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Artificial intelligence may dominate boardroom discussions, but payments companies face a more basic challenge.

They already possess enormous amounts of financial data. The harder task is turning that information into something finance teams can trust.

That theme anchored the latest installment of the PYMNTS Summer School series, which featured Ben Catterall, global head of solutions engineering at the finance ERP Fynapse. Firms that gain the strategic advantages will not necessarily collect more data than their competitors, Catterall said. They will build stronger financial foundations that preserve the meaning behind every transactional event.

“Every payment company has tons of data,” Catterall said. “But data without context is not particularly useful.”

It’s crucial to understand what the data represents, along with “what happened in the real world to generate that data,” he said.

Cards, digital wallets, buy now, pay later (BNPL) products, subscriptions and app store purchases each generate different financial records. Cross-border commerce adds currencies, settlement differences and multiple payment gateways, while transaction volumes continue to climb.

Regardless of payment type, finance departments must still reconcile transactions accurately and move them into the general ledger without delaying the business. Legacy finance systems, built around batch processing and summarized data for a pre-cloud era, often struggle to keep pace with that complexity.

The consequences extend beyond operational efficiency. Catterall described one multinational payments client that processed transactions across nearly 18 countries. At a summary level, the books appeared balanced. A closer examination of transaction-level records uncovered foreign exchange spreads that averaged about 2%, creating millions of dollars in lost revenue across roughly $100 million in cross-border payments volume.

“If you apply that to $100 million of cross-border payments being processed, that could be $2 million a year that’s lost,” Catterall said.

For finance leaders, examples like that reinforce why transaction-level visibility is more than an accounting exercise. It provides a way to understand where revenue disappears, where payment costs accumulate, and where operational changes can improve financial performance.

Build the Foundation Before Deploying AI

The same principle applies to AI.

Many financial institutions have launched AI proofs-of-concept during the past two years, but relatively few have progressed into production. The obstacle often lies beneath the AI models themselves, Catterall said.

“If you’re relying on batched, aggregated, summarized data, and you put an AI tool on top of that, all that AI tool can learn from is the summary view,” Catterall said. “It doesn’t have enough to go on.”

Research shows that 46% of AI proofs of acceptance fail to reach production because poor data quality limits their effectiveness, Catterall said.

That philosophy shapes Aptitude’s platform, which captures financial events as they occur instead of reconstructing them at the end of the reporting period.

The objective is not simply a faster month-end close. It is to give finance teams continuous visibility into margins, payment costs, and business performance while activity is taking place in real time, enabling finance teams to drive real-time business decisions.

Finance organizations should treat financial data as core infrastructure rather than a byproduct of payments, Catterall said. He added, “Making that available to the business is a differentiator.”

Companies that preserve detailed transaction records and make them available across treasury, pricing, forecasting and risk are better positioned to support growth without sacrificing financial control. This is what financial truth for payments really means and is what Fynapse delivers for the likes of T-Mobile, which now processes 200 million journal lines an hour in real time.

As payment methods multiply and AI assumes a larger operational role, that discipline may prove to be finance’s most durable competitive advantage.

The firms that modernize their finance data architecture today will be better equipped to understand tomorrow’s transactions rather than simply record them.

What Finance-Grade Data Unlocks

A modernized approach to data means three things. First, the data shows individual transactions, not just totals. Second, it keeps the details behind each transaction. Those details include the payment method, the currency, the fees and who was involved. Finally, the data is available right away, not pieced together later during a close.

Watch the complete PYMNTS Summer School interview to learn:

• Why real-time accounting changes the relationship between finance and auditors.
• How agentic AI could reshape financial governance and reporting.
• What finance teams should demand before approving another AI initiative.

For all PYMNTS AI coverage, subscribe to the daily AI Newsletter.

Artificial intelligence is not creating a finance revolution as much as it is conducting an audit.

John Landy, chief technology officer at Billtrust, told PYMNTS that the lesson chief financial officers should carry forward from previous waves of digital transformation is straightforward. AI cannot compensate for poor enterprise architecture.

“The biggest issue is around having something be a bolt-on investment after the fact when you have fragmented data and solutions and systems and vendors working in your current environment today,” Landy said. “If you think about re-architecting from the data all the way up through and think about control and context throughout each of those, then you can really reap the most rewards from the AI investment.”

