Cash Flow Forecasting: 7 Data Points that Drive Accuracy and Predictability

Forecasting cash flow is hard – 3x harder than forecasting revenue, according to EY. You can predict revenue right down to the dollar, but all that doesn’t matter until the money hits your account. There are a lot of moving parts that make it hard to predict when cash will show up, and that irregularity makes it hard to plan financing, investing and… well, everything a business needs to stabilize and grow.

The way you’re forecasting cash flow today could be hurting you more than helping you. Advanced analytics and AI advancements have made it possible to predict cash flow with a level of accuracy that wasn’t possible before, and in today’s economy, it’s crucial that you take every necessary step to tap into predictive cash flow intelligence. Let’s dive in.

More Data = More Accurate Forecast

The more data you bring in, the sharper your forecast gets. The problem? Sprawling systems equate to a data disconnect.

Take the ERP system for example. As far as it’s concerned, the job is done when an invoice gets logged. ERPs weren’t built to generate working capital intelligence. They don’t know when a buyer suddenly stops paying on time, let alone what that delay means for cash timing. And the real issue is this: every other system and process is operating like everything went according to plan — when maybe it didn’t.

The quality of data is also a common concern. Forecasting makes many assumptions — standard timing based on when the invoice should’ve been received, not when it was actually received. Now, multiply that across hundreds or thousands of invoices, and you can see how things go awry. Even if the ERP is connected to an accounts receivable platform, that doesn’t mean systems are sharing data in real-time for a truly accurate forecast. Systems can update at different times, reflecting different versions of reality.

The Solution: Everything in One Place

The fix isn’t complicated – at least, not with the right accounts receivable solution. It’s just about bringing everything together. When you unify your AR ecosystem into one platform, your data finally connects because all data lives in one place. A clear view of what’s happening across the entire O2C cycle makes it easier to see how each activity impacts the state of future cash flow.

Another important point: it’s not just about what’s happening inside the business. External data is just as insightful. Macro factors, credit data, industry benchmarks…this is all vital context for understanding what affects payment timing and how to improve it so cash can hit faster and more predictably. Your AR platform should be able to pull this in with the intelligence to connect it all, so you can understand what’s happening and make decisions from a position of strength.

Using AI to Monitor Data and Adjust Your Cash Flow Forecast Daily

It’s easy to see how legacy forecasting approaches don’t work anymore. They’re based on averages like Days Sales Outstanding metrics and historical run rates. By the time the treasury group sees it, the data is already outdated.

With AI as a real-time data cruncher, the cash forecast can be updatedautomatically as new information comes in. Instead of updating your forecast once a month or once a quarter, it’s updating daily and flagging anything that significantly increases or decreases working capital. For instance, with Billtrust, you can see which customers are driving changes in your cash flow, and how those changes will impact your cash availability over the next 13 weeks. That’s a lot of foresight! See what Cash Forecasting looks like in the Billtrust platform with this open demo.

Leading AR platforms also factor in a variety of buyer behaviors to continuously adjust the forecast and flag potential risk. Unlike revenue prediction, which is typically based off more concrete factors like signed contracts and purchase orders, cash flow predictions essentially come down to predicting the likelihood of payment behavior based on current activities and historical trends. A lapsed payment, a shift in payment method, a new pattern of slowing remittances – by the time any of that data shows up in a financial report, the window to respond or shift financial investment strategies is long gone. With software providing the right insight, this doesn’t happen.

What Should Alter Your Cash Forecast? These 7 Data Points Drive Accuracy and Predictability

Here are the buyer activities your accounts receivable automation system should continuously monitor to keep forecasting accurate.

