Future of Consumer Lending Belongs to PhDs, Risk-Takers
The alternative lender's new decisioning engine runs multiple models to bring its short-term loans to a larger market. It's also planning to operate in more states, a tough task for online lenders looking to navigate varied state laws.April 26
Former Google CIO Douglas Merrill believes his firm, ZestCash, can jump-start the subprime lending market with an alternative underwriting engine that uses thousands of consumer data points and proprietary math to aid loan decisions.January 20
We all know that the FICO score is less predictive at the lower end of the credit spectrum. A thin credit file, or no file, makes it hard to grant loans on the basis of a FICO score alone in the subprime segments of the market, and those below.
But we now live in a world swimming in data. Advertisers can find out what a person buys at the supermarket, what magazines she subscribes to, what education she has, what websites she visits, even who her friends are on Facebook. They use all of this information to do a better job of targeting ads in real time. Would we get better results out of underwriting if we included all of these datasets, too? Of course we would.
In subprime, people have been doing this for years. Teams of smart guys develop a hypothesis about a new signal from the market, back-test it rigorously and build a new model to update their underwriting. But this relies on smart people and smart insights.
Today, with tens of thousands of data points available, it's hard to know what will be significant, and which hypotheses to test. Your team of smart guys might identify some predictors, but will they find them all? Probably not.
Advances in technology mean that now you don't have to develop your hypotheses in advance. You can simply make loans, wait for some to go bad, and look for what those bad loans had in common. Each time you update your model, it gets better. Rinse and repeat.
This isn't easy. In fact, it's really, really hard. It requires teams of math and computer science PhDs, with expertise in big data and machine learning (a branch of artificial intelligence).
Some companies have started to do it, and it's working. They typically focus on serving the underbanked via payday and installment lending. Four examples are Zestcash, Wonga, Progreso Financiero, and Global Analytics. These startups are getting big, fast. (Full disclosure: my venture capital firm is an investor in Zestcash, and I hold a board seat there.) They are making borrowing quick and easy, and passing the savings on to consumers in the form of lower rates. Wired magazine reported last year that Wonga made over a million loans in its first three years of lending, and it continues to grow at a similar rate. But all four of these companies are doubling revenues, or better, year on year.
Watch this space. More companies will apply big data and machine learning to different lending products (small businesses, automobiles, etc.) and in different geographies. Most will fail because of the technical complexity. But the winners will really upend the industries they compete in. They will win because they are willing to take more risk. They will make more loans, knowing that they will make more bad loans, in order to teach their algorithms how to distinguish good risks from bad ones. That is the tuition price for learning how to build and train new underwriting algorithms.
Does your bank have the courage, and the knowledge, to compete in this new world?
Jeremy Liew is a managing director at Lightspeed Venture Partners in Menlo Park, Calif.