By Sarah Davies, Chief Data & Analytics Officer, Nova Credit
This is the third of a three-part series addressing some of the major challenges in our current credit system – and how we can fix them.
BNPL has seen some pretty impressive growth these past few years, and so far it doesn’t look like it’s slowing down – the adoption of BNPL is expected to grow at a CAGR of 32% between 2022 and 2028.
Consumers want to use credit products. The ones without the traditional creditworthy file are finding ways of doing so outside the traditional credit scoring paradigm. 40% of consumers use BNPL, and on average, each consumer uses four BNPL products. Those are typically younger and less financially secure households.
While BNPL shows promises of inclusivity, it also brings a slew of new challenges for the industry, and more specifically for lenders, who now have to figure out how to conduct credit risk analysis on these types of loans.
Why BNPL is a challenge for lenders
Typically, when you take out a credit card loan, you repay the loan on a monthly basis. That means that if you miss a payment, you go from 30 days late, to 60 days late, to 90 days late. In a nutshell, the entire credit system – monthly reporting and monthly levels of delinquency –
is built around that structure of reporting.
Along comes BNPL, which has a structure that allows the consumer to take out a loan amount and break it into four payments. Missed payments, then, aren’t divided into monthly levels of delinquency. This makes it a radically different product for the industry.
The problem is that our data reporting system doesn't know how to handle BNPL and doesn't know how to record it. Even the credit bureaus themselves have mixed opinions about how to store the data. Experian and Transunion, for example, both use secondary databases to process BNPL loans. Building on these challenges, it follows that credit scores are clearly not built to predict risk when consumers use these loans.
Bringing in cash flow and cash data as a solution
Cash data and cash flow analytics can provide insight into consumer risk in a way that the credit system right now isn't really optimized for.
Cash data looks at your bank data as of the day a given sum is pulled. And it's as complete a picture as you can get in terms of the consumers expenses, income and expense transactions.
It can identify recurring payment patterns, even when they're very short – like that pay-by-four idea of seeing a recurring payment of the same amount four times.
On top of that, cash flow can help you spot some of these loan stacking paradigms, where you've got a consumer who's taking out multiple BNPL loans. That means you get a broader view of a consumer’s expense behavior as a whole.
A big benefit then is that you can quickly start spotting triggers and signals that the consumer is overextending their budget. With a 30-day lag in delinquency spotting, that’s something our current credit system can’t do.
Bottom line
Cash data allows you to understand how a consumer is able to afford (or not afford) certain loans real-time.
Cash Atlas™ is Nova Credit’s suite of cash flow attributes designed to measure and be sensitized to affordability dynamics on the consumer's bank account. Cash Atlas specifically looks at the presence of recurring payments, including trends in volatility, income and expenses associated with the consumer’s bank account.
Here’s the key takeaway: All of the attributes associated with Cash Atlas™ will directly give you an identification of BNPL presence and activity, and then allow you as the lender to work with those consumer behaviors and determine whether you want to underwrite a loan or not.