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Accounts Receivable

Predict Late-Paying Invoices with Business Central Late Payment Prediction

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What the source describes

Microsoft Learn documents the Business Central Late Payment Prediction extension, which predicts whether posted sales invoices will be paid late so you can adjust payment terms or methods. Once enabled, a Payments Predicted to be Late tile opens Customer Ledger Entries with Late Payment and Prediction Confidence columns, and the model trains on invoice amounts, payment terms, credit memos and aggregated customer payment history.

Result or outcome

According to the source, predictions are labeled High confidence at 90% or more, Medium between 80% and 90%, and Low below 80%, and Business Central will not let you enable the extension manually if the model quality is too low.

How to run it

  1. Enable the Late Payment Prediction extension from the notification on a posted sales document, or through the Late Payment Prediction Setup page.
  2. On the setup page, check the model quality and set a minimum quality threshold you are comfortable relying on.
  3. Open the Payments Predicted to be Late tile to view Customer Ledger Entries with Late Payment and Prediction Confidence columns.
  4. Use the Customer Details FactBox to review each flagged customer, or run the prompt on this page against an exported open invoice list.
  5. Act on high-confidence predictions with early reminders, calls or revised payment terms for future orders.

Try it yourself

Copyable prompt

I want to spot which open invoices for [business name] are most likely to be paid late. Here is my open invoice list with customer, invoice amount, invoice date, due date and payment terms, plus each customer's history of past invoices with days paid late: [paste data]. For each open invoice, estimate a late-payment risk of high, medium or low and give the one or two facts from the history that drive the rating. Do not invent data. Then suggest a specific action for each high-risk invoice, such as an early reminder, a call, or changing terms for future orders. Return a table sorted by risk.

Replace the [bracketed] placeholders. Remove client names, account numbers, and personal data before pasting into any AI tool.

What to review

Weigh the Prediction Confidence before acting, and add the hidden Prediction Confidence % column if you need the exact figure. If the standard model does not fit the business, predictions may be weak until enough paid and late invoices exist to train My Model. Confirm any change to payment terms or customer blocking with the business owner first.

Related free download: 50 AI Prompts for Bookkeepers (Prompt pack (PDF))

Source: Microsoft Learn (Business Central documentation)

Not professional advice. This is general information, not accounting, tax, legal or financial advice. Check AI output yourself and consult a licensed professional for your situation. See our Terms of Use.

This entry summarizes a public source. Features, plans, and availability change, so confirm details with the vendor. Tool names are trademarks of their respective owners.

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