As an emerging, revolutionary technology, artificial intelligence (AI) is seemingly everywhere.

It's on the front page of the newspaper. It’s in the board room. It’s on Wall Street as well as Main Street. If you’re like most businesspeople, AI is a little bit in your head, too.

The question is: What are you going to do about it?

As a treasury manager, you have a simple choice: Be intimidated by AI — fear the change it’s destined to bring to your department — or embrace all the ways it can make your work more efficient, accurate and secure to maximize your organization's cash flow.

The use of artificial intelligence in treasury has progressed in stages. It moved from rules-based automation to machine learning applications to predictive analytics. Let’s review how AI is impacting treasury management along this continuum and the steps you should take to benefit.

Ways AI can make you more efficient today

Artificial intelligence enables machines to reason, learn and act in ways that would normally require human intelligence or involve data whose scale exceeds what humans can analyze. Doing so can make business more efficient. This is where it’s already paying dividends in treasury management and where experts suggest you start when looking to harness AI.

Caroline Reddington, head of Treasury Management at Cadence Bank, recommends you begin by contemplating potential use cases. “What’s repetitive? Where am I spending a lot of time but not providing value? Those are good places to start,” she says.

Payables and receivables processing and associated reporting have historically required treasury employees to spend considerable time manually performing routine low-value tasks — such as converting paper records to digital formats, flagging discrepancies, generating routine reports and managing exceptions and disputes. Today, AI is already helping automate such tasks.

As an example, AI is using data extracted from multiple departmental and accounting sources and matching it against bank statements and the general ledger to automatically reconcile transactions.

Using basic robotic process automation (RPA) and other AI tools, treasury managers can shift employees’ repetitive and manually intensive tasks to machines and reallocate their time to higher-value analytical work.

“A simple but valuable AI use case is getting rid of macros in Excel spreadsheets and replacing them with RPA connection points,” Reddington says.

Better, more accurate cash forecasting

The emerging AI application attracting the most interest from treasury managers is cash forecasting, “whether it’s in their own shops, or partnering with a bank or fintech,” Reddington says.

Sub-fields of AI, such as machine learning, data mining and advanced statistical modeling, can be combined to produce predictive analytics. AI-supported cash forecasting tools are enabling treasurers to predict cash flows, so they can plan for investments or loans and better manage their cash.

For years, treasury cash forecasting has relied on reports that banks generate for their clients and Excel spreadsheets companies maintain on their own. But now there is a growing number of cash forecasting tools that leverage AI to analyze historical trends and predict cash flows. 

"Leveraging AI in that space is providing better accuracy, and it’s just faster. Accuracy will continue to improve, and longer-term forecasting will keep getting better."

Caroline Reddington

Enhancing payments initiation

Going forward, treasury managers will also be able to harness AI tools to analyze information such as the size of a particular payment, how fast it must be delivered and the payment types the supplier accepts, and then recommend the most appropriate payment type to use.

For example, if Accounts Payable needs to deliver a payment today and the supplier accepts payments via the RTP^® network or some other instant payment method, that instant payment type would be the likely recommendation. If the treasurer has three days to make the payment, the system might suggest an Automated Clearing House (ACH) transaction or some other payment channel.

A weapon against rampant payments fraud

Also expect AI to be at the center of efforts to fight payments fraud, which Reddington says has been “growing like wildfire.”

Business email compromise was the number one avenue for attempted and actual payments fraud in 2024, according to the 2025 AFP Payments Fraud and Control Survey Report. In a BEC attack, criminals use social engineering to compromise email messages so they appear to be from a known source making a legitimate request. A fraudster might send an email to an accounts payable staffer that purports to be from the company’s CFO, directing the staffer to wire a large payment to an account the fraudster controls. A fraudster impersonating a legitimate vendor might email the staffer requesting that account information be changed for the next payment to the vendor.

To combat BEC, artificial intelligence can power real-time transaction monitoring. “Treasury will be able to leverage AI to ask if it’s likely the email came from the legitimate vendor, and to quickly determine if the new bank account and routing numbers are valid,” Reddington explains.

The federal government has already had success using AI to fight fraud. The U.S. Department of Treasury has credited AI-powered tools with helping to prevent and recover over $4 billion in fraudulent and improper payments in fiscal 2024. Recovery of about one-quarter of those funds came from using machine learning — a form of AI — to catch check fraud.

Important steps in your AI journey

Reddington suggests treasury managers embrace AI by taking these steps:

• Start small and build.

  Have your team brainstorm how using AI might help you automate certain time-consuming tasks. Then delve into ways AI might help you improve strategic decision-making. Ask: Where could having better data, better data aggregation or modeling help?

• Understand your organization’s risk appetite.

  Be sure to communicate with the risk team and other stakeholders in your business to ensure any AI tools utilized in treasury don’t expose company data to breaches.

• Evaluate technology investments with AI in mind.

  Consult with treasury technology vendors about their product roadmaps. Before you buy a new ERP or treasury platform, make sure it will accommodate future AI applications.

• Stay tuned.

  The next step in the AI continuum is generative AI — using machine learning models to learn from large amounts of data. Talk to your bankers and other experts to stay educated about AI developments, and keep an eye out for future game-changing treasury applications that generative AI might usher in.

Looking for a competitive advantage?

It's understandable that treasury professionals, like many people in business, get a little nervous when the subject of AI comes up. Could this shooting-star technology, with all its speed, accuracy and efficiency, eliminate treasury jobs?

It's possible. But Reddington recommends a more positive outlook. She suggests that treasury managers view AI as a complementary technology that can help the department excel in its everyday mission. “I don’t think AI is necessarily going to replace corporate treasury teams,” Reddington says. “But those treasury teams that use AI are likely to outpace their competitors.”

This article is provided as a free service to you and is for general informational purposes only. Cadence Bank makes no representations or warranties as to the accuracy, completeness or timeliness of the content in the article. The article is not intended to provide legal, accounting or tax advice and should not be relied upon for such purposes.