Addressing the data standardization gap in corporate payments

Corporate payments sit at the center of how organizations operate, invest, and manage risk.  

Every transaction carries information about who is being paid, for what, under which terms, and in which context. And when this information is captured in a consistent, structured way, it becomes a powerful asset for forecasting, controls, and strategic decision-making. 

Across industries, pressure is growing to treat payment data as more than just evidence of money moving from A to B. Finance leaders want better insight into spend patterns, IT teams want cleaner inputs for automation and analytics, and Compliance needs reliable detail to monitor risk and meet regulatory expectations. All of these goals depend on having a common language for describing payments.

Thankfully, regulators and industry bodies are pushing in the same direction. Standards such as ISO 20022 promote richer, more structured transaction data that can be processed automatically, checked more rigorously, and reused across systems without rework. ^[1]

Organizations that align with these standards are better positioned to modernize their payment operations and reap the benefits.

So, what happens when this data isn’t standardized – and how can organizations fix it?

1. Data silos limit spend visibility

A major challenge is that spend data rarely lives in one place. Accounting software, travel and expense tools, and countless spreadsheets each hold a fragment of the truth. But when you try to answer basic questions about total spend, you’re left stitching together incomplete views – and the insights you care about most tend to fall into the gaps.

These silos slow decision-making at every level. CFOs can struggle to answer seemingly simple questions like, “How much did we spend on travel across all departments last quarter?” Getting to an answer often means pulling separate reports from multiple tools, exporting them to spreadsheets, and manually stitching everything together before any real analysis can start.

Standardizing how spend is captured and labeled removes much of this friction. With aligned formats and definitions in place, Finance can explore company-wide spending patterns quickly, IT can connect and automate systems more reliably, and Compliance can monitor transactions with fewer blind spots. The result is a far clearer view of where money is going, and far less time wasted chasing the underlying data.

2. Incompatible file types mean manual work

Another source of friction is incompatible file formats. Card payment data might export as XML, the accounting system expects CSV, and other platforms generate Excel workbooks. 

Because these files don’t line up out of the box, someone ends up acting as “human middleware” – copying, reformatting, and importing data just to keep systems in sync. The work is repetitive, error-prone, and pulls people away from analysis and oversight. 

Standardization removes much of this overhead. Instead of treating every integration as a one-off project, organizations define a common structure for payment data and stick to it. That could mean agreeing a shared CSV layout across tools or consolidating around a payments standard such as ISO 20022 XML.

Once there is a consistent target format, translation of that data can be automated. As data flows from one system to another without constant human intervention, delays are reduced and mistakes are avoided. This means teams who previously spent hours wrangling files get time back for investigations, forecasting, and other higher-value work.

3. Errors rise and reporting slows down

When data isn’t captured in a consistent way, every handoff becomes a chance for mistakes. Manual re-entry, copy-paste work, and ad hoc reformatting will inevitably introduce typos, misaligned columns, or missing rows that quietly change the numbers. 

Over time, these small discrepancies can accumulate into bigger issues: reconciliations take longer, reports don’t fully align with underlying systems, and stakeholders start to question which figures they can rely on.

Standardization reduces both the noise and the delay. With payment data following a shared structure and format, there is far less manual manipulation required to prepare it for reporting. 

Clean, consistent datasets can move directly into analytics and BI tools, cutting down on data prep and shortening reporting cycles. This means Finance teams spend less time fixing anomalies and chasing missing details, and more time interpreting trends and advising the business. The result is faster, more dependable reporting and greater confidence in the numbers that drive decisions.

4. Lack of standards blocks automation

Many organizations have ambitions for automated finance workflows – straight-through payments, real-time expense checks, and faster reconciliation. The sticking point is often the data feeding those workflows. 

When every system and department structures information differently, automation rules and scripts become brittle. Each new file layout or coding convention demands extra mapping and clean up, dragging people back into the loop to review and correct exceptions.

Standardization removes much of this fragility. When payment data follows shared field formats, codes, and definitions, it’s far easier to plug into automated processes and advanced analytics. Tools that depend on clean, well-structured inputs – including robotic process automation, machine learning, and AI – can operate far more reliably.

With consistent standards in place, workflows that previously needed manual intervention can run end-to-end in software because the inputs are predictable. Automation can then scale across more use cases, while you spend less time firefighting data issues and more time shaping strategy.

5. Audits and compliance complications

Audits and regulatory reviews depend on complete, well-documented transaction histories. When fragmented data exists across systems and formats, assembling that history becomes a project in itself. Auditors may need to request multiple exports, reconcile them manually, and spend extra time checking that the data set is actually complete. That drives up both the time and cost of each audit cycle.

Inconsistent tech structures also create real compliance risk. If different systems capture different fields or apply different coding conventions, key information can be missed or misclassified. Over time, these small gaps accumulate into larger blind spots: Controls are harder to evidence, exceptions are harder to trace, and the likelihood of findings or penalties increases.

Data standardization lightens this load. When payment records follow shared formats and definitions, tracing a transaction from initiation to settlement is far more straightforward. Auditors can sample and test data without constantly converting or reconciling files, and compliance teams can generate regulatory reports with greater confidence that nothing has fallen through the cracks.

Organizations that align their internal data practices with established industry standards, such as ISO 20022 for payments, see this effect most clearly. A common data language improves transparency, supports more robust monitoring, and reduces manual intervention in control processes. 

The cleaner and more consistent your payment data, the easier it becomes to demonstrate to regulators and auditors that your house is in order.

Closing the data standardization gap

The lack of standardization in payment data sits behind many of the daily frustrations in Finance, IT, and Compliance. Closing that gap turns payment information from an administrative burden into an asset you can actually use. 

With a common structure in place, the same data that once slowed down reporting, audits, and automation starts to support faster decisions, stronger controls, and smoother operations.

Standardized payment data also creates a solid foundation for what comes next. Once fields, codes, and formats are aligned, it becomes far easier to layer on enrichment, bring in detailed Level 3 transaction data, and apply more advanced analytics and AI-driven tools. Every team is working from the same playbook, which makes downstream improvements cheaper, quicker, and more reliable.

There's plenty more interesting insights coming up – subscribe today to our newsletter for the latest in corporate payment.

FAQ

What is data standardization?

Data standardization refers to the alignment of formatting, structure, and file type of data, ensuring it is consistent, complete, and machine-readable. Standardizing data in this way allows it to be transferred between systems seamlessly while making it easier to analyze at scale.

Why is data standardization important in corporate payment?

Data standardization is particularly important for the corporate payment industry as it enables the flow of transaction data between back-office systems and platforms. This then allows the data to be used for compliance and reporting, as well as for visibility and analysis into spending. This is especially important today with the implementation of AI workflows which utilize structured data, as well as the growing regulatory requirements relating to data security and reporting.

What is the current global financial data standard?

While there is no single standard governing all financial data globally, ISO 20022 is the de facto global standard for payment and financial messaging. ISO 20022 is a common data language for structuring and exchanging financial transaction data. It relies on structured, XML-based data with standardized fields, clear definitions, and significantly richer detail per transaction to ensure cross compatibility between financial platforms, banks, and other systems.

[1]  ISO 20022 | iso20022.org