big data | July 30, 2013

Big Data Meets Credit: Douglas Merrill's ZestFinance Profiled In GigaOM

For people with less-than-perfect credit, there are few options available when a few bucks shy of making rent, paying a bill, or dealing with an unexpected expense. Douglas Merrill, a big data speaker and founder of ZestFinance, is looking to change that. There's a huge gap when it comes to accessing credit. There are credit cards and loans from banks (which are available to most people with solid credit scores) on one end, and payday loans and pawnshops at the other. The problem with this system is that people with less than ideal or nonexistent credit scores are unable to access affordable short-term credit options in a crunch. That's because most payday loan agencies or pawn brokerages only rely on a limited set of variables to determine their payback percentages—causing their rates to be skyhigh.

A new GigaOM profile on ZestFinance explains how Merrill (the former Chief Information Officer at Google) and his team designed the company to better analyze credit risk. They combine big data metrics and human analysis to better assess whether someone is a good candidate for credit, offering them a rate that is more in line with their credit situation.

Here's GigaOM's breakdown of their business model:

  • "The magic behind ZestFinance’s methods lies in the approximately 70,000 variables its models considers, which are then analyzed using a number of machine learning algorithms. Once the algorithms have done their job, humans step in to apply some logic, judgment and  context to the results. All told, ZestFinance claims a roughly 60 percent improvement over traditional underwriting models, and repayment rates 90 percent higher than traditional methods."

The model is capturing attention in the venture capital world, and the Los Angeles-based startup recently received a large investment to propel their business forward. In Douglas Merrill's talks, he draws on his work both at ZestFinance and his time at Google. He shows audiences how to think more innovatively about big data, and, how to apply the opportunities it presents to your business.

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