BNPL firms must re-evaluate their models in line with today’s landscape

The popularity of "buy now, pay later" (BNPL) has surged in the past few years, with an influx of pure-play providers as well as major banks such as NatWest and HSBC entering the space. The market is growing rapidly, as reports forecast BNPL spending in the UK to reach £37 billion by 2026. With that growth comes a responsibility to ensure that customers across the whole BNPL chain, both B2B and B2C, are managed in a way that best serves their interests. 

The models typically relied on by BNPL lenders are being stretched given the current economic climate, which includes the cost-of-living crisis, inflation, soaring interest rates and the ongoing impact of the war in Ukraine. BNPL firms are spreading their risk very thin across a large number of end customers and essentially holding open risk in their books. It is crucial for them to re-evaluate their models based on more up-to-date parameters to navigate these challenging times.

Managing risk across the entire BNPL ecosystem

Banks dealing with traditional credit applications have standard criteria that must be met, yet if the same customer comes through the BNPL channel those in-depth questions are not asked and instead a simple credit score check is performed. The problem is that credit rating agencies use historical or incomplete data that is not real time and doesn’t account for how financial circumstances may have changed in the past few months alone. For example, someone’s mortgage may be coming up for renewal on a higher rate and, in the midst of this they may apply to purchase a high value item on Amazon expecting a decision within thirty seconds.  

The right analysis needs to happen upfront to gain visibility of the whole picture and ascertain how much disposable income an applicant has after covering their higher living expenses. It’s an area where policy is already tightening, with the government setting out plans to bring BNPL firms within the parameters of FCA regulation. One of the key objectives of this move is consumer protection, by ensuring that lenders have the most accurate checks in place to gauge just how affordable loans are for people in the present economic landscape.

In good times, it makes sense for BNPL players to spread their risk thinner as a large customer base is keeping merchants solvent. However, as more than half of UK consumers say that they have reduced their spending on non-essentials or discretionary items, now is the time for BNPL companies to shore up their B2B and B2C community with extra support.

This requires gaining a deeper understanding of customers’ overall situation, beyond a credit check, to prevent them from taking on purchases that they cannot afford. Otherwise, missed repayments can have an adverse effect on merchants who distribute BNPL lending products to end users. The key question, therefore, is how well banks and customer data are integrated.

Prioritising better data integration

Typically, when a consumer decides to buy a product from a merchant, they are transferred to their bank’s payment portal for the next stage of the process. When the bank is making an important decision on that customer’s creditworthiness it is dependent on information from Experian or a similar credit scoring agency, without having a full view of lifestyle, shopping, and behavioural patterns (e.g., whether the customer has started opting for value retailers compared with their usual choices, due to their budgets becoming more constricted). If they pay their mortgage or credit card bills on time, the credit scoring check shows them as being creditworthy without considering the wider picture. The customer may be spending their entire salary on covering their mortgage while their disposable income may be significantly smaller. 

Access to this type of analysis could indicate to the BNPL lender that a potential borrower may not be able to cover payments for an expensive item in a few months – when winter heating bills reappear, for instance – and enable them to help the customer as well as drive positive social and economic impact by refusing credit for now. If anything, the crash of 2008 taught us the dangers of putting near-term gains above long-term cash flows and stability.

Under current systems, only credit-related transactions and questions are considered as part of the decision-making process for credit. This is a missed opportunity and needs to be turned on its head. The industry must evolve to look beyond credit to make credit decisions. And it is here that the power of technology will play a pivotal role. Innovation is moving at such a pace that machine learning algorithms can evaluate much bigger and richer pools of data relating to customers’ lives far beyond their credit transactions. 

Consumers hit by the cost-of-living crisis may increasingly turn to BNPL solutions but, without more rigorous and data-led processes, can become trapped in a cycle of debt and also negatively affect merchants. It is time to flip the approach by aligning BNPL models with today’s economic backdrop and intelligent digital capabilities.