GoEasy: Subprime Lending Data Advantage
Former VP of Engineering at Upstart
inpractise.com/articles/goeasy-subprime-lending-data-advantage
Former VP of Engineering at Upstart
The expert is a former VP of Engineering at Upstart Holdings, Inc. where he led the team responsible for developing the algorithm that predicts loan default risks and converts this information into an APR. His expertise lies in the subprime consumer lending process, from loan application submission to verification and APR determination.
Interview Transcript
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Is this process done manually at first or is everything automated?
The goal is to automate everything. The reason is that any manual process significantly prolongs the time taken. We found that when the verification system was automated, the conversion rate was approximately 10 times higher. It was remarkable how much more frequently people would accept the loan compared to when there was a manual step involved, such as checking back in a day or a few hours. This would cause a significant drop-off. I can't share specific numbers, but I can tell you that when I was there, about half of the applications were automatically approved, and about 90% of the loans originated were from those that were automatically approved. So, you can infer how much better conversion rates you get just by having everything automatically approved.
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It's like comparing the borrower to other similar cohorts.
Indeed, it is largely based on historical payment information to Upstart. During some of our Databricks projects, I suggested discarding some old data due to the effort required to correct it. Despite it constituting only 5% of our data, the suggestion was not well-received. All past data is deemed crucial.
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