Kiva Co-Founder Flannery Dives Back Into Lending In Emerging Markets With New Startup Branch

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A decade ago, Matt Flannery changed the world of lending in developing countries when he co-founded Kiva, a peer-to-peer lending model that allowed consumers to support women and micro-entrepreneurs in emerging markets with loans in increments as small as $25.

Now, he’s looking more broadly at commercial lending space in Sub-Saharan Africa and how data science can be applied to make small business lending smarter and more accurate. He stepped back from an operational role at Kiva and started Branch with $1.6 million from Khosla Impact and Formation 8, the fund from Palantir co-founder Joe Lonsdale.

“Personally, I think I’m better as a startup guy,” Flannery said. “It feels just like I’m back in 2005 and I’m coding and programming all day, working my butt off.”

Flannery feels that there’s room for financial institutions that cater to a middle layer of consumers and small businesses in Sub-Saharan African markets like Kenya.

“Banks only lend to rich people, and then there are micro-finance institutions, which tend to lend to lower-income communities,” he said.

Flannery believes that neither existing banks or nonprofits are set up to take advantage of the promise of mobile banking networks for commercial lending. He believes that it will be startups that do it.

Now with rapidly maturing mobile banking networks like M-Pesa, there’s a wealth of data on a potential borrower’s history of transactions and payments that can augment or replace what conventional commercial banking staff examine. Not only that, you can leverage mobile money networks themselves to send money directly to borrowers. Then, trust networks and a borrower’s community can also validate their creditworthiness.

Flannery says Branch is like a bank in your pocket. Without relying on the overhead of physical banking locations and the hired lending and collections officers to fill them, a digital micro-finance institution could offer a suite of financial services entirely from a phone and make lending decisions in seconds based on machine-learning techniques.

They’re creating a company that is marketed toward young Kenyans in the early 20s and 30s as an aspirational brand, and they have a vision to expand to other emerging markets. They’re starting with loans that range from $20 to $1,000. So far, they’ve done a few thousand loans.

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