Customer Collections Risk Prediction

The Customer Collections Risk Prediction application predicts the risk of a scheduled payment on an invoice being late because it won't be paid by the due date.

This application creates a risk score for each customer by considering the likelihood of late payment over all invoices. The risk score for an invoice or a customer is 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 Risk 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.
  • Extend Due Date by Days – For prediction model training, the invoice payment is considered late if it's still unpaid after the due date. Setting this parameter allows the prediction model to extend the due date which works as additional grace days. For example, if this parameter is set to 5 days, then this application doesn't consider invoices for which collections are overdue up to 5 days as late.

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 Risk Prediction in the Customer folder and Risk Prediction Statistics in the Transaction Details folder.

See Subject Areas.

Use these subject areas to create user defined analyses on risk of late payment for the open invoices or to understand the customers at risk of default.

Column Definition
Customer Risk Decile Decile ranking in which the customer falls in based on the customer risk score aggregated across invoices.
Customer Risk Percentile Percentile ranking in which the customer falls in the customer risk score is aggregate across invoices.

Default Score

 Probability of defaulting for the payment schedule.
Default Score Decile Decile ranking for each record assigned based on Default Score between 1 to 10 in the increasing order of risk.
Default Score Percentile Percentile ranking for each record assigned based on Default Score between 1 to 100 in the increasing order of risk.
Risk Score Default score and the unpaid amount that indicates the amount at risk.
Schedule Risk Score Decile Decile ranking for each record assigned based on Risk Score between 1 to 10 in an increasing order of risk.
Schedule Risk Score Percentile Percentile ranking for each record assigned based on Default Score between 1 to 100 in an increasing order of risk.
Prediction Date Time Date and time stamp for the last prediction scoring engine run.
Prediction Processed Indicator (Y/N) This indicates whether the record was used for processing by the engine or not.
Run Date Date and time stamp for the last prediction training engine run.
Classifier Score Bucket Classifier scores from 1 to 100 assigned based on prediction model training data.
Actual Defaults Number of actual defaults against a particular classifier score.
Classifier Predicted Defaults Number of predicted defaults against a particular classifier score.
Classifier Accuracy Percentage Calculated as Actual Defaults/Classifier Predicted Default.
Use these prebuilt visualization projects to get started with customer collections risk analysis:
  • Customer Risk Analysis – Look at the overdue amount as a proportion of total outstanding amount for customers. Including risk classification based on deciles as well as risk analysis across payment terms.
  • Invoice Risk Analysis – Provides visibility for transactions contributing to the risk with default probability and risk score for each individual invoice.

These projects are available in Shared Folder > Oracle > Fusion ERP > Accounts Receivable > Customer Collections Risk Analysis.

Frequently Asked Questions

Review these questions to understand the application:

  1. 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.

  2. 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.

  3. How frequently is the model calibrated or trained?

    The model is trained or recalibrated on a weekly basis to improve predictions over time.

  4. What algorithms does the prediction model use?

    The algorithms used is a proprietary multi-classification algorithm.