In the context of the credit reporting industry, Buy Now, Pay Later (BNPL) is brand new. As it continues to establish its foothold as a favoured payment method with 360 million estimated BNPL users worldwide, a question arises: is anyone keeping track of all of these deferred payments, the “phantom debt” being incurred by consumers paying in instalments?

Yes and no. Information about BNPL consumer spending is dispersed haphazardly throughout the BNPL ecosystem: for example, it is held by BNPL platforms, financial regulators, merchants, and of course the consumer. Each of these datasets is incomplete, and there is no comprehensive - or even close to comprehensive - repository of this important data.

As a result, BNPL lenders currently have no visibility into a consumer’s borrowing activity on other BNPL platforms. This means that even if they perform a traditional credit check, BNPL lenders cannot determine if a consumer holds concurrent BNPL loans, or has defaulted on a payment, with another BNPL provider. And although BNPL lenders do have effective methods to detect when a consumer is in financial distress, the Consumer Financial Protection Bureau has voiced concerns that this data blind spot could erode the guardrails employed by individual BNPL lenders, who cannot solve this problem on their own.

BNPL lenders are also unable to refine their underwriting models using the dataset which should be most relevant to them: their customers’ BNPL purchase history.

To oversimplify: there is a clear need for BNPL transaction data to be aggregated. The creation of a real-time, secure and reliable data repository for BNPL transaction data forms Qlarifi’s core mission. No more spooky “phantom” debt.

The success of a BNPL data aggregation platform depends on the following:

1. It must be real time. The average BNPL consumer uses the product 25-50 times per year, and given the short term nature of these loans the only way for a BNPL provider to use the aggregation data to supplement its underwriting tools is via real time reporting. A reporting method which updates only monthly - or even weekly - has limited usefulness as it cannot really be trusted.

2. The data approach must be consistent and standardized. Standardization in ingestion methodologies gives compliance and risk teams a clear understanding of how the data will be incorporated into assessment models and scoring systems. Inconsistent reporting is a roadblock to successful aggregation. (Today, certain BNPLs only report positive or negative information about their users, and most credit bureaus hold the information entirely separate from their general reporting data.)

3. Security and reliability are key. The data infrastructure must be capable of withstanding high volumes of streamed (live) data, so that BNPL providers can rely on the service. It should also follow a data-minimisation principle, whereby PII is protected and only strictly necessary information is shared in any system interaction.

The aggregation of BNPL information will be transformative for the BNPL industry, and has huge potential to help consumers who have been locked out of the credit market. The industry can begin to truly understand BNPL as a product, and assess its value against the mix of other lending products that are available. This data, aggregated responsibly, also presents distinct opportunities for policymakers and other industry participants who seek insights into consumer behaviour. And of course, there will be no more fears of phantom debt.