Manage cash flow

AI offers the most benefit to treasury managers in data-heavy tasks such as cash forecasting, reconciliation, and payment processing. AI may help improve efficiency and analytical precision when supported by appropriate data quality and controls. It can flag errors and discrepancies, allowing treasury teams to find and correct issues.

“Cash forecasting may become more reliable when supported by high-quality, well-governed data, which can translate into better decisions, not just better reporting,” Senuta says.

That distinction is important: While faster reporting is valuable, the quality of information matters more. When treasury teams have clean and current data flowing into their tools, they can make more informed cash management decisions.

AI’s instant access to information about liquidity and payment optimization may provide enhanced visibility into projected liquidity positions. Instead of reacting to problems when they arise, treasury can better identify and evaluate potential liquidity risks before they occur.

“A treasury team could receive an AI-generated notification early in the day that their operating account is likely to fall below a liquidity threshold later in the day,” Senuta says. “That gives the team the opportunity to prevent last-minute funding scrambles.”

Such alerts might relate to current-day balances, expected cash inflows, payment timing, prior transaction patterns, and established account thresholds.

With that early warning, cash managers could:

• Resequence non-urgent payments.
• Move funds between accounts before cut-off times.
• Avoid last-minute, short-term borrowing.

When timing affects risk and return, advanced insight may help support more informed operational and liquidity management decisions.

AI can help treasury become more strategic by changing how work is divided between systems and people, says Elliot Cheek, Truist’s head of liquidity. Historically, treasury staff have been responsible for repetitive work such as payment matching and tracking down invoices, as well as checking for errors.

“It’s very challenging to be the doer as well as the reviewer,” Cheek notes. But AI can be far more efficient at data capture and entry, allowing humans to focus on tasks such as exception management and collections. “AI may enable treasury professionals to focus more time on strategic activities and decision support.”

Certain AI solutions may be more adaptable than traditional rule-based tools depending on the use case and implementation. Spreadsheet models and rule-based tools are often limited to doing what they’re told. But AI can learn from changing behavior over time, making it possible to spot timing patterns and payment anomalies that can be easy to miss in a static process.

That advantage offers particular value for payments and fraud mitigation. AI models can learn what “normal” transaction behavior looks like in terms of timing, counterparties, and payment rails, then flag exceptions before payments are released or settled. That may help reduce the likelihood of losses, exceptions, and manual rework.

The key to a successful AI project is to start with focused, measurable uses and resist the urge to pursue AI for its own sake, Cheek says.

“Form a hypothesis, define criteria, and make it measurable and quantitative,” Cheek suggests. “Otherwise, it’s very hard to measure success.”

Every project will be different, and Truist payments specialists can help you customize a plan for using AI in treasury. But you’ll generally:

• Define a specific use case, such as reconciliation or forecasting.
• Ensure data used is accurate and relevant.
• Set measurable success metrics.
• Test and refine the process before scaling.

Implementation can also require complex data integrations across systems; ongoing coordination among finance, technology, and other departments; and leadership buy-in to manage the change for their teams.

More data is not always better, Cheek adds. Consistent, structured information that reflects actual cash behavior is more valuable than complexity without a clear benefit.

To make the most of AI, track improvements and results. A company might target better forecast accuracy or fewer payment exceptions, for example. Starting with one application that can show measurable results helps organizations build confidence and learn what is required to scale.

AI can support critical decision-making, but humans still need to make those decisions. The stakes are too high for blind automation, says Senuta. And no matter how advanced AI becomes, company leadership should ensure payments land on time and cash is available when needed.

“If an outcome is hard to reverse or high impact, you have to keep a human in the loop,” Senuta says. “AI can recommend and prioritize, but people should be the ones approving actions.”

That principle gives treasury teams a framework for responsible adoption. Use AI to surface information, prioritize options, and handle repetitive work. Keep human oversight when financial, operational, or reputational risk is high. In practice, that means establishing clear approval authority, model validation, and audit trails so teams understand AI insights and who should act on them.

As AI capabilities evolve, treasury teams that establish strong data foundations and governance today will be better positioned to scale more advanced use cases tomorrow.