• Days to Pay (DTP): You can’t forecast future available cash based off what DTP was last week or month. You’ll want daily metrics and reports.
• Autopay opt-ins and opt-outs: Opt-ins signal financial strength. Customers can afford to put payments on autopilot, great! Opt-outs suggest trouble ahead, which your forecast needs to reflect. Supplier-managed autopay lowers this risk: you enroll the buyer, and they stay enrolled unless you approve of a status change.
• Changes in payment methods: Each payment method tells you something about buyer behavior, especially changes in how they pay. Your platform should be built to monitor those signals and adjust forecasting accordingly.
• Historical payment patterns: That customer who historically pays 10 days late every month? Not ideal, but highly predictable – allowing you to adjust your late payment reminders to the 11th or 12th day for better efficiency. The real risk is the buyer who starts slipping. When behavior changes from the norm, that’s where your forecast should respond and where your AR team’s focus should be.
• Delinquencies and aging invoices: You can’t let aging data be the only data feeding the forecast. By the time delinquency hits, you’ve already lost weeks of visibility and made financial decisions based on inaccurate data. AI should monitor situations and send alerts warning you of new behaviors and continuing trends.
• Dispute trends: One dispute? It happens. A pattern of disputes? That’s a problem, and your forecast should show it. More importantly, your AR system should clue you in on how to fix what’s causing that friction in the customer experience.
• External data: As mentioned, it’s not just about what your customers are doing with you but what’s happening around them. If similar companies are slowing payments or a customer’s credit profile is shifting, that context matters and should influence the way you manage credit allocations – not to mention the forecast.

Improve Cash Flow Predictability by Influencing Buyers

Influence buyer behavior, and you’ll influence payment timing — which in turn improves cash flow predictability and forecasting. But how do you change the way your buyers behave? Research shows that customer payments are usually swayed by multiple touchpoints, and study findings reveal which touchpoints work best for which customers. The following list includes real data insights from 13 million buyers and $1 trillion in annual transactions that you can leverage to get your customers to pay more consistently and pay earlier too:

• Email and text reminders are most effective when sent starting 30 days before the due date, so don’t be afraid to start early.
• It’s best to wait 5 days between each payment reminder, otherwise you’re over-contacting customers and risking the relationship.
• When should you use a phone call? Place it the day before or on the due date — any earlier, and it’ll likely get ignored or forgotten because urgency hasn’t peaked yet.

Know What Gets in the Way of Cash

Your own processes or oversights could be getting in the way of cash – eek! Sometimes it’s something small, like sending an invoice via email when the customer requires a portal. Missing a field. Forgetting an attachment. You might not even realize operational hangups are obscuring your view. A lot of financial managers accept friction as part of the work, but that friction adds up.

Payment data analytics help you see what gets in the way of cash availability: what issues customers flag most frequently, which invoices take longer to pay when they come through a certain channel. Those trends tell you where things are breaking down – and once you can see them, you can fix them.

A True Story: Using Payment Analytics to Prevent AR Delinquency

For example, one Billtrust customer studied their buyer payment analytics and found that they needed to make dispute processes more accessible. Insight into customer behavior revealed that the AR team needed to make it easier for recipients to initiate a dispute, so AR leaders pulled that verbiage to the top of their payment reminders. This helped flush out dispute issues faster, which meant the AR team could address them immediately before small issues grew into delinquent invoices.

AI-powered AR software makes payment analytics visible at scale, so you’re not relying on gut feelings or one-off observations to make cash flow improvements. You can actually witness how customers behave and adjust your processes to optimize working capital.

The Best Forecasters Know How to Train AI

Agentic AI is the muscle behind much of what we’ve discussed so far: constantly monitoring AR data, analyzing payment activity to flag cash flow disruptions for follow-up, and suggesting next steps to accelerate remittance times.

We won’t get into the nitty gritty of how agentic AI works in AR – you can find that in this guide. At the end of the day, what you need to understand is the rationale behind how AI analyzes buyer behavior, weighs risk, and makes decisions or suggestions. Those are questions you need to bring to your AR software provider.

We also need to talk about training. Agentic AI is exciting because it significantly frees up time, but teams should be reallocating at least some of those productivity gains to training. AI can constantly watch what’s happening and suggest (or even make) improvements, but humans still need to review it, tweak it, and intercept when necessary to make sure it’s driving desired outcomes.

This is what Werner, full-service distributor and solutions partner, does. Billtrust helped their AR team save $80K+ annually in operational costs, but they diligently teach their cash application AI model how to improve.

The bottom line is that agentic AI is not a magic cure-all. Reaching the highest levels of automation depend largely on the features that allow you to coach and customize the AI’s decisioning model. If you want to explore this and more AI features in AR solutions, here’s a helpful buyer’s guide.

Payment Behavior: The Secret to Cash Forecasting

By monitoring buyer behavior, your treasury team will know exactly what cash flow is coming before the month-end report – and more importantly, what they can do to influence the outcomes they want to see instead. Ask yourself (and be honest): is your current forecasting approach holding you back? Imagine being able to see your cash position three months out and actually trust it. Billtrust can help you with that break through.