Customer Collections Date Prediction
The Customer Collections Date Prediction application predicts the date when an invoice will be paid by customer and calculates the expected delay days to help manage cash flows precisely.
The predicted payment date and predicted delay days related attributes are exposed in the Financials - AR Transaction and Financials - AR Aging subject areas.
Prerequisites
Ensure that the Accounts Receivables functional area is activated prior to enabling this application on the Enable Features page. See Activate a Data Pipeline for a Functional Area and Enable Generally Available Features.
Configuration Parameters
Configure the Customer Collections Date Prediction application by selecting appropriate values for these parameters:
- Historic Periods for Training data – The number of months of training data desired for training a prediction model. Model is expected to get trained and perform better on longer time frames like 60 months. Although there are no preset limits, it is recommended that at least 24 months of training data is provided.
- Future Period for Prediction – The number of future periods in months for which prediction scores will be available based on Scoring data. Some payment schedules extend to many years and this period controls how many months ahead need to be evaluated for risk of late payment. For example, if this is set to 12 months, predictions will be made only for those invoices for which payments are due within the next 12 months.
- Invoice Threshold Amount – Predictions will be made for only invoices where due amounts are above the threshold amount. Use this to filter low valued invoices or set it as “0” to consider everything.
How to Use the Predictions
The prediction scores and related attributes are available in the Financials - AR Aging and AR Transactions subject areas. View Customer Date Prediction in the Customer folder and Date Prediction Statistics in the Transaction Details folder.
See Subject Areas.
Use these subject areas to create user defined analyses to know the total receivables amount that will get delayed, minimum, and maximum predicted delays for a specific customer.
| Column | Definition |
|---|---|
| Minimum Predicted Delay Days | Minimum number of delay days predicted across invoices for the customer. |
| Maximum Predicted Delay Days | Maximum number of delay days predicted across invoices for the customer. |
| Average Predicted Delay Days | Average number of delay days predicted across invoices for the customer. |
| Customer Delay Decile | Decile ranking for the customer assigned based on Default Score between 1 to 10 in the increasing order of risk. |
| Customer Delay Percentile | Percentile ranking for the customer assigned based on Default Score between 1 to 100 in the increasing order of risk. |
| Coefficient of Variation | Calculated as variation between predicted delay days and actual delay days. |
| Payment Schedule Identifier | Payment schedule identifier for an invoice. |
| Predicted Payment Date | Predicted date for the payment for each individual invoice |
| Predicted Delay Days | Calculated as a difference of predicted delay days and schedule due date. |
Frequently Asked Questions
Review these questions to understand the application:
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How much data do we need for the prediction model to be accurate?
Accuracy of predictions improve if larger amounts of historical data is used for training. The minimum recommended training data is 2 years of invoice payment schedules and payments. The classifier accuracy metric shows the accuracy of model predictions. Minimum data must be 10000 records and the recommended data is few tens of thousands records.
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How frequently does the model create predictions on future data?
The model calculates and generates predictions on future data daily. Previous predictions are overwritten based on the learnings from actual payment data. A snapshot of previous predictions is also maintained for historical reference.
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How frequently is the model calibrated or trained?
The model is trained or recalibrated on a weekly basis to improve predictions over time.
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What algorithms does the prediction model use?
The algorithms used is a proprietary multi-classification algorithm.