The Data Benefits of Treasury Management Systems

Key takeaways:

• Cloud-based TMS adoption is surging. More than 65% of companies use these platforms and the market is projected to reach $18 billion by 2035.
• AI and blockchain are transforming treasury. 74% of treasuries leverage AI for forecasting and risk management.
• Data analytics are being powered by AI and cloud computing. This enables automation, liquidity optimization and faster, more accurate cash flow forecasts.
• Generative AI and predictive analytics are driving the next wave of innovation. Alongside rapid growth in digital payments and embedded finance.

Turning data into treasury power

The forces of disruption over the past five years have transformed how corporations run treasury operations. In the early 2020s, checks were still common for B2B transactions, but adoption of digital payments accelerated rapidly. By 2023, over 65% of companies had adopted cloud-based TMS platforms, and the global TMS market reached USD 6.53 billion in 2026, projected to hit USD 18 billion by 2035.

Companies with automatic accounts payable and accounts receivable processes were well-positioned for this shift. Today, PwC’s 2025 Global Treasury Survey shows AI and blockchain integrations are accelerating adoption, and 74% of treasuries actively use AI for forecasting and risk management.

Not only do managed service platforms provide machine-learning powered tools, they also provide the foundation for the next round of disruption: data analytics powered by AI and cloud computing. These technologies enable treasurers to automate reconciliations, optimize liquidity and deliver faster, more accurate cash flow forecasts.

Now treasury departments are gearing up for the next round of innovation, which includes generative AI, real-time predictive analytics and embedded finance capabilities that help treasurers uncover competitive insights.

“For decades treasurers have been sitting on mountains of data, without a way to access it. Now they can analyze payables and receivables to reveal patterns of customer behavior and business performance to gain a competitive edge,” says Adam Keck, senior vice president and director of product management for Fifth Third Bank.

Filling the data gap

Becoming a data-driven treasury department doesn’t happen overnight, and can take years in some cases, given the complexity of technology and skills required.

Part of the problem, says Keck, is that apart from the largest corporations, many companies don’t typically have the data infrastructure or skilled labor needed to link disparate data sources and rationalize data into meaningful data sets. "Large institutions can build their own data infrastructure enabling technology, and hire the data scientists and so on, but this isn’t often an option for smaller companies.”

Fifth Third is addressing this gap by adding data analytics capabilities to its treasury management services, building on its automated receivable and automated payable tools.

“Our data management system offering already provides powerful tools for automated receivables and automated payables that use machine learning to speed reconciliations and manage liquidity, so adding data analytics to our products is a natural evolution,” says Keck. “We already see all of our clients’ receivables, so we can use our analytics tools to find patterns in their data and offer actionable insights.”

The number of ways data can be utilized are countless. But here are some of the more common reasons treasurers are becoming more data-driven:

Improved real-time cash flow insights, forecasting and stress testing

The number of crises and periods of heightened volatility companies have endured since the 2008 financial crisis has complicated the strategy of basing business decisions on historical data. Risk management now has to be more proactive and forward-thinking. Predictive rather than historical analysis is now being developed by treasury departments, which can access internal and external databases and stress test their access to liquidity under different scenarios. This way the organization can be better prepared for another crisis, thrive after conducting a transformative merger or acquisition, or any other business scenario.

Predictive analysis is replacing historical models. Real-time dashboards and rolling forecasts are now standard, supported by AI-driven scenario planning and ERP/TMS integrations. More timely identification, measurement and understanding of key liquidity drivers are helping corporations manage liquidity.

Spotting payment challenges

Data analytics can make it easier to uncover and resolve discrepancies, such as customers struggling to pay on time, or detect a store that’s starting to see sales drop off.

AI can detect anomalies like late payments or declining sales, triggering automated workflows to address issues quickly.

“Once you’ve detected a problem at the payments or cash flow level, other lines of business can be put into action by having the AI system send automated emails to representatives,” says Keck. “It could be that a store is seeing sales drop because there’s construction in the area. Marketing could then be targeted to that area to boost traffic.”

Finding potential opportunities

The same process can potentially unearth business opportunities. Data analytics can be as broad or as granular as a company needs. “This is just an example, but let’s take a pizza franchise. It could home in on the fact that some neighborhoods generate more orders for pepperoni and cheese on certain days, and you could offer discounts on that pizza item on the other days to boost sales,” says Keck. “The treasury department could also conceivably analyze the data to detect whether the demand in a certain neighborhood warrants another store opening.

What’s next for treasury management?

Generative AI copilots are poised to scale across core functions like forecasting, compliance and fraud detection. Simultaneously, the global digital payments market continues its rapid growth trajectory as businesses embrace more efficient and secure payment ecosystems.

To successfully implement advanced treasury technologies and AI-driven solutions, preparation is essential. This includes ensuring robust data governance, integrating systems for real-time visibility and aligning internal processes to support automation and analytics.

The key to unlocking your treasury data rests in having the computing infrastructure to properly manage data, especially when it is being supplied from a variety of sources within a business. The visibility treasury management services provide can make it easier for professionals to spot trends and tackle pitfalls before they become major financial obstacles—as well as find revenue-driving opportunities ahead of the competition.

To learn more about this and other treasury tools, contact your relationship manager, treasury management officer or find a banker to learn more.