The observation reflects a broader shift occurring across enterprise finance. Competitive differentiation is moving away from purchasing AI capabilities and toward designing the operating environment that allows those capabilities to function effectively.

AI Is Only as Good as the Financial Context Behind It

The accessibility of AI can obscure its dependency on the systems beneath it. Powerful models are broadly available. Accurate, connected and operationally useful data is not.

“The AI tooling is available and the AI is available to all end users at most organizations,” Landy said. “It will present the data however accurate you give it.”

Accounts receivable makes the problem especially visible. The function often spans multiple ERPs, payment systems, customer records, vendor applications and manually maintained spreadsheets. Some systems update in real time. Others operate in batches. Employees reconcile the differences.

“Most organizations are dealing with complex environments. Multiple ERPs is the standard sort of system background, as well as many vendors … large and small vendors across the ecosystem to get their work done,” Landy said, noting that spreadsheets also add another layer of temporal uncertainty.

“You can think of a spreadsheet as a snapshot of data in time that you are trying to correlate across real-time systems, batch systems and reconciliation processes,” he said.

Installing artificial intelligence above that environment does not resolve the discrepancies. It gives the system a faster way to process them.

“AI has to have visibility into all of it,” Landy said. “It cannot be added after the fact.”

That principle is starting to show up in how AR data reaches AI tools. Rather than requiring finance teams to pull data into a separate platform, live invoice-to-cash information can now be exposed directly inside the assistants people already use for decision-making, asking a plain-language question and getting a data-backed answer without opening a report or switching tools.

It’s a change in interface, but it reflects the same underlying idea Landy described where the value isn’t in adding an AI layer on top; it’s in placing accurate, structured context where the question is actually being asked.

The Real Productivity Gain Is Time to Think

Much of the AI conversation in finance focuses on headcount, task automation and processing speed. Landy points to a more consequential gain: reducing the time finance teams spend assembling information before they can make a decision.

“If you can save the time people spend assembling information, that is the biggest difference,” he said. “They can spend that time thinking and making better business decisions.”

That shifts the finance function away from producing retrospective reports and toward managing events as they develop.

A late payment from a consistently reliable customer, for example, may appear insignificant in isolation. Connected AI systems can recognize that the payment has broken an established pattern, assess its impact on a cash forecast and alert the appropriate employee before the missed payment becomes a collections problem.

“If you can detect in real time that a payment has not arrived when it typically does, you can react and have a more meaningful conversation before it becomes a problem,” Landy said.

The distinction is subtle but important. AI’s value is not merely that it helps finance teams respond faster. It allows them to intervene earlier, when more options remain available.

Autonomous Finance Needs Boundaries

Not every workflow should move toward full automation at the same speed.

Cash application, invoice matching, aging analysis and fraud flagging are natural candidates because they involve high-volume processes, recognizable patterns and measurable outcomes. The role of the human can be limited to reviewing exceptions.

The calculation changes when a decision involves a strategic customer, a large dispute or a meaningful legal or reputational risk.

“If you have an important relationship, you are not going to want the process automated to the point where no one is available to handle the communication,” Landy said.

The question for CFOs is therefore not whether humans remain involved, but where they sit in the system. Landy separates workflows into those where employees are “in the loop,” those where they remain “on the loop” as supervisors and those that can operate autonomously.

That governance cannot be applied after deployment. It must shape the workflow from the start.

“Anything involving legal, brand or reputational risk should have a human in the loop,” Landy said.

The CFO Becomes a Context Architect

Artificial intelligence also changes the traditional division of labor between finance and IT. CFOs do not need to become machine-learning engineers, but they do need to understand how financial data is stored, shared, secured and introduced into AI systems.

“The number one requirement is a partnership between CFOs and their IT teams to ensure the right infrastructure is being evaluated and addressed,” Landy said.

Over time, that partnership may evolve into a broader role for finance leaders.

“Every business unit owner, including finance leaders, should move toward becoming a context architect for their solutions,” Landy said, “working with human and digital coworkers to deliver meaningful results.”

Watch the full PYMNTS TV interview with Billtrust CTO John Landy to hear more about:

• Why AI cannot repair fragmented finance infrastructure. Landy said bolt-on deployments will struggle when accounts receivable data remains scattered across multiple ERPs, vendor systems, batch processes and spreadsheets.
• How connected AI can move finance from reporting problems to preventing them. The discussion explores how real-time visibility into payment behavior can help teams identify late-payment risks earlier, improve cash forecasting and spend less time assembling information.
• Why autonomous finance still requires deliberate human boundaries. Landy argues that high-volume tasks such as cash application, aging analysis and fraud flagging are strong automation candidates, while credit decisions, major disputes and legal or reputational risks should retain human oversight.

