Shortly after the CFPB launched a market-monitoring inquiry into five BNPL lenders, Experian and TransUnion announced that they would begin including BNPL data into their credit reporting. BNPL lenders have historically required minimal data from consumers when underwriting them (as compared, for example, to what credit card lenders ask for in an application), which has made BNPL a rare and important form of credit access for the 60 million thin- and no-file consumers in the U.S.
If credit bureaus start furnishing BNPL performance data to their lender clients, it could help the credit invisible population bolster their credit profiles for much larger purchases. Equifax internal research (done in conjunction with FICO) found that on-time payments for BNPL could increase credit scores by 13–21 points. More consumers will take out BNPL products in the coming years, as it becomes more seamless with checkout — Verifone just announced that BNPL will be a payment option on millions of its payment devices and online checkout systems across the country.
While this all sounds promising, how the bureaus choose to categorize and report BNPL loans matters. If, for instance, they treat it as a new, standalone category, it may be ignored entirely by downstream lenders, whose systems still rely heavily on FICO, which employs a fairly rigid framework to generate a score (and one that likely won’t incorporate a brand new category any time soon). With this in mind, our suggestion would be to classify BNPL in the same category as an unsecured personal loan — while it may not be interest-bearing, lenders have no recourse to repossess goods sold through BNPL.
As the credit bureaus continue to work out the specifics, meaningful change will also still be needed on the lender side. Lenders need to proactively incorporate bureau-reported BNPL performance into their risk models. Each BNPL player has a different product strategy (e.g. those who underwrite more significantly vs. those who allow anyone to pay in installments) which can translate into different loss rates over the course of a given credit cycle. Inertia can be a powerful force here, as many risk teams are hesitant to shake things up for fear of taking on higher losses.