Spreadsheets are still embedded in the UK payments industry. More than half of firms (56%) use them as a core tool for reconciliation and reporting. They’re familiar, flexible and quick to patch into legacy processes. Teams know how to bend them to edge cases.
But what works in a start-up or small programme rarely scales. As volumes grow, spreadsheet-driven workflows become brittle foundations for compliance, audit readiness and growth.
In this article, we’ll look at how spreadsheets slow down reporting, why reconciliation is the foundation of accurate outputs and what smart data looks like in practice. Finally, we’ll outline a practical playbook for payments business to modernise reporting workflows for scale.
Manual processes & delayed reporting: the 82% problem
82% of UK payments businesses say they frequently struggle to meet reporting deadlines. The root cause isn’t lack of effort, but capacity swallowed by manual, fragmented workflows.
Teams spend hours every day pulling files from multiple systems, cleaning and standardising formats, enriching with missing context and hand-matching exceptions. It’s work that doesn’t generate insight—only the eligibility to report.
Kani’s payments reporting survey data shows the scale of the drain. Before reconciliation even begins, firms spend about three hours per day on data preparation, with some spending five or more. On a daily cadence, that’s more than 700 hours every year lost to prep cycles.
And once prep is done, the heavy lifting continues. Respondents flagged data collection, matching and exception handling as the top time drains (each cited by 32%), with data standardisation (31%) and audit adjustments (28%) close behind.
No wonder a majority (53%) say they spend too much time creating reports—and that’s before regulator-specific formats or card-scheme requirements are even factored in. Manual steps multiply with volume, channels and counterparties. Deadlines don’t move.
Why spreadsheets fail at scale
Efficient, accurate reporting depends on equally efficient, accurate reconciliation. When reconciliation is slow or inconsistent, the cracks show up quickly: breaks pile up, errors spread and reporting deadlines slip. Spreadsheets simply can’t keep pace with today’s payments data: high volume, multi-currency, multi-rail and under constant audit pressure.
At scale, three failure modes dominate:
1) Errors grow with volume
Nearly half (44%) of reconciliation errors stem from human intervention and system integration issues. Add lack of real-time access and inconsistent formats, and you have errors built into the process, not occasional mishaps.
2) Matching outgrows flat files
Cross-currency transactions (23%), multiple payment channels (22%) and high volumes (20%) are the top matching barriers. Each adds manual effort and creates more exceptions to chase—work that flat files were never designed to handle.
3) Audit trails break down
Spreadsheets don’t preserve controls, lineage or repeatability. As scrutiny rises, flat files work against audit readiness, slowing down close processes and exposing gaps.
The business impact is real: respondents report financial discrepancies (35%), compliance risk (29%), delayed reporting (28%) and even damage to investment and growth prospects (34%) when errors creep in.
It’s not just tools: the systemic blockers
The goal shouldn’t be to “automate reconciliation and reporting” in name only. Reporting inefficiency runs deeper than tool choice. Survey data shows firms also struggle with:
❌Data inconsistency across systems
Multiple sources, formats and naming conventions undermine match rates and trust in outputs. For example, the same transaction might be tagged with different identifiers across a processor file, bank statement and internal ledger.
Without rigorous normalisation rules, even correct data appears mismatched, creating exceptions that don’t need to exist. Over time, these inconsistencies also erode confidence in reports, as stakeholders question whether the outputs are truly reconciled.
❌Exception management overhead
32% of firms cite exceptions as one of the most time-consuming stages. The problem is often the fragility of matching logic and standards or missing enrichment data, creating false breaks that pile into exception queues.
Analysts are forced into manual investigation cycles, chasing issues that could have been resolved automatically with stronger rules or better upstream controls. This overhead grows in proportion to transaction volume, making it a major scalability barrier.
❌Verification and cleansing
Before reconciliation and reporting can even begin, teams spend significant time validating input files, correcting missing or malformed fields and ensuring data completeness. These pre-checks are vital for accuracy, but they also delay reporting cycles.
❌Reporting overload
Even where software is in place, many firms still spend too much time producing reports (53%). This points to issues with configuration and data models, not just tooling gaps. If the reconciliation engine doesn’t map cleanly to reporting requirements—regulator formats, card scheme templates, internal MI—then teams are stuck reshaping the same data again and again. That means more manual adjustments, more duplication of effort and more room for error.
Technology on its own doesn’t fix these problems. Modernisation means re-engineering workflows around the data, not just bolting tools onto the edges.
What “smart data” looks like
Smart data is about reliability. It means reconciliation outputs that are already validated, normalised and enriched, so they can flow straight into reporting, analytics and audits every day, at scale.
Instead of wasting time reworking spreadsheets or chasing gaps, teams can trust that the data foundation is stable and audit-ready. That requires:
- Automated ingestion and matching across processors, issuers, acquirers and banks, reducing the daily prep burden that currently consumes hundreds of hours a year.
- Normalisation and standardisation that collapse format differences into a single model, raising auto-match rates and reducing exceptions.
- Enrichment by design. Codifying enrichment in the pipeline, not in one-off spreadsheets, strengthens audit trails and reporting quality.
- Governance-ready outputs with lineage, controls and repeatability, so regulator and scheme reports don’t require last-minute heroics.
A playbook to modernise payments reporting
As data volumes grow and regulatory scrutiny tightens, businesses can’t afford to patch spreadsheets indefinitely. Modernising reporting means rethinking workflows and tackling each blocker step by step.
Here’s how to get there:
✅Simplify your system landscape
Complex, layered workflows are a hidden tax on speed. Unify systems to reduce silos and handoffs.
✅Attack exceptions at the source
You won’t eliminate errors, but you can raise auto-match rates and streamline exception workflows, cutting the time drain and stabilising reporting timelines.
✅Treat deadlines as diagnostics
Missed submissions are an upstream signal. Use reporting SLAs as health checks to expose collection, standardisation and matching bottlenecks.
✅Automate now, save later
Moving a three-hour daily prep routine into an automated pipeline reclaims ~700 hours a year—time that can be redeployed to analysis, forecasting and audits.
✅Adopt tailored solutions
Off-the-shelf won’t fit every payments data environment. Choose platforms and partners that adapt to your workflows rather than forcing you to contort to the tool.
Bottom line
Spreadsheets gave payments teams a flexible start, but at scale they’ve become a structural risk. They drain capacity, expand the error surface and leave firms scrambling to meet reporting obligations.
Modern operations demand more. Smart data—reconciled, validated, normalised and enrichment-ready—creates reporting that’s accurate by default, not assembled at the last minute. It’s about workflows designed for scale, auditability and trust, not patched flat files.
Firms that make this shift will reduce exceptions, strengthen resilience and build a data foundation that supports growth, investor confidence and regulatory trust. That’s the real prize of moving from spreadsheets to smart data.
📊 Want to learn more?
Explore how Kani helps automate payments reporting workflows end-to-end—from ingestion to audit.
