artificial intelligence This technology may dominate boardroom discussions, but payments companies face a more fundamental challenge.
They already have huge sums of money Data. The trickier task is turning that information into something finance teams can trust.
This topic is the one that anchored the last installment of PYMNTS Summer School The series that appeared Ben CatterallGlobal Head of Solutions Architecture for Financial ERP Finabs. Catterall said companies that gain strategic advantages will not necessarily collect more data than their competitors. They will build stronger financial foundations that preserve the meaning behind every transaction event.
“Every payment company has a lot of data,” Catterall said. “But data without context is not particularly useful.”
He said it’s important to understand what the data represents, along with “what happened in the real world to generate that data.”
Cards, digital wallets, buy now, pay later (BNPL) products, subscriptions, and app store purchases all create different financial records. Cross-border trade adds currencies, settlement spreads and multiple payment gateways, while transaction volumes continue to rise.
Regardless of the payment type, finance departments must accurately reconcile transactions and move them to the general ledger without delaying business. Legacy finance systems, built on the payment processing and shorthand data of the pre-cloud era, often struggle to keep up with this complexity.
The consequences extend beyond operational efficiency. Catterall described one multinational payments client that processed transactions across nearly 18 countries. On a summary level, the books seemed balanced. A closer examination of transaction-level records revealed foreign exchange spreads that averaged about 2%, creating millions of dollars in lost revenue across nearly $100 million in cross-border payment volume.
“If you apply that to $100 million of cross-border payments that are processed, that could be a loss of $2 million a year,” Catterall said.
For finance leaders, examples like these reinforce why transaction-level visibility is more than just an accounting exercise. It provides a way to understand where revenue is disappearing, where payment costs are accumulating, and where operational changes can improve financial performance.
Build the foundation before deploying AI
The same principle applies to artificial intelligence.
Many financial institutions have launched proofs of concept for AI over the past couple of years, but relatively few have progressed to production. The obstacle often lies beneath the AI models themselves, Catterall said.
“If you’re relying on aggregated, aggregated, summarized data, and you put an AI tool on top of that, all the AI tool can learn from it is the summary view,” Catterall said. “She doesn’t have enough to go on.”
Catterall said research shows that 46% of AI acceptance guides fail to reach production because poor data quality limits their effectiveness.
That philosophy constitutes Efficiencyplatform, which captures financial events as they happen rather than reconstructing them at the end of the reporting period.
The goal is not just a faster close at the end of the month. It gives finance teams continuous visibility into margins, payment costs, and business performance as activity occurs in real time, enabling finance teams to make real-time business decisions.
Catterall said financial institutions should treat financial data as core infrastructure and not as a byproduct of payments. “Making this available to businesses is discriminatory,” he added.
Companies that maintain detailed transaction records and make them available across treasury, pricing, forecasting and risk are better placed to support growth without sacrificing financial control. This is what the financial reality of payments really means and is what Fynapse offers the likes of it T-Mobilewhich now processes 200 million journal lines per hour in real time.
As payment methods proliferate and AI takes on a greater operational role, this discipline may prove to be finance’s most enduring competitive advantage.
Companies that modernize their financial data architecture today will be better equipped to understand tomorrow’s transactions rather than simply record them.
What financial grade data unlocks
The modern approach to data means three things. First, the data shows individual transactions, not just totals. Second, it keeps the details behind each transaction. These details include payment method, currency, fees, and who participated. Finally, the data is available immediately, and is not collected together later during the lockdown.
a witness Complete PYMNTS Summer School interview To learn:
- Why real-time accounting is changing the relationship between finance and auditors.
- How agentic AI can reshape financial management and reporting.
- What should finance teams ask before agreeing to another AI initiative?
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