With traditional credit models struggling to keep pace with changing financial behaviours, UK retailers face a growing challenge – lost conversions at checkout, writes Andy Smith, CEO of Snap Finance UK, who explains how AI and smarter data models are rapidly changing retail finance …
As retail evolves, the role of finance is no longer just transactional – it is increasingly central to unlocking demand. In the furniture sector, where purchases are high value and often carefully planned, a declined finance application rarely leads to a delayed purchase. Instead, when customers cannot access finance, they typically abandon the purchase altogether, making it the difference between completing a sale and losing it entirely.
This challenge is not only about conversion, but also financial inclusion. Approximately 20 million UK adults are financially underserved due to low credit scores, limited credit histories or fluctuating incomes, leaving them excluded by traditional PoS finance. For retailers, this creates lost sales and missed opportunities to serve a broader customer base.
At Snap, this has led to a focus on rethinking how retail finance lending decisions are made beyond traditional credit score-based assessments. Supported by advanced AI and machine learning capabilities, there’s an opportunity to enable more dynamic customer journeys when accessing credit, which is better aligned with real-world affordability. The result is expanded access to finance in a more inclusive and responsible way.
Achieving this means ensuring decision-making models are timely. Most traditional credit models are updated annually, which made sense when financial behaviours were more predictable. Today, however, many consumers are more exposed to income volatility, changing spending patterns and cost of living pressures.
Consequently, models trained on data from 12 months ago, or longer, may not accurately reflect a consumer’s current affordability. Recognising and being able to adjust to this creates a significant advantage as it allows more accurate, responsive lending decisions better aligned to how customers actually manage their finances. This underpins why Snap retrains and redeploys its models every few weeks, not every 12 months.
Complementing improved lending decisions to drive conversions at the checkout is speed, which is critical to maintaining customer momentum and completing the sale. Snap combines AI with open banking and multi-bureau data across the decisioning process as an integrated solution. Income and expenditure data is assessed in real time to streamline decisions, automate verification and improve risk evaluation.
This reduces friction at the PoS, with fewer documents, fewer delays, and fewer customers abandoning their basket. The result is a faster, more efficient process, and a measurable increase in approval rates for consumers often declined by mainstream lenders.
This has direct advantages for furniture retailers. More accurate, real-time decisioning means more customers can access finance at the moment of intent, reducing drop-off at checkout and converting demand that might otherwise be lost. In a category where finance is often the deciding factor between browsing and buying, this uplift can have a material impact on a retailer’s weekly sales performance.
As retail continues to evolve, financial services must adapt alongside it. The combination of machine learning, rapid model iteration, integrated open banking and multi-bureau data, and advanced credit decisioning, is enabling a more responsive and inclusive model of retail finance.
For furniture retailers, this shift is becoming a clear differentiator. Faster, smarter and more adaptive finance is no longer a back-end function – it is a key driver of growth. In a market where customer finances are changing faster than ever, there is a clear opportunity to create a more inclusive lending environment to support financially underserved customers. The real question for retailers is whether their finance partner can adapt at the same pace.
