Smart Steps to Establish Rock Solid AR Forecasting

Earlier this year, the International Monetary Fund released its economic forecast for 2023. In it, they stated, “The outlook is uncertain again amid financial sector turmoil, high inflation, ongoing effects of Russia’s invasion of Ukraine, and three years of COVID.”

With an uncertain outlook, accounts receivable professionals must focus on establishing rock solid financial forecasting. This is a vital part of maintaining the financial health of a company and steadying the ship in turbulent times. It impacts the working capital of a business and can greatly reduce reliance on external financing such as loans.

Accounts receivable forecasting: the element of a company’s cash flow forecast that estimates the amount of cash it is due to receive over a set period.

One of the most reliable ways to forecast accounts receivable is using Days Sales Outstanding (DSO).

It starts with a sales forecast

The process of establishing an accurate receivables forecast begins with forecasting your expected sales revenue over a set period.

Sales forecast = Previous month’s revenue + expected growth – expected churn

In practice, that may look like this:

Last month’s revenue was $50,000
The average monthly growth has been 5%
Average monthly churn has been 1%

Your sales forecast equation would be: $50,000 + ($50,000 x 5%) – ($50,000 x 1%) = $52,000.

However, several additional factors should be taken into consideration when creating a sales forecast. Do you anticipate the acquisition of new customers? Have you implemented any price changes? Has the economic outlook changed? Is the market seeing substantial growth or loss? Any of these incidents can impact sales and should be considered as variables when forecasting. And suffice it to say, there are plenty of variables AR professionals must consider today.

Calculate Days Sales Outstanding

After determining your sales forecast, you’ll need to establish your days sales outstanding. It’s one of the most important metrics an accounts receivable team can monitor, as it calculates the average number of days it takes to recover receivables after a sale.

DSO = (Accounts receivable/Net Credit Sales) x number of days

Now, let’s plug in some hypothetical numbers.

Net credit sales are $100,000
Accounts receivable is $70,000 for 50 days

Calculating those numbers would give you: ($70,000/$100,00) x 50 = 35. So, your DSO would be 35 days. As a general rule, anything under 45 is considered a low DSO, meaning that a DSO of 35 would indicate that your customers tend to pay their invoices promptly. Of course, DSO numbers also vary by industry, so it’s important to benchmark your business accordingly.

Adding it all together

After calculating your forecasted sales and DSO, you can forecast your accounts receivable.

Accounts receivable forecast = DSO x (Sales Forecast/Time).

Using the numbers determined above means your receivables forecast equation would look like this: 35 x ($52,000/50) = $36,400. In other words, your company can expect $36,400 incoming for the period being measured.

Factoring in Uncertainty

While the above process provides a reliable picture of the amount of incoming revenue to expect, there are always variables that can impact the actual results. The most obvious challenge that an AR team faces is that just because a customer has agreed to certain payment terms does not mean that they will adhere to them. Late payments and non-payment of invoices are a problem that every AR department faces.

93% of organizations state that they experience late payments

Attempting to account for that possibility requires a historical examination of customer behavior to discover payment trends. This can be a labor-intensive and complicated process for AR departments relying on manual accounts receivable.

Adopting an automation solution that employs artificial intelligence and machine learning can greatly reduce the labor involved, while increasing the accuracy of the assessment. The process is known as predictive analytics — a form of data mining that deals with extracting information from data and using it to predict trends and behavior patterns.

Predictive analytics can be employed on multiple levels. At its most basic, it examines individual accounts and their past payment behavior and assigns them a letter grade based on their propensity to pay on time. Using algorithms, the software also assesses individual invoices, noting if they are likely to be paid by their due date. If they are expected to be overdue, it also estimates how overdue they are likely to run, splitting them into buckets of 1-30 days, 31-60 days, 61-90 days, and 90+ days overdue.

This same approach is then applied to your receivables as a whole and extrapolates how much money you can expect to come in daily. Because the system uses machine learning, it also grows more accurate the longer it is used, incorporating new data in real time.

This information can be leveraged to provide more context for your accounts receivable forecast, helping you better factor in potential uncertainties in your assessment.

Better Data for Better Stability

With a high degree of uncertainty still plaguing the economy, organizations need to take advantage of every opportunity to build better financial stability. Regularly forecasting accounts receivable is an essential part of this process, providing you the ability to make effective business decisions, improve working capital, and reduce reliance on outside financing.

Adopting automation software that facilitates better reporting through the use of AI and machine learning not only reduces the amount of work involved in creating reports. It also allows organizations to better plan for variables in customer behavior.