everything, Click Here: The Folly of Technological Solutionism.