Lending standards as we understand them today tend to reflect a verdict about our past spending history. In the future, lending standards may be more tied to the things we are doing right now.
Even if those things – like only using capital letters when filling out forms, or taking the time to read the entire terms and conditions – may not seem on the surface to be actually related to credit.
The theory is that these behaviors can telegraph clues about potential customers and that sophisticated software there to scan those action types and see the patters.
“We’re building the consumer bank of the future,” said Louis Beryl, chief executive of Earnest, one of the new lenders emerging to ride the data-backed lending trend.
This approach is not without difficulties – the tech is new and unproven. Further, consumer lending outside of its normal bank context raises questions, especially for regulators who enforce anti-discrimination laws.
The new lending start-ups are not consumer banks in the full-service sense and do not take deposits. Instead, they are focused on transforming the economics of underwriting with an eye toward making credit ready and affordable for more people.
“The potential is there to save millions of people billions of dollars,” said Rajeev V. Date, a venture investor and former banker, who also was deputy director of the Consumer Financial Protection Bureau.
However using data may tread on a legally grey areas since legally, lenders cannot discriminate against loan applicants on the basis of sex, race, religion, marital status, national origin or age. Big-data lending, though, relies on software algorithms largely working on their own and learning as they go.
A learning algorithm that is assessing credit worthiness might become inadvertently racist or sexist (etc) as it went along, even if were not programmed to do so. .
“It is important to maintain the discipline of not trying to explain too much,” said Max Levchin, chief executive of Affirm. Adding human assumptions, he noted, could introduce bias into the data analysis.
Levchin’s firm, Affirm says it is on track to lend $100 million during its first year in operation. Moreover, over 100 online merchants are using its installment loan product, and the company is getting ready to branch out into educational lending.
“The long game is to use data and software to chew up and revolutionize the financial ecosystem,” said Mr. Levchin. Levchin is no stranger to changing how payments are made. He was a co-founder of PayPal, the leading Internet payment service.
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