The credit score has a strange blind spot. It knows a lot about the borrower and almost nothing about what the borrower is about to buy.

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And as latest installment of “The Future of AI Credit” playbook, a PYMNTS Intelligence collaboration with Thredd, shows, creditworthiness is not necessarily constant. A borrower can be attractive for one transaction and unattractive for another.

A customer can have sufficient cash flow to support a $2,000 purchase on a six-month repayment schedule, but not a $2,000 revolving balance priced indefinitely. A merchant whose revenues fluctuate dramatically month to month may appear volatile under a conventional model even when its cash flows closely track predictable seasonal patterns.

The emerging opportunity is not simply to use artificial intelligence to make the old underwriting process faster. It is to change the unit that is itself being underwritten.

The report argues that the terms of a traditional credit product have historically been established largely independently of what the borrowed money will ultimately finance.

A lender can know whether someone paid a card bill late two years ago, how much revolving debt they carry and how long their oldest account has been open. What the traditional credit file does not see as well is the economic context surrounding the next dollar of credit: whether a small business is financing inventory likely to turn into revenue in 30 days, whether a gig worker’s irregular income is actually healthy cash flow or whether a particular purchase deserves the same price as every other purchase made by the same borrower.

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