Forecasting Accounts Receivable: Why Due Dates Alone Don’t Cut It

For many finance teams, Accounts Receivable (AR) is one of the most challenging parts of working capital to forecast accurately. While it’s easy to default to invoice due dates when building an AR forecast, many customers see due dates as a “suggestion” not a commitment. Meaning a due date-based forecast can create a misleading picture—one that can ripple through your entire liquidity model.

The reality? Customers rarely pay exactly on the due date. And if your AR forecast doesn’t reflect actual payment behavior, you risk overestimating cash inflows, underestimating liquidity gaps, and making decisions based on flawed assumptions.

Here’s how to forecast AR more effectively—by modeling how customers actually behave, not how your systems assume they’ll behave.

Why Relying on Due Dates Creates Forecasting Risk

Most ERP and billing systems make it easy to export open receivables and their due dates. But this convenience masks a real problem: you’re forecasting cash based on hope, not history.

Using due dates assumes that:

• Every customer pays on time
• All disputes have been resolved
• Collections are smooth and predictable

In reality, B2B customers pay an average of 6–10 days past due, depending on industry, economic cycle, and even company culture. In some sectors (like construction or industrial services), 15–30 days late is the norm.

If you build your cash forecast around due dates, you’ll likely:

• Overstate near-term cash inflows
• Miss early signs of risk from slowing collections
• Risk ending in an unfavorable liquidity situation

What to Model Instead: Outstanding AR Behavior

To improve AR forecasting, shift your approach from invoice-based timing to behavior-based modeling. That means building your forecast around when cash is actually received—not just when it’s due.

1. Use Historical Collection Patterns

Start with 6–12 months of AR data. For each customer (or group), calculate:

• Weighted Average Days to Pay (WDTP): Actual number of days from invoice to payment
• Collection curves: What percentage of AR is paid in 0–30, 31–60, 61–90+ days
• Variance from terms: How payment timing differs from contractual terms

This helps establish a realistic, data-driven baseline.

2. Build Aging-Based Cash Inflow Forecasts

Replace due-date logic with an aging-based forecast:

• If you have $500K in the 0–30 bucket and typically collect 80% within a month, forecast $400K in receipts
• For 31–60, apply a lower collection percentage and longer lag

This approach reflects how receivables are actually converted to cash—and avoids inflating your inflow assumptions.

3. Incorporate “Expected Payment Dates”

Your team should be modeling customer level behavior and patterns. Many variances can be due to customer policies (e.g. Customer A might not mail the check until the due date), using analytical tools you can model this and use it to refine your forecast.

Collections isn’t just back-office — it’s an input to your liquidity model.

Segment Your Customers: One Size Doesn’t Fit All

Different customer types have different behaviors. To improve accuracy, segment your AR forecast by:

• Customer tier (enterprise vs. SMB)
• Industry (healthcare, SaaS, manufacturing, etc.)
• Payment history (on-time, habitually late, in dispute)

Apply different aging curves and write-off buffers to each group. This prevents your forecast from being skewed by a few slow payers or overly optimistic assumptions.

Account for Disputes and Write-Off Risk

Not all open AR is collectible. Make sure you factor in:

• Disputed invoices that may take weeks or months to resolve
• Expected write-offs based on historical bad debt

This helps avoid the common trap of forecasting based on gross AR, instead of net collectible cash.

Keep It Fresh: AR Forecasting Is Not a One-Time Task

Customer behavior changes—especially during economic shifts, ownership changes, or seasonal cycles. That’s why leading finance teams refresh AR forecasts weekly, and tie them directly into broader cash and liquidity models.

If you’re still relying on spreadsheets and manual updates, this can become a huge lift. That’s where tools like Pegasus Insights make a difference—automating AR aging pulls, applying behavioral payment curves, and refreshing forecasts in real time.

Review Material Vendors

Often businesses find that 80% of their collections come from 20% of their customers. Review your forecast for material invoices. Using historical trends greatly improves your forecast, but if a couple of invoices make up a significant amount of your forecast, it is worth verifying them manually. Materiality varies by company, but it is worth checking with the AR manager to verify the expected payments of your largest invoices in the short term.

There are Consequences of Getting it Wrong. Poor Visibility into AR Can:

• Cause unexpected short-term borrowing or credit line draws
• Delay payments to vendors and penalties potentially causing business disruption
• Undermine EBITDA if collections start slipping quietly
• Covenant breaches
• Reputation risk

Confidence in AR Forecasting Enables:

• More confident liquidity planning allowing businesses to invest with confidence
• Smoother treasury and payables alignment
• Better cash control at quarter-end or during fundraising
• Properly captured synergies and incentives
• Confidence that you’re comfortably in the guidelines of agreements

Best Practices Summary

• Don’t use invoice due dates to forecast AR — model based on actual behavior
• Use AR aging buckets and apply collection percentages from historical data (effective DSO to include overdue AR)
• Segment customers by industry or business line and payment patterns for more precise modeling
• Incorporate expected payment dates from collections teams when available
• Refresh forecasts frequently and tie them into your liquidity model
• Use technology to automate and scale the process as your AR portfolio grows

Final Thoughts

Sounds complicated? It is, and getting it right is essential for growing businesses.

Forecasting AR accurately is one of the most powerful levers finance teams have to take control of cash. But it requires a shift in thinking—from static schedules to dynamic behavior. Pegasus forecasting tools were designed with this in mind: Pegasus uses DSO rolls, key invoice timing, and models customer behavior seamlessly.

By modeling how customers actually pay—and using tools to continuously update those insights—you can turn AR from a reporting lag into a real forecasting advantage.

And if your team is still juggling spreadsheets to make it happen, Pegasus Insights can help you move faster, stay aligned, and make your forecast something you can count on.