In the days of community banks, a person’s upstanding reputation around town gave him access to a reasonable loan. But global financial institutions can’t trust strangers, so the credit rating was born.
A startup called Lenddo hopes to return lending to that community bank era, but with a modern twist. The company gauges a person’s creditworthiness using his or her online reputation, as assessed through sites such as Facebook, Twitter, and LinkedIn, to grant loans. To secure repayment, it forgoes collateral and instead relies on peer pressure through the same social networks.
The target market is a demographic often ignored by banks today: the 1.2 billion people, largely in developing countries, who are part of the world’s emerging middle class but who still struggle to access credit because they lack a documented financial history and strong identity records. “Our theory is, we could duplicate the social dynamics of microfinance, but instead do it online,” says CEO Jeff Stewart, referring to the practice of making small cash loans to the world’s poorest people and relying on peer accountability to ensure low default rates.
For now, Lenddo is lending several hundred dollars at a time—the equivalent of one month’s salary—to applicants in the Philippines and Colombia. In May, it raised $8 million dollars from investors to add engineers and expand into new countries.
Lenddo sees a big prospective market. Mexico alone will graduate 800,000 engineers in the next four years, and many will want to access credit for the first time, Stewart says. Money is only loaned to pay for “life changing” expenses like health or education bills.
Stewart says Lenddo is ultimately not a lending institution but a technology company—and he hopes that large financial institutions will use its platform to expand worldwide access to many financial services, from credit to insurance.
The heart of Lenddo’s underwriting platform is the technology it uses to calculate a credit score after a user grants access to his or her accounts, including Facebook, Gmail, Twitter, LinkedIn, and Yahoo.
The company relies on three classes of algorithms to gauge a person’s likelihood of loan repayment. One validates truthfulness; for example, it would be statistically odd if a supposed engineering student in Bogota had few friends at school or never wrote e-mails containing certain words. Another looks for behavioral and demographic clues that predict the probability of repayment, similar to how online ads are targeted based on Web surfing patterns today.
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