Affirm Bets on Where Borrowers Are Headed

Picture someone who had a bad year. A few bills went unpaid, and their credit score took the hit. Then things turned around. Every payment since has been on time. The score hasn’t caught up. Now picture someone whose trouble is just starting. A missed payment here, another there, spread across a few accounts. On paper, those two people can look the same. Same number of missed payments. Same answer at checkout, and it’s usually no. Last week, Affirm announced that a new underwriting model built to tell those two apart is live at U.S. checkouts. In a conversation with PYMNTS CEO Karen Webster, Affirm President Libor Michalek went past the announcement and into the parts that don’t fit in one: what the old models were missing, who stands to gain the most and what it sets up for Affirm next. What they were missing, he said, was time. “The time dimension of multiple purchases, multiple credit events in a customer’s life wasn’t being represented with particularly high fidelity,” Michalek told Webster. Put simply, the older models were good at counting what happened. They had a harder time with when it happened and what came after. The new one draws on Affirm’s 14 years of lending history and uses transformer technology, the same kind behind large language models, to follow the story in order. Five of the 10 Deserved a Yes Michalek gave Webster an easy way to think about it. Take 10 applicants an older model would turn down. It declines all of them because it can’t tell which ones will get in over their heads. A better model might spot five who can repay. The other five still get a no. The bar didn’t move. Affirm can now see who clears it. For those five people, that’s credit they would have been denied, which is a familiar story for a lot of subprime and near-prime consumers. For Affirm, it’s five customers it was sending away. Thin Files Have the Most to Gain Webster asked the natural follow-up. What happens when there’s barely any story to read? Michalek said even a short history has an order to it. Credit bureau records, Affirm’s own data and cash flow information that users choose to share all add to the picture. “Where before two very different users could look the same because of that sparseness of data, we’re really able to tease them apart,” he said. This is where Affirm saw its biggest jump. Among consumers with something on their credit report but not enough for a FICO score, the new model improved Affirm’s ability to rank who’s likely to miss a first payment by about 2.1 times as much as the next version of its older model did. One caution on that number. It measures how well the model sorts risk, so it shouldn’t be read as twice as many approvals. Michalek also said Affirm will keep reporting repayment behavior to the credit bureaus. Affirm can make some decisions without a FICO score, but the rest of a customer’s financial life still leans on one. It does raise a question worth watching. As more lenders learn to read the behavior behind the score, how much will the score itself matter? Michalek told Webster that Affirm had transformer models in testing more than a year ago. The predictions were good...