One of the biggest challenges finance teams face is with accounts receivable forecasting. Sure, every company has payment terms in place, but there’s certainly no guarantee that every single one of your clients or customers is going to stick to them.
At the same time, we all know that short term cash flow issues can hit any business, so it doesn’t make sense to cut an account loose as soon as they’re a day overdue.
Often there’s not even any real consistency with late payments. Customers who have paid on time for years can all of a sudden start falling further and further behind. Others who have always been a little slow can implement a new system, software or payments process and begin to pay everything exactly on time.
That adds up to a lot of question marks around forecasting. But because accounts receivable is such a major component of a company’s financial projections (indeed, the major component), it’s not an area where finance teams can accept inaccuracy.
And while there’s no way to know exactly when every single account is going to be paid, finance teams can improve the accuracy of their forecasts through the use of data and analytics. The right technology can bring together huge amounts of historical data to inform those forward projections, making them as accurate as they can possibly be.
Why Accurate Accounts Receivable Forecasting Matters
Accounts receivable projections are going to make up the bulk of many company’s cash flow forecasts. Whether looking to understand potential future cash reserves, how new R&D or marketing spend might impact net profit, or working through a headcount plan for the coming year, unless you can accurately predict when cash is coming in, you can’t plan how best to spend or invest it.
If your forecasts are based around an average of 30 days sales outstanding (DSO), and the reality is closer to 60, that’s a recipe for a cash crunch.
Even if you have sufficient cash reserves to cover in the short term, using those makes the business vulnerable and causes major problems for risk management.
The more accurate your AR forecasting is, the better you can manage these potential issues. In a broad sense, it doesn’t matter so much if your customers take a while to pay (as long as they do pay).
Where the real problems occur is when there’s a mismatch between a company’s expectations of when they’ll pay, and the reality of when the cash actually arrives.
The Complexities of AR Forecasting
While it’s vital to get AR forecasting right, it’s also difficult to do. It should be simple. Your contract terms say your customers have 30 days to pay, and so your DSO should be around 30 days. But anyone who’s been in finance for a while knows that’s not the reality.
That’s not the only wrinkle in accurate AR forecasting. The more complex a business becomes, the more nuances there will be to their cash flow cycle. Here are a few of the added challenges finance teams need to navigate.
Lack of Control
First and foremost, the time it takes to get paid is totally out of a company’s hands.
You can’t reach in and take the cash you’re owed, and at times the customer might not even have it themselves to pay you.
You can send reminders, follow up directly and even pause services if invoices remain outstanding, but you can’t force a customer to pay by a certain date.
Data Volumes
For many companies, there’s a huge volume of transaction data to sort through. As a company grows, it can be a major undertaking to consistently monitor who has actually paid and who hasn’t.
This isn’t something that doesn’t get done, it’s a fundamental part of the finance function, but it can take substantial time and resources to track and monitor payments.
Multiple Entity Management
All of this complexity is taken up a level when multiple entities are involved. Different entities may have different payment terms, customers with varied expectations or industry norms on how and when to pay.
Some industries will be more susceptible to cashflow problems, which can increase the volatility of your accounts receivable, while others maintain steady cash flow that allows them to consistently pay on time.
How Modern Treasury Tech Helps Reduce AR Uncertainty
The solution to all of these challenges is data and analytics. While finance teams can’t necessarily change the payment patterns of their customers, they can better prepare for them.
This comes down to understanding the historical behavior of your customers, at a granular level. Getting down to that granular level is important, because it’s the only way for your forecasts to maintain accuracy as the business grows and develops.
Accounts Receivable Forecast Example
Consider a multi-national company with multiple entities and business units. Across the entire company, the average DSO is around 45.
When building their financial forecasts, the finance team at the company could easily use this figure to provide an estimate of their future cash flow and cash reserves, and it may be accurate enough in the short term.
But while the overall average DSO is 45, this global figure is made up of a huge number of business units and product lines that have DSOs running from 7 to 90.
Department A, which has an average DSO of 80 due to the standards of the industry it operates in, is experiencing strong demand due to a global shortage in the products it sells.
Department B, which is in the FMCG sector, has a DSO of just 12, but it’s experiencing a significant slowdown in demand due to increased competition.
This trend can quickly make the company’s AR forecast inaccurate, even if the bottom line revenue figure is right on target. Correcting the forecast based on the new global average, without taking into account the underlying business changes, will mean continued problems in AR forecast accuracy.
These complex variables combined with manual processes, and spreadsheet-based treasury operations, makes it no surprise that 37% of CFOs don’t fully trust their organization’s financial data (Blackline).
Rather than taking a simple overview of a company’s financials, modern treasury technology provides the capabilities to forecast with far greater accuracy, pulling the specific DSO from every individual product line, entity, business unit or any other categorization you can think of.
