How to Improve Short-Term Accounts Receivable Collections Forecasting

No modern business can hope to thrive without the ability to predict the future. This may sound impossible, but forecasting is a skill. It is also a science that artificial intelligence has mastered.

Short-term accounts receivable forecasting is especially difficult because it involves digging beyond general patterns. Regardless of the deadline on the invoice, you must determine when and if clients might pay.

What Is Short-Term Accounts Receivable Collections Forecasting?

“Short-term accounts receivable collections forecasting” refers to the process of projecting payments the company will receive within a short period of time. For example, Company A has $500,000 in unpaid invoices from clients. All of these payments are due within the next two weeks. Company A also has outstanding costs, which amount to $450,000 and are due in 3 weeks. Company A recently bought new equipment and only has $25,000 cash in hand.

At first glance, it may appear that Company A will have enough money to cover the costs. However, Customer B owes $100,000 of that total and has occasionally paid invoices late or in installments. The process by which Company A determines whether clients will pay enough of their invoices so it can cover its own costs is known as short-term forecasting for accounts receivables.

Why Are Short-Term Accounts Payable Projections More Difficult?

Using the example above, determining whether Customer B will pay its invoice within two weeks is a lot more difficult than estimating payment for a month. This is because, with short-term projections, there’s less time to accommodate variables that may affect payment. 

The more interconnected the industry is, the more likely high-impact variables become. Customers might delay because they have not received payments from their customers or because they need to retain extra cash for accounting purposes.

Why Is Forecasting Accounts Receivable So Important?

Imagine what might happen to Company A if it only received $400,000 of the money it was owed. To pay its obligations for the month, it would need to empty its banking account and would still be $25,000 short. If Company A knows this ahead of time, it can plan by reducing other expenses or seeking short-term loans with favorable rates.

Without accurately forecasting accounts payable and receivable, Company A could discover it needs extra money just days before its payments are due. This significantly increases the need to rely on fast financing, which negatively impacts the bottom line.

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What Is The Forecasting Accounts Receivable Formula?

The good news is you can easily forecast accounts receivable using DSO (days sales outstanding):

Accounts Receivable Forecast = DSO x

(Sales Forecast ÷ Days in Forecast)

Where DSO = average accounts receivable ÷ (annual revenue ÷ 365)

You should also note the days in the forecast refers to the time period used for the projection. The sales forecast refers to the expected revenue from sales.

How Can Business Owners Improve Short-Term AR Collections Forecasting?

Improving short-term AR collections is a never-ending process. These are some of the more successful methods business owners have used:

• Analysis: Paying keen attention to industry trends, seasonal trends, and client trends can better determine who will pay in full and when. There is no standard way to accomplish this, so it will take some trial and error.
• Contingency: Planning for the possibility of not receiving on-time payments by introducing variance can help. It can also help collections teams evaluate their own projection performance.
• Organization: Categorizing accounts receivables can make it easier to determine which customers pay more easily and quickly. You can either choose categories based on placement in the supply chain, payment history, or different industries.

Knowing how to forecast cash collections sounds intimidating, but automation can help. Gaviti creates automated AR collection tools that streamline the forecasting and collections process by analyzing data. This can significantly improve the accuracy and speed of predictions. Sign up for our demo to get started.