How AI is Reshaping Core Finance Teams: Treasury Management Case Study

Treasury management is a critical function in financial operations. It ensures businesses have adequate liquidity to meet obligations and achieve strategic goals. However, traditional methods of treasury and cash flow forecasting—like spreadsheets or manual reconciliation—often fall short due to:

1. Limited Accuracy

Spreadsheets leave room for error. Minor miscalculations can lead to inaccurate cash flow trends. For companies managing complex global operations, this can become a costly issue.

1. Time and Resource Intensive Processes

Manual treasury/cash management tasks such as transaction categorization consume hours better allocated to higher-value strategic activities. Financial leaders require tools that work smarter, not harder.

1. Lack of Real-Time Visibility

Cash data changes rapidly. Without real-time insights, businesses risk making decisions based on outdated or incomplete information. Fortunately, AI-powered cash and treasury management tools are addressing these challenges head-on

Key Insights from the Panax AI & Automation Survey Report 2025

Panax, a cash management start-up surveyed over 200 treasury and cash management professionals. Their Panax AI & Automation Survey Report 2025 serves as a benchmark for understanding how financial teams are adopting AI to enhance performance. Here are some key takeaways:

A Growing Adoption Rate

The report shows 70% of companies now use AI-driven forecasting tools, underscoring the shift away from legacy systems toward more agile, smart platforms.

Risk Mitigation

AI helps alleviate significant financial risks. The survey findings reveal key challenges:

• 33% of businesses face internal risks ranging from fraud to operational inefficiencies.
• 27% struggle with debt obligations, requiring significant cash outflows.
• 26% experience market liquidity risks, making it difficult to convert assets.

AI tools address these vulnerabilities by automating fraud detection, optimizing cash positions, and offering tailored compliance strategies.

1. Challenges to Overcome

While the benefits of AI are clear, adoption does not come without hurdles. Respondents flagged regulatory compliance (32%), data reliability, and implementation costs as top concerns. Financial leaders must work closely with AI solution providers to ensure ethical, responsible, and rule-e result is a stronger financial position and increased confidence in long-term planning.

Categorization & Forecasting

One area that AI can be particularly beneficial is transaction categorization. Being able to process transaction data swiftly and accurately, significantly reduces the manual effort to classify and tag cash flows. By leveraging machine learning, AI systems identify patterns and anomalies in financial data, allowing for more consistent categorization processes. Additionally, AI learns and adapts over time, refining its accuracy and ensuring that the categorizations align with the organization's financial framework.

In the case of cash forecasting, traditional methods often rely on static models which can leave organizations vulnerable to sudden market shifts. AI, however, integrates dynamic data sources and uses predictive analytics to create more robust and adaptive cash flow forecasts. This capability in theory allows treasury teams to anticipate cash shortages or surpluses with greater accuracy,

Be Warned. Rubbish in, Rubbish Out!

While AI can significantly enhance cash forecasting, it is not a magical one-click solution. The quality of predictions and insights generated by AI is heavily dependent on the quality of the data fed into the system. If inaccurate, incomplete, or outdated data is used, the resulting forecasts will inevitably be flawed—reinforcing the principle of "rubbish in, rubbish out."

Organizations must ensure that their data inputs are clean, consistent, and up-to-date to fully leverage the advantages of AI in cash forecasting.

Not Just Treasury Teams

AI is transforming other core finance teams by streamlining processes, improving accuracy, and enabling smarter decision-making across functions such as accounts payable, accounts receivable, and financial planning and analysis (FP&A).

1. Accounts Payable Automation

AI can streamline invoice processing by automatically extracting and verifying information from invoices, flagging anomalies, and reducing manual effort. For example, Tipalti provides an AI-driven solution that automates the accounts payable workflow, ensuring efficiency and accuracy in payment processing.

1. Accounts Receivable Optimization

By predicting customer payment behaviors and automating reminder emails, AI can enhance cash flow management and minimize overdue payments. A notable example is YayPay, a platform using AI to help finance teams improve collections and payment forecasting.

1. Expense Management

AI can classify expenses, detect duplicate entries, and identify potential fraud, making expense reporting faster and more secure. Expensify leverages AI for real-time expense reporting and fraud detection, saving valuable time for finance teams.

1. Forecasting and Budgeting

AI improves the accuracy of financial forecasting by analysing large datasets, identifying trends, and providing insights that enhance budget planning. Companies like Anaplan offer AI-powered forecasting tools that enable FP&A teams to create more precise and adaptable financial plans.

The Future of AI in Financial Technology

Looking ahead, AI will continue to unlock opportunities for innovation in finance. Here’s what financial leaders can expect in the coming years:

1. More Machine Learning in Financial Forecasting

Machine learning models will become increasingly sophisticated, capable of learning from new data streams and accurately predicting complex financial scenarios.

1. Hyper-Personalization in Financial Services

AI will refine how financial institutions serve their clients by creating tailored services based on individual usage patterns and preferences.

1. Seamless Integration Across All Functions

AI platforms like Panax are already integrating with ERPs, accounting systems, and cash platforms. Expect an even greater transition towards unified financial ecosystems that eliminate silos altogether.

Financial leaders that start preparing now will set themselves apart as trailblazers in the next era of financial technology.

Why AI is No Longer Optional for Financial Leaders

The financial landscape of 2025 demands decisive action. AI is no longer just a nice-to-have but an essential lever for success in treasury management and beyond.

Key takeaways for financial executives include the following:

• AI improves operational efficiency, reduces risks, and enhances compliance.
• Treasury functions, thanks to AI, are evolving into hubs of strategic insight and control.
• Adoption of AI isn’t just about staying competitive—it’s about setting the industry benchmark for adaptability.