It can allow modeled changes in revenue share from Department A and Department B to flow through to the global forecast, taking into account cash flow impact of resulting accounts receivable time scales.
There are some specific features in Trovata that facilitate this process:
Multi-Bank Data Lake
Too often, treasurers find themselves in the dark when it comes to understanding their organization’s complete financial picture.
Complicated banking infrastructures spread across various regions pose a significant challenge in obtaining the necessary level of cash visibility to build accurate predictive models. Especially if you’re manually collecting data and working from spreadsheets.
Open banking APIs streamline this process and ensure 100% accuracy. API-based cash management software can seamlessly integrate with your company’s bank accounts, regardless of their number, capturing transactional data in real-time.
Open banking facilitates the secure and direct transfer of data from banks to clients. This means no more manual normalization of bank data or wrestling with endless spreadsheets.
Instead, you gain access to near real-time cash balances, transaction details, and payment tracking all in one centralized platform. No more chasing after elusive bank data – open banking brings it all to your fingertips in a unified interface.
APIs serve as the conduit for consolidating and standardizing balance and transaction data into a comprehensive multi-bank data repository, providing you with a single, reliable source for all your banking and cash-related information.
However, merely having a comprehensive view isn’t sufficient for unlocking the deep insights necessary to optimize your cash management and investment strategies. You need solutions that empower you to instantly analyze cash across specific categories and ensure your transaction data is accurate.
Transaction Tagging
Tags are used to effortlessly generate historical reports and become the cash flow line items within the forecasting module.
The ability to categorize transactions with tags is a simple tool that adds serious value to your data analysis, and can take just about any form you can imagine. Some examples are tagging transactions by geographic region, product category, vendor or even account manager.
Tags then allow you to review and forecast your accounts receivable in a very specific way. You can review the data per category, and then populate that detail into your global forecast.
This makes it possible to take specific, granular detail and build those numbers into your overall model, without the need to manually calculate metrics for each category.
AI & Machine Learning
Trovata AI allows users to quickly build forecasts and adjust models through the use of natural language prompts.
For example, let’s consider a company which relies heavily on raw commodities to manufacture their products.
A global supply chain issue, like we’ve seen with the war in Ukraine or even the blockage in the Suez canal, can throw a wrench in the normally smooth operations of a company like this, sending the cost of commodities through the roof.
Using AI, users can quickly adjust their forecasts to take into account this change, taking guidance from the human analyst as to whether this is likely to be a short or a long term change to their input costs.
AI can also be used to help identify issues that have already happened, making it quicker to get to the root of the problem. If the treasury team is noticing that cash reserves are trending down, while net profit remains trending up, running a query through Trovata AI can help surface the problem.
It may be in this instance that DSO has increased by 30 days over the past quarter, suggesting that there are some short term cash flow risks that need to be looked at closely.
However, it’s worth keeping in mind that automation, AI and machine learning and powerful tools for analysts, but they don’t replace them completely.
This ‘controversial’ topic was discussed in detail in a recent episode of Fintech Corner, Trovata CTO Joseph Drambarean and Software Engineer Yannis Katsaros.
As Yannis says in the episode, you can take 10 different people, with the same expertise and using the same dataset, and you’ll get 10 different sets of forecasts.
The nature of financial forecasting is that there is uncertainty, and the different assumptions made around this uncertainty can lead to wildly different outcomes.
The key benefit of using AI is that, as Yannis puts it, “You need to use statistics and probability as a tool and mechanism to help you quantify uncertainty.”
It’s not about using AI as a set and forget, one click financial forecast, but creating an instant foundational starting point for skilled analysts to use their knowledge and experience to build better, more accurate projections.
How Trovata Improves AR Forecasting
Trovata brings together a wide range of features and technology to help improve accounts receivable forecasting. The data lake uses open banking APIs to collate a clean, organized, centralized source of truth for all of your company’s financial data, providing a foundation to conduct analysis and forecasting off a single normalized dataset.
There’s no longer any need to spend hours every week consolidating financial data from various banking portals into a spreadsheet to then update your financial model. The data is fed directly, meaning not only is it available in real time, but it’s also 100% accurate.
A single error in a spreadsheet can throw out an entire forecast, and there’s almost no area where this presents a bigger risk to the business than accounts receivable forecasting. After all, if there’s a significant miscalculation in when you expect to get paid, that can quickly lead to a cash flow crunch.
Not only does Trovata provide access to all of your data in one place, it also allows you to view and analyze that data across your tagged categories and by entity. That multi-entity cash management is particularly useful, giving head office deep insights into the cash position of each of their underlying entities. This allows for far more accurate forecasts and better resource allocation across the company.
In short, Trovata brings finance and treasury management into the modern age, providing a consolidated and sophisticated platform that gives greater insight into your historical performance, and more accurate forecasts of your future performance.
If you’d like to learn more about how Trovata can transform your cash management capabilities, book a demo today!