Beginning with this year’s holiday shopping season, PayPal Pay in 4 will be available for consumers in Canada, according to a Monday (Nov. 10) press release.

“PayPal has served Canadians for over 15 years and is one of the most trusted brands across the country,” Michelle Gill, general manager for small businesses and financial services at PayPal, said in the release. “To meet demand for transparent and trusted payment options, PayPal’s Pay in 4 helps Canadians manage cash flow without late fees or hidden costs. Businesses also benefit as those who offer PayPal BNPL offerings experience increased conversion and higher sales—both critical during [the] peak holiday season.”

PayPal’s 2025 Festive Spending Survey showed that 60% of respondents who haven’t used BNPL would be encouraged to try it if there were no fees, the release said. Pay in 4 lets shoppers divide eligible purchases from $30 to $1,500 into four equal, interest-free payments across six weeks.

The expansion comes as consumers turn to BNPL to manage growing economic pressures.

An October column from PYMNTS CEO Karen Webster said BNPL is not about spending sprees, but more about control. Consumers see installment financing as a rational response to an unpredictable economy, a budgeting strategy rather than a symptom of excess.

PYMNTS Intelligence found that consumers increasingly turn to BNPL to manage the timing of payments for groceries, apparel and other basics.

“The transparency of fixed installment plans, with no compounding interest and clear payoff dates, makes them appealing as credit card rates hover near record highs,” PYMNTS reported Friday (Nov. 7).

Earnings from major BNPL players back up that trend. For example, Affirm reported that active consumers and cardholders reached record levels in its latest quarter, while Sezzle surpassed $1 billion in quarterly volume.

“Consumers are not simply adding more debt; they are also searching for flexibility,” PYMNTS reported. “For households caught between inflation and high rates, BNPL functions as a safety valve that helps them preserve cash flow and maintain spending stability.”

That’s according to the bank’s business outlook indicator survey, released Monday (Oct. 20) and showing a slight uptick, though still in negative territory: -2.3 during the third quarter, compared to -2.4 in the prior quarter.

“Firms’ outlooks and intentions remain subdued despite a gradual improvement in sentiment and a slight easing of perceived uncertainty,” the survey said. “Expectations for growth in domestic and export sales remain soft due to concerns about the broad economic effects of trade tensions.”

Business leaders told the central bank they do not expect sales growth to strengthen. The survey includes comments from exporters in the steel and aluminum sectors, which reported “especially weak outlooks” and “significant layoffs due to the tariffs.”

“Although some exports of primary aluminum have been redirected to Europe, these exporters view this strategy as an unsustainable alternative to US market access because of concerns about long-term profitability,” the survey said.

Companies also said they expect cost increases due to tariff and trade-related uncertainty. At the same time, dwindling demand is preventing them from passing costs on to consumers.

Also Monday, a report by Reuters said that global companies expect the impact of tariffs to soften as more countries negotiate new trade deals with the U.S.

Although companies expect their combined cost of tariffs to total between $21 billion and $22.9 billion this year, they expect it to fall to $15 billion next year, the report said, citing Reuters’ analysis of corporate statements, regulatory filings and earnings calls.

Meanwhile, PYMNTS reported last week that many companies have quantified the cost of tariffs, embedded it into forecasts, and reworked supply and sourcing strategies.

Days earlier, Philadelphia Fed President and CEO Anna Paulson had said that tariff-induced price increases have been “somewhat smaller than anticipated” and that the increases thus far are unlikely to leave “a lasting imprint on inflation.”

Paulson added that many businesses had unlocked ways to avoid passing on increased costs to their customers in order to preserve their market share.

The PYMNTS Intelligence report “The Enterprise Reset: Navigating Tariffs, Supply Chain Shifts and Cost Pressures” shows that companies have lowered costs, diversified foreign suppliers, localized sourcing and reworked operations to increase their resilience and stay competitive.

“In dealing with the impact of tariffs, companies have broken away from business as usual by replacing suppliers, redesigning products and leaning into just-in-time inventory models,” PYMNTS wrote.