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Dynamics 365 Community / Blogs / Mansi Soni’s Knowledge Junction - Business Cent... / Predict Late Payments in Bu...

Predict Late Payments in Business Central - Proactive Receivables Management

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Mansi Soni Profile Picture Mansi Soni 8,837 Super User 2025 Season 2
In a world where cash flow drives survival and growth, knowing which invoices are likely to be paid late can be a game-changer. Microsoft Dynamics 365 Business Central’s Late Payment Prediction extension introduces AI-powered insights right into your receivables workflow. Rather than waiting for invoices to go past due and chasing them reactively, you can get ahead by identifying risk up front.

Here’s how it works-and why it matters.

What Is Late Payment Prediction?

Late Payment Prediction is an extension built into Business Central (online) that helps predict whether a posted sales invoice will be paid on time or late.

Key features:
  • When viewing a posted sales document, you’ll see a notification prompting you to Enable predictions (if not already enabled).
  • Once enabled, a “Payments Predicted to be Late” tile becomes available in the Business Manager Role Center.
  • On the Customer Ledger Entries page, new columns appear:
    • Late Payment (Yes / No)
    • Prediction Confidence (High / Medium / Low)
    • Optionally, Prediction Confidence % (the numeric percentage)
  • The system uses a predictive model running in the backend (Azure ML), and evaluates, retrains, and updates predictions over time.
How Predictions Are Made

The model’s predictions are based on a blend of individual invoice data and aggregated customer behavior metrics. Some of the data used:
  • Invoice amount (in local currency, tax included)
  • Payment terms (days between posting and due date)
  • Whether a credit memo was applied
  • Aggregated historical data for that customer:
    • Number & total amounts of paid vs late invoices
    • Outstanding invoices (including those already overdue)
    • Ratios: late vs paid, outstanding late vs all outstanding
    • Average days late, etc.
The predictive model can be the standard model (provided by Microsoft) or your custom model (trained on your own data).
If you choose a custom model (via Azure ML), you supply the API URL and key, and switch Business Central to use My Model.

Why This Feature Matters
  • Proactive collections - Instead of waiting for invoices to become overdue, collections teams can focus efforts on high-risk invoices early.
  • Better cash flow forecasting - Knowing which payments are likely to slip helps you anticipate shortfalls and plan.
  • Resource optimization - Your team can avoid chasing every invoice, and instead prioritize higher-risk ones.
  • Customizable & extensible - The model evolves with your data; as your business grows, predictions improve.
  • Transparent confidence levels - You don’t have to treat every prediction as fact; “High / Medium / Low” confidence gives you a calibrated view.

Setup & Usage: A Step-by-Step

Enable the extension
  • Use the “Enable” link in the notification on a posted sales document, or go to Late Payment Prediction Setup via search.
    Note: Business Central may prevent enablement if model quality is too low.
Configure model & thresholds
  • Review Model Quality (how accurate the model is).
  • Set a Model Quality Threshold below which predictions aren’t trusted.
  • (Optional) Use your own Azure ML model instead of standard.
Update predictions
  • Predictions are auto-recalculated weekly.
  • You can also force a manual Update Prediction action.
View & act on predictions
  • Go to the Payments Predicted to be Late tile.
  • Review predicted late invoices in Customer Ledger Entries with their confidence levels.
  • Use filters (e.g. Late = Yes) and export or escalate those invoices.
  • Consider modifying payment terms, sending reminders, or switching payment methods for riskier invoices.
Things to Watch Out For & Limitations
  • Not available in on-premises deployments
    The Late Payment Prediction function is only supported in Business Central online.
  • Model quality depends on data
    If your history is sparse or lacks variety (few late payments or few invoices), the model may struggle or refuse to enable.
  • Predictions are probabilistic, not definitive
    A “Late” or “High Confidence” label is a prediction, not a guarantee. Use it as a guide, not an absolute.
  • Data privacy, security & costs
    If using your own Azure ML model, you’ll need to manage API keys and your ML environment. Also, compute usage may have cost implications.
  • Adoption & trust
    Users and collections teams may initially distrust predictions. You’ll likely need change management-show value via pilot usage, reporting, and feedback cycles.
Example Scenario

Imagine a mid-sized manufacturing firm. Historically, a small subset of customers tends to pay 10–20 days past due, but often unpredictably.
  • They enable Late Payment Prediction in Business Central.
  • Over a few weeks, the system flags certain invoices as “Late – High Confidence.”
  • The collections team immediately reaches out to those customers, perhaps offering a small discount or more favourable terms if they pay earlier.
  • Some customers accept; some don’t. Over time, fewer invoices go into severe overdue status.
  • Because the system continues learning, its predictions get sharper with more data.
The net result: fewer bad debts, improved cash flow, less reactive chasing, and more efficient resource allocation.

In Summary

Late Payment Prediction brings AI into your receivables process-making the leap from reactive collection to proactive cash flow management. Whether you use Microsoft’s standard model or your custom version, this extension gives you visibility, predictive insight, and the ability to act early.


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