Treasury Forecasting in an Era of Constant Change
Published: July 09, 2026
Treasury forecasting has entered a new era. In recent years, treasurers have had to navigate a combination of geopolitical uncertainty, shifting trade policies, inflationary pressures, interest rate volatility, and challenging supply chains. Events that once unfolded over months can now impact liquidity positions within days. Yet many treasury teams still rely on forecasting processes built for an old, more stable, world.
As a result, the questions being asked of treasury are changing. Whether the trigger is tariffs, changing customer payment behaviour, or macroeconomic movements, treasury leaders are increasingly addressing forward-looking questions. What happens if capital requirements increase unexpectedly? How much liquidity is required under different scenarios? Can the business absorb a deterioration in collections, a shift in trade routes, or a refinancing event?
With the planning horizon shrinking from quarters to days, forecasting is evolving from a periodic reporting exercise into a strategic capability that supports funding, investment, and risk management decisions across the business. Understanding current cash balances is no longer enough. Treasury teams must assess the financial implications of uncertainty before it materialises, with the objective being not only to estimate future cash positions but to provide greater confidence in the assumptions, risks, and potential outcomes that sit behind them.
The question is no longer whether forecasting is important, but if existing processes are capable of keeping pace with an increasingly dynamic operating environment.
Process, identity, evaluate, understand
This is where advances in AI and ML are beginning to change the treasury landscape. As treasury technology evolves to incorporate these capabilities, it is enabling organisations to improve forecast accuracy, increase agility, and gain greater confidence in their liquidity planning.
Indeed, the value of AI is not simply that it can generate forecasts more quickly. Its real value lies in helping treasury teams process more variables, identify hidden patterns, evaluate a wider range of scenarios, and better understand uncertainty.
ML models can identify customer payment behaviours, recognise seasonal patterns, detect anomalies, and continuously refine assumptions as conditions evolve. They can also support more sophisticated scenario analysis, enabling treasury teams to assess the potential impact of different interest rate environments, changes in capital, or disruptions to expected cash flows.
Rather than presenting a single forecast figure, these capabilities enable treasury teams to understand a range of possible liquidity outcomes and the risks associated with each. The result is not simply greater forecast accuracy, but greater confidence in how the organisation responds when conditions change.
Most importantly, it means treasury professionals are able spend less time producing forecasts and providing more simulations and instead focus more time interpreting them.
Robust data
While AI continues to attract attention, forecasting capabilities remain only as strong as the data that supports them.
Many treasury organisations are not suffering from a lack of data. Most have access to ERP information, banking data, business forecasts, and a growing range of external market intelligence. The challenge lies in transforming that information into meaningful insight.
Disconnected systems, inconsistent data structures, and manual processes continue to undermine forecasting accuracy across many organisations. This is why many treasury teams are increasingly focusing on creating connected treasury environments where data, forecasting, liquidity management, funding, and risk activities operate together.
Four foundations
Technology should be evaluated as part of a broader treasury operating model: visibility remains important, but visibility alone is no longer enough. Leading treasury organisations are increasingly moving towards a model built on four foundations: visibility, connectivity, intelligence, and action.
Visibility provides an understanding of current positions. Connectivity brings together data from banks, ERP systems, business units, and market sources. Intelligence transforms that information into insight through forecasting, analytics, and scenario modelling. Action ensures those insights can be translated quickly into treasury activities and business decisions.
Treasury teams have spent years improving access to cash positions, centralising information, and enhancing reporting. While visibility remains essential, it is increasingly becoming a baseline expectation rather than a differentiator.
The next stage of treasury’s evolution is intelligence. Organisations are increasingly looking beyond what their cash position is today and focusing on what it means for tomorrow, next week, and next month. The ability to understand uncertainty, challenge assumptions, and assess potential outcomes is becoming just as important as the forecast itself.
A new mandate
In this environment, forecasting is becoming far more than a reporting exercise. It is evolving into a strategic capability that helps organisations evaluate risk, allocate capital, preserve liquidity, and respond to change.
Organisations exploring treasury technology must look beyond individual forecasting functionality and consider how forecasting integrates with the broader treasury ecosystem.
In an era of constant change, treasury is evolving its foundations to encompass visibility, connectivity, intelligence, and action, emerging as a strategic advisory centre rather than a traditional service function.