Paul Thomas, Senior Vice President, EMEA, Acclaim
The UK Buy Now Pay Later (BNPL) sector is an undoubted success story, having grown from just £60m under a decade ago to over £13bn in 2024. The FCA recently reported that 20% of UK consumers (10.9 million adults) used it in the 12 months leading up to May 2024. The likes of Klarna, Clearpay, PayPal and Zilch, among many others, are increasingly well known, and the sector is now widely regarded as one of the most competitive areas of consumer finance.
Any financial services niche that experiences this level of growth will inevitably attract scrutiny of regulators and this has certainly been the case for BNPL. In February this year, the FCA confirmed new protections for BNPL borrowers, intended to bring providers closer to the standards expected of mainstream credit institutions.
Clearly, these are important changes, but for the BNPL providers themselves, they also present a range of compliance and operational challenges that are certain to squeeze margins and increase pressure on existing customer service resources and processes. More specifically, the new rules are likely to substantially increase customer-service workloads and regulatory oversight obligations at a time when many BNPL providers are already operating in highly competitive, margin-sensitive business models.
A defined role for AI
To address these challenges, it’s likely that some providers will invest in expanding their existing contact centre operations to manage the expected growth in customer interaction and associated compliance processes.
Whether this is financially viable, however, is debatable. For many businesses across the sector, resources are already stretched, and the prospect of scaling traditional support operations may prove increasingly difficult, if not impossible, to justify.
At the same time, the contact centre/customer experience ecosystem has been enthusiastically assessing how AI can deliver on its ability to scale. In many cases, the argument has already been won, with AI projects rapidly moving from experimentation to rollout as a means of augmenting human professionals.
AI-driven communication platforms are increasingly being positioned as a way to absorb large volumes of routine and repeatable customer interactions without creating equivalent pressure on support teams.
Most common applications include managing routine customer inquiries, automating reminders and follow-ups, prioritising complaints or providing updates on case progress, while elevating more complex issues to human agents.
For BNPL and other regulated environments, integrating effective, advanced AI tools allows human support teams to focus on higher-value or more sensitive interactions where qualities such as empathy and judgment or expert negotiation skills and experience are more important.
The best of both worlds
The primary question is not whether AI can hold a meaningful, positive conversation with customers, but whether it can operate reliably and consistently within regulated business processes and defined policy boundaries.
For instance, generic chatbot deployments using standard, off-the-shelf tools are unlikely to be sufficient for regulated financial services environments where interactions will soon be subjected to more nuanced governance.
Instead, AI systems must operate within clearly defined rules and policy boundaries rather than functioning as open-ended conversational tools. In practice, this means organisations are placing greater emphasis on conversations designed around specific outcomes and approved workflows. This should also extend to providing visibility into what decisions are made, and whether communication processes align with regulatory obligations.
The most effective AI strategies will focus on integrating tools directly into operational systems and workflows rather than treating them as a standalone chatbot layer sitting separately from the wider business process. This level of integration is important because customer service interactions rarely occur in isolation and are often connected to and informed by a variety of other tools and processes, from case management systems, payment systems, CRM platforms to complaint processes and escalation procedures.
In this context, human agents remain an integral part of the operating model. But for highly regulated industries in general and BNPL in particular, combining AI-driven scalability with human oversight offers a more sustainable way to meet rising customer-service and compliance expectations without undermining profitability. Achieving the balance right will be crucial to keeping the remarkable BNPL success story on track for the long term.