While payment data has never been in short supply, getting useful insights from that data is another matter altogether – with many failing to take advantage of it.
That’s one of many takeaways from our latest survey of around 650 finance leaders across Europe, as well as a recent podcast conversation with Carrie Haywood, Head of Customer and Business Services at AirPlus.
In short, finance experts are asking for smarter payment ecosystems: Ones that are faster, more integrated, and better at turning data into impactful decisions. That’s largely why comprehensive data analytics is among the most in-demand innovations in corporate payments today.
This gives us a clear lesson: Payment analytics should deliver tangible value that directly benefits other activities.
So, if it isn’t informing decisions, improving efficiency, or uncovering opportunities, then it’s likely a vanity project that’s using resources with little to show for it.
With that in mind, here are six ways to get more value from your payment data analytics.
Standard dashboards can be useful as a starting point, though they rarely reflect the full complexity of a modern business. That's because different regions, entities, suppliers, and business units often have very different priorities. One team may be focused on budget control, another on process efficiency, and another on policy compliance.
Payment analytics are significantly more valuable when decision-makers can shape dashboards around the metrics that matter most to their business context, rather than a one-size-fits-all snapshot of general activity. That level of personalization will help teams move beyond generic reporting and focus on insights that support action.
There’s little doubt that visibility matters. That said, visibility alone doesn't hold a candle to data-backed decision making.
As our survey data shows, many finance leaders are well aware of this: 59% are actively using payment data analytics to manage costs and optimize budgets, while 44% are using it to monitor purchases and prevent overspending. These leaders are using data to take meaningful action, not just to be on the lookout for errors or anomalies.
This highlights an important point: Payment data is most valuable when it supports a clear commercial or operational objective. This can mean identifying inefficiencies, improving forecasting, strengthening policy compliance, or gaining better oversight of spend. The point is not simply understanding what happened, but making better decisions about what happens next.
While static reports can answer predefined questions, stronger analytics give users the flexibility to investigate their own. For instance, that could mean reviewing spend by supplier, by region, or by business unit. Alternatively, it could mean comparing trends over time or isolating unusual transaction patterns.
That freedom matters because it shortens the distance between a question and a decision. Instead of waiting for the next reporting cycle or relying on a specialist to interpret the numbers, teams can identify issues earlier, challenge assumptions faster, and act before inefficiencies become embedded.
Automation is now a baseline expectation in corporate payments. It reduces manual work, improves consistency, and speeds up routine processes.
Our survey reflects that shift: 46% of finance leaders are automating fraud prevention and risk management, while 35% are in the process of implementing automation in their payment workflows.
Still, automation alone is not enough. When issues become more complex, businesses need access to expert support that can help interpret the situation and guide the right response. The strongest payment ecosystems combine both: Efficient automation for routine tasks and informed human expertise when judgment is needed.
Payment data becomes far more useful when it reflects the way the business actually operates. A high-level view may be helpful, but it rarely shows where action is needed most.
Breaking insights down by region, business unit, supplier type, traveler group, or spend category gives teams a more accurate picture of what’s happening across the organization. It can reveal where costs are rising, where friction is building, or where policy adherence is inconsistent.
That kind of context makes data actionable, as it allows leaders to focus on the areas that need attention rather than relying on broad averages that can mask important differences. The sharper the context, the more precise the response.
Data can show what’s happening, but it can’t always explain why, which is where the human perspective comes in. Conversations with internal teams, stakeholders, and payment partners can surface operational realities that aren’t obvious from the numbers alone. In many cases, that perspective is what turns a useful data point into a meaningful business insight.
It’ll also help organizations improve over time. When businesses combine analytics with a clearer understanding of how people experience payment processes in practice, they’re better placed to solve the right problems.
Significant opportunity lies in using analytics to support better decisions, faster responses, and more targeted strategic action. This can take the form of building dashboards around business priorities, making data easier to explore, adding context through segmentation, and ensuring insight can be translated into action when it matters most.
But it takes more than a surface level glance – rather a more analytical approach that uncovers the ‘why’ behind than the ‘what’.
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