%20copy%201.svg)
%205.png)
.png)
Bectran Product Team
I
December 17, 2025
7 minutes to read
Most cash flow forecasts are built on optimism. You run a report in your ERP, look at the open accounts receivable, apply the due dates, and generate a number.
The math is simple: Invoice Amount + Due Date = Cash Inflow.
That math rarely survives contact with reality. The spreadsheet assumes that a due date is a commitment. For a specific segment of your customer base, a due date is merely a suggestion.
Your ERP knows the terms are Net 30. It does not know that the customer has paid on day 45 for the last three years. It does not know that the customer is currently disputing a line item to delay payment. It does not know that you are debating whether to even ship their next order because they have broken too many promises.
This gap between contracted terms and actual behavior is where forecasts fall apart. You have to stop forecasting based on what should happen and start forecasting based on what is likely to happen.
There is a difference between a customer who pays late due to a one-time operational glitch and a customer who pays late as a matter of policy. The latter group requires a completely different forecasting logic. Credit teams see this constantly with collections strategy. If they're repeat offenders, you don't want to reopen that account. The risk is too high.
This decision point impacts your cash forecast. If a customer is a repeat offender, the question is: should we even be doing business with them, and if we stop, how does that change our inflow projections?
When you keep a repeat offender in your standard forecast bucket, two things happen. You inflate short-term cash expectations by projecting cash arriving next week that historically won't arrive for another month. And you obscure true risk by blending high-risk accounts with standard payers.
Most standard reporting tools treat all open invoices equally until they are written off. Until you actively intervene, the system assumes the repeat offender is just like your best customer, only slightly behind schedule.
Your ERP relies on static fields: Invoice Date and Payment Terms. These are contractual absolutes. Payment behavior is fluid. A customer might have Net 30 terms but an Average Days to Pay (ADP) of 52. If your forecast uses Net 30, you are consistently 22 days wrong on every single invoice for that customer. Multiply that across fifty high-risk accounts, and your monthly forecast could be off by hundreds of thousands of dollars.
In many systems, once an invoice is paid (even if it was paid 90 days late), the account returns to a "clean" status in terms of credit availability. The system does not remember the pain it took to collect that money. It simply sees Credit Limit Available > Order Amount. This leads to reopening accounts that should remain closed. When you reopen a high-risk account without adjusting the forecast, you are voluntarily stepping back into a prediction you know is wrong.
The specific risk profile of a repeat offender lives in the head of a collector, not in the data field used by the Treasurer or Credit Manager. The collector knows that Company X always waits for the second demand letter. The forecaster, looking only at the aging bucket, sees "Current" or "1-30 Days Past Due" and assumes standard probability. This disconnect between the qualitative knowledge of the collections team and the quantitative output of the forecast creates a persistent variance in cash reporting.
You fix this by adjusting your model to reflect actual behavior. Move from a Contract-Based Forecast to a Behavior-Based Forecast.
Most teams segment by balance size (identifying top 10 debtors). While important, this misses the risk factor. You must segment by predictability. Divide your open AR into three specific buckets:
The Reliable Core: Customers who pay within terms or within a standard grace period (less than 5 days late).
The Consistent Laggards: Customers who pay, but always late. They are not necessarily bad debt risks, but they ignore terms. They are predictable in their lateness.
The Repeat Offenders (High Risk): Customers with erratic payment patterns, broken promises, or recent placement with agencies.
For the Repeat Offenders, you need a specific protocol. These are accounts where you might choose not to reopen the relationship. Your forecast needs to reflect two scenarios:
Scenario A: The Account is Active but Erratic
If you continue to ship to them, do not use the Due Date. Do not even use the Average Days to Pay, because their behavior is worsening. Manually push their expected cash date to the conservative edge of their historical range. If they have paid between 45 and 70 days in the past, forecast for day 75. It is better to be pleasantly surprised by early cash than disappointed by a shortfall.
Scenario B: The Stop-Ship Decision
If the decision is made to close the account or place it on strict hold, you must immediately adjust the revenue side of your forecast. While this benefits the AR forecast (stopping the bleeding), it impacts the sales forecast. Credit Managers must communicate this downstream impact immediately. If you cut off a repeat offender, the projected sales for Q4 drop. If Finance is still forecasting that revenue, the cash flow model breaks.
For your highest-risk bucket, apply a probability discount to the cash value. If a repeat offender owes $50,000, and they are currently 60 days past due with broken promises, counting that as $50,000 in next month's cash is reckless.
Create a rule for your spreadsheet:
This provides a "Safe Cash" number to your CFO (the amount you are virtually certain will arrive) separate from the "Target Cash" number.
The most difficult part of managing repeat offenders is the discipline. Every time you reopen a high-risk account without securing a deposit or changing terms, you reintroduce volatility into your cash flow.
Best Practice: The Probationary Period
If a repeat offender clears their balance and wants to order again, do not immediately restore their original credit limit and terms.
By controlling the reentry of these customers, you stabilize the input side of your forecast. You stop feeding bad data into the machine.
Accurate forecasting is the primary way a Credit Manager builds credibility with the C-Suite. When you can say, "Our system shows $1M coming in next week, but I am adjusting that to $850k because $150k sits with repeat offenders who historically pay late," you shift from being a data reporter to a risk manager.
By identifying repeat offenders early and excluding them from optimistic forecasts, you give Treasury a realistic view of working capital. This prevents the company from making expenditure commitments based on cash that won't be there.
Telling Sales that a customer is cut off is difficult. It is easier when you can show the negative impact that customer has on cash flow predictability. When you treat repeat offenders as a distinct category, you can quantify the cost of doing business with them—not just in bad debt, but in the unpredictability they introduce to the business.
When you separate repeat offenders from the standard workflow, your collectors stop chasing them with standard reminders (which are ignored) and switch to escalation tactics immediately. This improves the efficiency of your team, allowing them to focus on the "Consistent Laggards" who might actually pay if nudged.
Forecasting cash flow is about adjusting ERP data based on what you know about your customers. Repeat offenders are volatility generators. Your forecast must respect that volatility.
To tighten your cash flow predictions this month:
The goal is to have a forecast that doesn't lie to you about the risks you are carrying. When you stop counting on repeat offenders to act like model customers, you start seeing your cash position clearly.
Stop forecasting with due dates that repeat offenders ignore. Bectran's portfolio monitoring tracks Average Days to Pay alongside payment terms, automatically segmenting customers by payment behavior so you can build risk-adjusted forecasts based on what customers actually do, not what they contractually should do. See how portfolio insights work.
300+ tools for efficiency and risk management
.svg){kind=icon}
