As the BNPL industry grows, BNPL payment plans are forming a more substantial part of the overall credit mix of consumers. Given the wide adoption of these solutions, there are clear trends showing increased scrutiny of BNPL exposure alongside credit cards by traditional lenders in making lending decisions.

Much of the increased attention on this form of payment is focused around the question of how using BNPL will impact consumers’ credit scores.

At the moment, for the most part, BNPL transaction history is not incorporated into consumers’ credit scores from FICO and VantageScore (the most commonly used scoring methodologies in the US). Why is this? It is not, in fact, a result of principled objections from the BNPL providers. The reluctance is driven by the knowledge that BNPL loans are something of a shock to the scoring system, which has been built and iterated on the basis of traditional loans (such as mortgages and credit cards).

If BNPL information were ingested into the credit reporting ecosystem, it would likely have detrimental impacts on the consumers’ credit score. Even where a consumer is using BNPL in the most responsible way possible, the incorporation of their BNPL data into their credit scores will likely see their scores take a dive.

The key difficulties of incorporating BNPL transaction data into scoring models

At first blush, the high-level calculation behind the FICO and VantageScore scoring models doesn’t seem too far out of step with BNPL products. In both scoring methodologies, the most heavily weighted factor is payment history, which makes up 35% of the FICO score and 41% of the VantageScore 4.0. It should follow that timely repayments of BNPL instalments would contribute positively to the overall score; unfortunately, it is not so straightforward, as other factors complicate the scoring.

• Utilisation rates as a scoring factor: The current models favour low utilisation as a way of indicating responsible spending behaviour and ability to repay (for example, a consumer using less than 30% of their credit limit on their credit card). However, this is built on an assumption that it is preferable for a consumer to hold a credit card with a large credit limit than for a consumer to build a lending profile through case-by-case acquisition with time-based lending. If you remove the assumption that a large amount of unused credit is indicative of a good consumer, you no longer have a solid basis for weighting a model in favour of low utilisation.

• The short term length of BNPL accounts: BNPL accounts have short terms, invariably much shorter than an instalment loan like a car or mortgage. This impacts the “Length of Credit History” and “Depth of Credit,” metrics in the scoring models, which contribute approximately 15-20% to the scoring calculations. These models take into consideration the age of the oldest account, newest account, and average age of all accounts, and reward longer terms and history. It is easy to see how multiple recent, short term accounts will skew this average, to the detriment of BNPL users’ scores.

• The frequency and recency of BNPL accounts: BNPL loans usually involve opening a new account for each purchase, as they are non-revolving loans. While this is one of the main selling points of the BNPL model from a consumer protection perspective (it allows consumers to borrow only what they need) it can negatively impact the “New Credit” component of both FICO and VantageScore models, which makes up 10% and 11% of the models respectively. While a customer opening four new credit card accounts in a month would be worthy of a red flag, the same cannot be said for four BNPL purchases. Unfortunately, the scoring algorithm doesn’t make this distinction and punishes frequency of use as if it were a traditional credit product, resulting in lower scores.

• Potential lack of credit mix: Where a consumer has not been able to access traditional forms of credit, they might be using BNPL credit products instead of other types of credit. As the scoring rewards diversity in credit products, consumers who use BNPL responsibly but do not use other products will not likely see any improvement in their scores if these transaction data are included.

It is well known that credit scoring models have disproportionate impacts on certain socioeconomic groups. Thoughtless ingestion of BNPL data into these models could lead to further disadvantage for these groups. This is clearly a bad result from an access to credit perspective, as well as for the reputations of BNPL providers.

There is currently no consensus on how this new form of lending should be considered in the market when looked alongside the more traditional forms of debt. The scoring industry needs to further progress towards fairly reflecting the unique characteristics of the BNPL model before scores are ingested through the existing credit reporting ecosystem.

At Qlarifi, we are excited to be leading the conversation.