Why Data is the Real Problem in Payments Reconciliation

When people talk about reconciliation in payments, the conversation usually jumps straight to the matching process, aligning transactions, balancing books, and closing the loop.

But let’s be honest: matching isn’t the real problem. The data is.

At Kani, we’ve worked with fintechs, banks and payment providers of all shapes and sizes. And we’ve seen the same story play out time and again. Reconciliation fails because the underlying data is fragmented, inconsistent and incomplete.

The Hidden Pitfalls in Payments Data

Reconciling financial data sounds simple in theory, but in practice, it’s anything but. The three biggest culprits? Inconsistent formatting across providers, missing metadata and manual interventions.

Every provider seems to do things their own way. One might use ISO 20022, another sends custom CSVs, and some still rely on PDFs. Making sense of this mess is like assembling flat-pack furniture without instructions, and with half the screws missing.

Metadata is the glue that holds reconciliations together, and when it’s incomplete or missing, things fall apart fast. We’ve seen transaction IDs get truncated, timestamps disappear, and currencies go unspecified.

Downstream, finance teams end up manually cross-referencing files just to piece together what should’ve been obvious in the first place. But manual fixes are slow, error-prone and impossible to audit. One typo can throw off an entire reconciliation, which is often unnoticed until month-end.

The good news is that these problems aren’t inevitable. Better data management, clearer standards and consistent processes can eliminate most of them. A little structure up front saves hours of chaos later.

Automation Is Only as Good as the Data Behind It

There’s a lot of buzz around automation in finance, and rightly so. But let’s be clear: automation isn’t a shortcut. If you feed bad data into a system, you just get flawed results faster.

For automation to truly work, the data going in has to be clean, complete and standardised. That means putting proper data preparation steps in place before reconciliation even begins: validating incoming files, enriching missing metadata, resolving inconsistencies in format or currency and ensuring everything aligns across sources.

When that foundation is solid, automation becomes genuinely powerful. Reconciliation speeds up. Exception handling becomes the exception, not the norm. And finance teams can rely on the outputs with confidence, because they know the inputs are trustworthy.

Setting the Standard with the Data Scorecard

Issues with underlying data quality are signs of deeper, structural problems in how payments data is created, formatted and shared. We believe these problems need to be measured objectively. That’s why we created the Kani Data Scorecard: a transparent framework to assess how “fit-for-purpose” your payments data really is.

It measures key dimensions like completeness, consistency and accuracy across all your imported files. It flags anomalies, highlights gaps and gives you a clear benchmark of how your data is performing over time, whether you're working with scheme reports, processor files or internal ledgers.

When your data is solid, everything else follows. Reconciliation becomes faster. Reporting becomes smoother. Audits get easier. And your finance team can stop firefighting and start focusing on the work that matters.