When we talk about AI in finance, one of the hottest topics is its potential to improve cash forecasting - and for a good reason. Cash forecasting is a thorn in the side of so many companies, even those with sophisticated finance functions.  The traditional models still require constant manual tweaking, and they often fall short of delivering timely insights.

Picture this: your finance team kicks off every quarter by building a detailed cash forecast., Over the next weeks they revise it repeatedly to reflect new information but rarely have the time bandwidth to circle back and validate if their initial predictions were right. By the time month three rolls around, those carefully constructed models bear little resemblance to reality. 

Sounds familiar? Well, the good news is that AI can help. But it’s not a silver bullet, and that’s important to be realistic about upfront.

Where AI can add value

Here’s what the right AI solution -certainly not every product on the market - can genuinely deliver in 2025:

• Real-time forecasts. Traditional cash forecasting tools work with static snapshots of data, often leading to delays in making strategic decisions. AI removed this lag by continuously analyzing transaction data in real-time, and dynamically updating forecasts as cash inflows and outflows happen. In traditional cash forecasting a model is often built at the start of a period and updated manually as new data comes in. AI eliminates this lag by continuously analysing real-time transaction data. For instance, as payments are received or made, AI adjusts the forecast dynamically, ensuring it reflects the latest cash position.

In practice, this means no more waiting until the end of the week or month to reconcile figures. AI ensures that your cash flow picture is always current, giving you the agility to make decisions on the fly.

• Pattern recognition. AI  excels at identifying trends and patterns in transaction data. Unlike traditional rule-based systems that rely on rigid, predefined criteria - think ‘if this, then that’ statements - AI uses advanced data clustering techniques to spot natural patterns in your cash flow data. For example, if certain payments are consistently delayed or if cash inflows peak during specific times of the month, AI can incorporate these patterns into the forecast. This results in a more nuanced and accurate prediction than the static models can provide.

• Anomaly detection. Beyond the actual forecast itself, AI can also flag potential issues, since it watches transactions 24/7. If a line item appears out of the ordinary or if cash outflows deviate significantly from historical norms, for example, AI can alert the finance team. It will also explain why each anomaly looks suspicious based on your company’s specific patterns. This proactive approach helps teams address problems before they escalate, whether it’s a missed payment or an unexpected expense. 
• Automation of manual tasks. As anyone who has ever worked in a treasury role will know, reconciling transactions and updating forecasts manually is time-consuming and error-prone. It can also be pretty soul-destroying! AI can help to automate these processes, categorizing hundreds of transactions in real-time and updating the forecast accordingly. In growing companies,  where treasury teams are often stretched thin, this automation frees valuable time for strategic initiatives. Instead of spending hours categorizing transactions and reconciling numbers, finance professionals can focus on higher value tasks like scenario planning and risk management. 

Where AI still struggles

While AI offers significant advantages, it’s important to understand its current limitations. Here’s where AI can fall short:

• ‍Navigate missing data (without enrichment). To work properly, AI needs clean, consistent information. If your bank connections are patchy or your systems don’t talk to each other, even the smartest AI will struggle. That’s why basic connectivity is so important to get right before adding any fancy features. It’s also where data enrichment can add huge value, linking disparate data points, filling in missing details, and providing context – essentially transforming raw, unstructured information into actionable insights (read more on this in our recent blog).

• Handle the unprecedented.. AI’s predictive capabilities are only as strong as the historical data it learns from. While it is highly effective at identifying patterns and trends, it struggles with unprecedented events – that fall outside the scope of its training data. For example, an unexpected economic downturn or a sudden regulatory change could impact cash flow in ways that AI can’t anticipate. In these instances, it still needs human guidance. The system improves by learning from how professionals handle these novel situations.

• Replace human judgment. Without doubt, AI can suggest actions and highlight risks or opportunities, but the final responsibility for key strategic decisions rests with humans. AI serves as a powerful tool to augment decision-making by providing insights and recommendations, but it is up to finance professionals to interpret those AI’s insights, align cash flow strategies with broader business goals, and prioritize actions accordingly.

Leave Rearview Forecasting Behind: The road ahead is AI

Most finance teams wouldn’t dream of going back to calculators and paper ledgers. Soon, they'll likely feel about manual forecasting as equally outdated. 

By combining human insight with AI’s ability to process patterns, spot anomalies, and update in real-time, we’re not just improving an old process – we're redefining what’s possible in cash forecasting. In short, it’s time to start believing in AI, and more importantly, to start deploying it.

Ready to see how AI can revolutionize your forecasting and turn it into a smarter, more proactive process? Get in touch with the Statement team and we’ll happily share our insights and show you the magic of our AI tools.

‍