{"id":3890,"date":"2025-08-28T10:47:51","date_gmt":"2025-08-28T09:47:51","guid":{"rendered":"https:\/\/kanipayments.com\/?post_type=blog&p=3890"},"modified":"2025-08-28T10:47:52","modified_gmt":"2025-08-28T09:47:52","slug":"modernising-payments-reporting-from-spreadsheets-to-smart-data","status":"publish","type":"blog","link":"https:\/\/kanipayments.com\/blog\/modernising-payments-reporting-from-spreadsheets-to-smart-data\/","title":{"rendered":"Modernising payments reporting: from spreadsheets to smart data"},"content":{"rendered":"\n
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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\u2019re familiar, flexible and quick to patch into legacy processes. Teams know how to bend them to edge cases.<\/p>\n

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.<\/p>\n

In this article, we\u2019ll 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\u2019ll outline a practical playbook for payments business to modernise reporting workflows for scale.<\/p>\n

Manual processes & delayed reporting: the 82% problem<\/h2>\n

82% of UK payments businesses say they frequently struggle to meet reporting deadlines. The root cause isn\u2019t lack of effort, but capacity swallowed by manual, fragmented workflows.<\/p>\n

Teams spend hours every day pulling files from multiple systems, cleaning and standardising formats, enriching with missing context and hand-matching exceptions. It\u2019s work that doesn\u2019t generate insight\u2014only the eligibility to report.<\/p>\n

Kani’s payments reporting survey<\/a> 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\u2019s more than 700 hours every year lost to prep cycles.<\/p>\n

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.<\/p>\n

No wonder a majority (53%) say they spend too much time creating reports\u2014and that\u2019s before regulator-specific formats or card-scheme requirements are even factored in. Manual steps multiply with volume, channels and counterparties. Deadlines don\u2019t move.<\/p>\n

Why spreadsheets fail at scale<\/h2>\n

Efficient, accurate reporting depends on equally efficient, accurate reconciliation<\/a>. When reconciliation is slow or inconsistent, the cracks show up quickly: breaks pile up, errors spread and reporting deadlines slip. Spreadsheets simply can\u2019t keep pace with today\u2019s payments data: high volume, multi-currency, multi-rail and under constant audit pressure.<\/p>\n

At scale, three failure modes dominate:<\/p>\n

1) Errors grow with volume<\/h4>\n

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.<\/p>\n

2) Matching outgrows flat files<\/h4>\n

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\u2014work that flat files were never designed to handle.<\/p>\n

3) Audit trails break down<\/h4>\n

Spreadsheets don\u2019t preserve controls, lineage or repeatability. As scrutiny rises, flat files work against audit readiness, slowing down close processes and exposing gaps.<\/p>\n

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.<\/p>\n

It\u2019s not just tools: the systemic blockers<\/h2>\n

The goal shouldn\u2019t be to \u201cautomate reconciliation and reporting\u201d in name only. Reporting inefficiency runs deeper than tool choice. Survey data shows firms also struggle with:<\/p>\n

\u274cData inconsistency across systems<\/h4>\n

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.<\/p>\n

Without rigorous normalisation rules, even correct data appears mismatched, creating exceptions that don\u2019t need to exist. Over time, these inconsistencies also erode confidence in reports, as stakeholders question whether the outputs are truly reconciled.<\/p>\n

\u274cException management overhead<\/h4>\n

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.<\/p>\n

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.<\/p>\n

\u274cVerification and cleansing<\/h4>\n

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.<\/p>\n

\u274cReporting overload<\/h4>\n

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\u2019t map cleanly to reporting requirements\u2014regulator formats, card scheme templates, internal MI\u2014then teams are stuck reshaping the same data again and again. That means more manual adjustments, more duplication of effort and more room for error.<\/p>\n

Technology on its own doesn\u2019t fix these problems. Modernisation means re-engineering workflows around the data, not just bolting tools onto the edges.<\/p>\n

What \u201csmart data\u201d looks like<\/h2>\n

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.<\/p>\n

Instead of wasting time reworking spreadsheets or chasing gaps, teams can trust that the data foundation is stable and audit-ready. That requires:<\/p>\n