Julie Taylor, Head of Fundraising Operations and Improvement at the King’s College Hospital Charity has a problem faced by almost every charity and small to medium business (SMB) in the UK. Responsible for maximising the donations to the charitable wing of one of the UK’s most prestigious hospitals, she finds that funds that should be going to change lives are slipping between the cracks.
I think that one of the biggest challenges is the spread of payments across multiple platforms,” she says. “People choose to pay donations using a variety of online tools, and for each one there is a different setup process, a different payment processing partner, and a different date that the money is available to us.
Julie has been left wondering why a payment that failed on one platform would succeed on another, why some potential donors would abandon payments while others wouldn’t, and why some subscribers would ‘churn’ within months while others remained for life.
This is not only a problem for the charity sector – every part of the UK’s commercial ecosystem deals with the same challenges. Chris Jones, Managing Director of payments consultancy firm PSE Consulting says that “the opportunity to make small businesses more efficient and have more time to get on with running their business as opposed to chasing lost transactions is something we are very much aware of. If you ask small businesses what causes them pain it is those uncollected payments and the three to four days a month that they typically spend doing reconciliation.”
Sources of hidden revenue loss are everywhere in modern business, and particularly in payments systems, which have only gotten become more complex. Despite this, many companies don’t look beyond transaction fees as their sole source of payments-related losses.
Payments technology provider Access PaySuite commissioned research of hundreds of management and finance professionals at SMEs across the UK to find the scale of the problem. The results were striking: a plurality of respondents (49%) are losing anywhere from £5,000 to £100,000 annually to failed transactions, payment-related customer churn, and the associated administrative burden. Around 8% report losing £1 million or more.
As a result, 95% of the companies surveyed are looking at AI-based systems as a possible way to find sources of hidden revenue loss.
An ecosystem built for leakage
Hidden revenue loss is embedded in the structure of modern payment systems. Fragmentation means that no single team sees the full picture. Authorisation failures, abandoned checkouts, refunds, chargebacks and silent churn sit in different dashboards and different departments.
Tony Craddock, Director General of The Payments Association, describes the issue as “a whole series of typically quite small failures”. An authorisation failure of a few percentage points may not seem material in isolation. But, he says, “because all of these small little pieces are in different functions within the company, the size of the overall problem is often unknown. It’s almost like a hidden loss of revenue.”
Access PaySuite’s research suggests that on average 3.4 per cent of transactions fail, and 55.8 per cent of those are never recovered.
Nearly half of businesses report checkout abandonment, with an average abandonment rate of 7.8 per cent. More than one in five say customers have switched to competitors for a better payment experience.
Sandra Mianda, Founder and CEO of Paypr.work, argues that the blind spot is cultural as well as technical.
Traditionally payments have been seen as a cost centre. The KPIs tracked are fees and approvals. But somewhere between intent and settlement, declines can have many different reasons that account for a lot more than what the data shows. There’s a real hidden opportunity in those failed transactions.
The problem is compounded by complexity. SMEs often operate with multiple payment service providers, trade across borders and currencies, and rely on legacy infrastructure. Data flows through finance, product and customer service teams in silos. Decisions are made locally, but the revenue impact is global.
The time tax
The revenue gap is more than just lost money, it’s lost time.
More than 70 per cent of organisations surveyed by Access PaySuite say they spend between five and 20 hours per week managing payment failures and related administration. Fewer than four in ten report having full visibility into the wider revenue impact of payment problems.
For small businesses, that drag is personal. “If you ask small businesses what causes them pain, it’s those exceptions of uncollected payments,” says Chris Jones, managing director of PSE Consulting. “Or the three to four days a month they typically spend doing reconciliation between different financial systems.”
Jones argues that much of the friction sits across the payments value chain. Issuers decline transactions without full context. Acquirers and schemes apply their own risk assessments. Meanwhile, businesses wrestle with refund management, disputes and chargebacks.
If you can move them into a single user experience and allow those exceptions to be handled in a much shorter activity focused on edge cases, that’s transformative.
