Mad Max Levchin has started three technology companies and runs a venture-capital firm
David Paul Morris–Getty Images
By Dan Kedmey
November 13, 2014

Judging by the numbers, Max Levchin’s life has had a quantum-leap quality most people would envy. His first jump came in 2002, shortly after he sold off his ownership stake in PayPal to eBay for an estimated $34 million. He was 27 years old, flush with cash and adrift in an ocean of downtime.

There was only one way out: launch another company. So in 2004 he started a personal-media-sharing service, Slide, which he eventually sold to Google in 2010 for $182 million.

Today, at 39, Levchin is at work on a third company, Affirm, and it’s not looking to make millionaires its main customers. Like PayPal before it, Affirm has grand ambitions to remake the way everyday consumers think of and use money. That’s a popular endeavor in Silicon Valley these days, as a raft of startups as well as giants such as Apple and Google tackle everything from payments and digital currency to venture investment and lending. Twitter and Facebook are also testing ways to let users send micropayments through their social networks. (On Sept. 30, eBay said it would spin off PayPal in 2015 to help it better compete with newer rivals.)

Affirm, which over the past year has raised $45 million from venture-capital firms, is fashioning itself as a lender. It offers consumers the option to split payments over time, which a growing number of online retailers have added to their checkout pages.

Affirm’s 32 employees have set up shop in San Francisco on a quiet street lined by venerable brick buildings, some of which withstood the infamous earthquake and fire of 1906. Here, Levchin appears to be thriving. Since Affirm launched six months ago, three to five businesses a week have adopted the new payment platform. At that rate, nearly 100 businesses will have a “Pay with Affirm” option this holiday shopping season. Levchin still favors the startup-chic look: a puffy sleeveless winter vest, unzipped to reveal a weathered T-shirt that practically announces, “I’ve got bigger things to worry about than shopping.”

But Levchin has been obsessing over shopping. He has been visiting retailers across the country, asking about the state of consumer lending. Millennials have a tentative relationship with it, often because they either don’t want or don’t qualify for a credit card. More than 6 in 10 of them say they have never signed up for a credit card–a group that has doubled in size since the financial collapse of 2008. Enter Affirm, which allows users to get instant loan approval by tapping their personal phone numbers on the site’s welcome page. Affirm makes lending decisions based on the data associated with that number on the Internet. “It anchors you to a whole host of information that is entirely public or pretty close to public,” says Levchin.

Affirm can, for instance, scan for background information across social media or dip into proprietary marketing databases and combine that with credit histories. In total, the Affirm team has identified more than 70,000 personal qualities that it thinks could predict a user’s likelihood of paying back a loan. Affirm claims to capture a borrower’s profile in full.

The company is so confident in its claims that it puts its own money on the line, extending loans to people whom banks might normally consider a risk. Active-duty soldiers, for example, sometimes return home with scant credit histories. A host of regulations require lenders to extend credit to the soldiers, even if the decision goes against their better judgment. As a result, Levchin says, some lenders have eyed returning soldiers with suspicion.

“I couldn’t care less about the narrative of why that might be true,” he says, “except that I know it’s actually not. From all the loans we’ve issued, I think we’ve had literally 100% repayment rate from active-duty servicemen.” Of course, military service is just one of at least 70,000 variables that can tip Affirm in the user’s favor. The formula is complex by design so that no one user can game the system by, say, posting “brain surgeon” as a new job on LinkedIn and then requesting a fat line of credit.

Whether Affirm will truly upend the rules of lending will depend on its ability to collect interest on loans without resorting to hidden fees. The service alerts users to approaching payment deadlines and clearly states fee rates before they arrive.

Affirm also has to lend at the right rates to the right people. Fortunately for the company, it has plenty of venture capital to test-drive its unified theory of lending. The company says it plans to lend $100 million to consumers over the next 12 months.

Longtime financial analysts have doubts about how quickly the norms of lending can be changed. Not that traditional banks haven’t been trying. “Social media in particular has been a topic that financial institutions and credit-score providers have kicked around for a number of years,” says Michael Misasi, a senior analyst with Mercator Advisory Group. “I think everyone is still trying to figure out how accurate that data really is.” The online data that purportedly offers a more intimate view of a borrower’s behavior could also be a minefield of inaccuracies and distortions. “What they put out there for others to see might not really be an accurate assessment of who they are,” Misasi says.

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Then there is the question of how long Affirm can fly under the regulatory radar. Traditional financial institutions must navigate a thicket of rules regarding whom they lend to and how the terms of the loan are disclosed. “Lenders have to make sure that the algorithms they’re using aren’t unfairly discriminating against any particular segment of customers,” Misasi says. “It’s still a pretty unclear space, regulatory-wise.”

Levchin himself may be Affirm’s greatest asset. Born and raised in Soviet-era Kiev, he comes from a long line of physicists and had a chance encounter with coding. His mother was a radiologist at a research institute in Ukraine, where she was tasked with extracting reliable measurements from aging, prewar Geiger counters, which spewed out a tremendous amount of erroneous data. Her manager dropped a computer on her desk and asked her to program her way to a more reliable reading. Stumped, she turned to her 11-year-old son and asked, “Do you know anything about this stuff?”

The question kick-started Levchin’s lifelong love of programming and, he says, made him aware of what data a machine can capture and what essential points might elude its sensors. “The fact that we can look at data, pull it and underwrite a loan for you in real time is very valuable, because we can literally decide, ‘Hey, in the last 48 hours you got a new job–that changes things a little bit,'” he says. “‘Now you’re able to afford more.'”

This appears in the November 24, 2014 issue of TIME.

Contact us at editors@time.com.

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