In 2025, U.K. payment fraud losses reached £1.28 billion, according to U.K. Finance’s Annual Fraud Report 2026. The industry prevented a further £1.68 billion, stopping roughly 70p in every £1 of attempted fraud before it reached customers. Those figures capture the scale of the threat, but not the operational burden that follows each case. Every flagged transaction triggers a cascade of work, from investigation and data extraction through matching, reporting, and resolution, and that burden now falls on processes that were never built for this volume.
That pressure has intensified since the Payment Systems Regulator’s authorised push payment reimbursement requirement came into force. More than 18 months on, the regulator’s data shows the scheme is working, with around 89% of in-scope losses now reimbursed to victims and most claims resolved within five business days. Reimbursement, though, is a long way from recovery. The money still leaves the system, and fast, mandatory repayment compresses the window to investigate and recover it.
Where reconciliation still depends on manual, disconnected processes, that pressure surfaces as backlogs, write-offs, and mounting conduct risk. The institutions pulling ahead are the ones treating reconciliation as a financial control rather than back-office admin and using automation and AI to shift human effort from processing volume to resolving the exceptions that matter.
The hidden cost of business as usual
Payment disputes have become high-volume operational workflows, and they no longer behave as the occasional exception. In card-based environments, disputes follow structured chargeback cycles governed by scheme rules and deadlines. In account-to-account payments, such as Faster Payments or SEPA Instant, there is often no guaranteed recovery path, and funds can leave the ecosystem within minutes.
The friction compounds wherever institutions still rely on fragmented systems and manual intervention across the reconciliation lifecycle. Unresolved exceptions delay downstream reconciliation, block financial posting, and introduce uncertainty into reported balances. In high-volume environments, backlogs that should be cleared in hours can take months to resolve. The impact reaches well beyond operations. Delays in dispute resolution are increasingly viewed as customer harm under frameworks such as the Financial Conduct Authority’s Consumer Duty, turning operational inefficiency into conduct risk as well as reputational damage.
When workflows depend on manual processes, the outcome is predictable. Backlogs grow, resolution times stretch, write-offs increase, and already stretched teams absorb the pressure.
A structural problem with financial consequences
Adding headcount does not solve this problem, it only scales the inefficiency. Highly skilled finance professionals end up preparing and validating data instead of applying judgment and managing risk. The real issue is a misalignment between the operating model and the demands of modern financial services, and no amount of extra capacity corrects that on its own.
Reconciliation deserves to be treated as a core financial control. Delays or breaks in reconciliation weaken auditability, disrupt financial reporting, and reduce the ability to evidence decisions under regulatory scrutiny. In a dispute scenario, every transaction must be traceable from initiation through to resolution, with a clear audit trail supporting both financial and customer outcomes.
The write-off trap shows what that misalignment costs. Write-offs rarely happen suddenly and are actually operational failures in disguise. Miss a chargeback deadline, or delay action on an account-to-account payment, and recovery becomes impossible. At scale, this points to a process that can no longer keep up.
Automation changes the economics of reconciliation
Automation fundamentally changes the economics of reconciliation. When up to 99.9% of transactions can be matched automatically, human effort shifts toward the exceptions rather than the volume. The result is faster resolution, fewer write-offs, and consistent control at scale.
Speed carries reputational weight in dispute scenarios. Customers do not separate the fraud from the process that follows it, and slow resolution erodes trust as quickly as the incident itself.
Automation to orchestration
AI takes this further by orchestrating processes rather than replacing them. In reconciliation, the difficulty lies less in identifying anomalies and more in prioritising which ones matter within shrinking recovery windows. AI helps by aligning exception handling with financial risk and time sensitivity. High-value disputes approaching scheme or regulatory deadlines can be prioritised automatically, while low-risk matches are resolved without manual intervention. The benefit is better allocation of attention to where it has the greatest financial impact.
This only works in financial services if AI operates within established control frameworks. Decisions must be explainable, auditable, and fully traceable. Black-box decisioning relocates risk rather than reducing it.
Over time, these systems reduce noise, improve accuracy, and help institutions manage growing complexity without adding headcount. They structure and prioritise the workload rather than removing human judgment from it, allowing finance and operations teams to focus on investigation, decision-making, and risk management with full visibility and control. Done well, this brings reconciliation closer to its core purpose, maintaining a single, trusted version of the truth.
As transaction volumes rise and system landscapes fragment, that goal becomes harder to reach. Intelligent orchestration restores it by integrating data, standardising workflows, and delivering real-time visibility across the entire lifecycle. Institutions getting this right are already seeing tangible results: reduced backlogs, faster resolution times, fewer write-offs, and finance teams shifting from reactive firefighting to proactive control.
The path forward
Fraud and payment disputes are not going away. They are accelerating in both volume and sophistication. The question is no longer whether institutions can detect fraud, but how efficiently, accurately, and at what scale they can resolve it.
The opportunity lies in closing the gap between dispute and resolution, moving from backlog to breakthrough. Institutions that modernise reconciliation through automation and AI will strengthen financial control, protect revenue, and meet rising regulatory and customer expectations. Fraud may be inevitable, but falling behind in resolving it is a choice, and increasingly it is where institutions stand to lose the most.
ABOUT THE AUTHOR:
Darren Heffernan is CEO of Trintech, a global provider of cloud-based financial close solutions. He joined Trintech in 2001 and has held a number of strategic and corporate development roles during his tenure.
Photo credit: iconicbestiary/Freepik
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