Julie Taylor sees the same pattern in the charity sector. Failed donations require follow-up calls. Regular giving mandates lapse. Indemnity claims are difficult to challenge. “The technology is geared towards bringing payments in, rather than actually focusing on those failures,” she says. The cost is not just financial. It is time diverted from mission-critical work.
The new layer of intelligence
Against that backdrop, it is not surprising that 95 per cent of SMEs surveyed believe AI systems could help close the revenue gap.
David Birch, an international adviser on digital financial services, says the promise of AI lies in pattern recognition. “It will find patterns. It will uncover connections. It will spot trends that you wouldn’t necessarily see yourself,” he says. Even small improvements can move the bottom line. “You only need to take decline rates down by a small amount to add a lot.”
Mianda cautions that AI must go beyond basic automation. “The data is one thing,” she says. “But being able to interpret what that story tells you in your environment and process that in a way that drives intelligent decisions, that’s the next step.” In the context of declines, that might mean dynamically deciding whether to retry a transaction, route it differently or adjust authentication triggers, all while maintaining compliance and trust.
Access PaySuite is launching a new unified payments platform with AI built in. Using AI to cut through raw data to surface meaningful insights, it frees teams from spreadsheet exports and specialist analysis, enabling immediate payment management and greater business value.
Julie Taylor sees predictive modelling as the breakthrough. “If we can predict where payments may fail or there may be issues with payments, we can start to do something about that and prepare in advance,” she says. For a charity dependent on regular giving, identifying at-risk donors before a subscription collapses could protect long-term income.
Craddock believes the opportunity is broader still. AI can help design more intuitive user journeys, strengthen fraud prevention at source and create feedback loops that recover abandoned carts. Over time, he argues, programmability and micro-payments may reshape how value moves through the system, opening new revenue models.
Yet there are cautions. Jones notes that AI-driven commerce may shift power towards platforms that control customer relationships. Businesses that gain incremental revenue through AI-generated demand must still consider data ownership and disintermediation risk. The technology is not neutral; it reshapes the ecosystem.
Closing the hidden revenue gap
The hidden revenue gap in UK SMEs is neither marginal nor inevitable. Failed transactions, abandoned checkouts, and payment-driven churn collectively cost tens of thousands of pounds per firm each year and, in some cases, millions. The administrative burden compounds the damage, draining time from already stretched teams.
What has changed is not the existence of lost revenue but the ability to see it. AI does not eliminate the structural quirks of a payments infrastructure built for a different era, but it does offer a way to see patterns, test interventions and automate recovery at a scale no human team could match.
The question for finance leaders is no longer whether their business has a hidden revenue gap, it is whether they have the visibility to see it and the tools to act. Platforms like Access PaySuite are making that transition possible, turning payments from a cost centre into a source of competitive advantage, with the gap closing with the tools that can measure it.
To learn more about how AI-driven payments insights can help uncover hidden revenue and reduce payment failures, visit our AI and the hidden revenue gap resource page or get in touch below.
FAQs
What is the ‘hidden revenue gap’ in UK SMEs?
The hidden revenue gap refers to money lost through failed transactions, abandoned checkouts, customer churn and admin inefficiencies, costs that don’t show clearly on the P&L but add up to tens or even hundreds of thousands each year.
Why are payment failures so hard for SMEs to identify and fix?
Payment failures are spread across multiple platforms, processors and internal teams. Because the data is fragmented, no single function sees the whole journey from customer intent to settlement, making it difficult to diagnose the root cause of revenue loss.
How much revenue do SMEs typically lose to payment‑related issues?
Research shows that nearly half of UK SMEs lose between £5,000 and £100,000 annually to failed transactions, churn and administrative time, and around 8% lose £1 million or more.
Why are 95% of SMEs now considering AI for payment optimisation?
AI can analyse patterns across decline codes, customer behaviour and payment routes to identify hidden issues, predict failures, automate recovery actions and improve authorisation rates, something manual teams and spreadsheets can’t do at scale.
How can AI help SMEs recover revenue lost through payment failures?
AI can dynamically adjust retry strategies, optimise authentication steps, detect friction points, and surface high‑risk transactions or donors before they churn, helping businesses recover revenue that would otherwise be lost.