In order to proactively avoid an overdue situation or to better accommodate the situation you can use the Predictive Scenario of Risk of Late Payment. This predefined predictive scenario allows you, as a data scientist or machine learning expert, to enable your users to predict the risk of late payment for invoices. After enabling the scenario, the Risk of Late Payment field supports collection specialists in their daily work. The prediction of the risk of late payment is based on historical data and by using Intelligent Scenario Lifecycle Management.
The Risk of Late Payment field indicates if there is a high risk of a late payment for an invoice. Late payment is defined as over 30 days after the due date for net payment.
Collection specialists can use this indicator in the following ways:
- Display the risk of late payment for invoices in the Process Receivables app.
- Define the risk of late payment as selection criterion to prioritize invoices, using the Manage Invoice Prioritization Rules app.
- Include the risk of late payment in the collection strategy.
The risk of late payment is determined based on a machine learning scenario. An automatic background job updates the risk of late payment for your invoices once a day.
When training the model, consider using data that corresponds to your business cases, with a sufficient amount of data, and train and activate the model at least once a month.
You need to enable the scenario using the Intelligent Scenarios and the Intelligent Scenario Management applications.