Payment predictions
The ability to forecast when customers are likely to pay invoices can be crucial to businesses. The reason is because it allows management to plan, knowing what resources they’ll likely have in the future, and it can also help increase the likelihood of being paid on time. Payment predictions help organizations identify which invoices are likely to be paid late or very late.
Payment predictions can also be combined with the Collects process automation feature to create collection activities for invoices predicted to be paid late. You can also set up a threshold or benchmark to ensure that collection activities are only created for invoices that have a high likelihood of being paid late.
Customer payment predictions help organizations deploy and use an AI solution in Finance. After you’ve set up and enabled the Finance insights add-in, you can deploy the AI solution with a single mouse click. This feature gives you access to view the intelligent prediction made by the AI solution. Then, the solution will provide details about how accurate a prediction it was able to make based on the data that it had to work with. If you want to modify the accuracy of the prediction, a subject matter expert can enter the AI Builder extension and edit the fields that are used to create the prediction. When satisfied with the changes, you can train the model and publish the changes. In Finance, the newly trained model will be picked up automatically to generate predictions.
The Customer payment predictions feature analyzes historical invoices, payments, and customer data by using machine learning to form a prediction about when customers will pay an outstanding invoice.
For each open invoice, the model will assign three payment probabilities:
- The probability that the payment will be made on time.
- The probability that the payment will be made late.
- The probability that the payment will be made very late.
The following screenshot shows the payment prediction gauge.
The following screenshot shows the payment predictions amount.
Each invoice is assigned a probability of on-time payment. Invoices that have less than 50 percent of on-time probability are tagged with a red circle to indicate that they might require attention from a collections agent.
The following screenshot shows the transactions for the Sparrow Retail customer and the probability of each individual invoice being paid on time.
Customer payment predictions provide detail about the factors that impacted it the most, which lead it to determine how likely a customer is to pay on time. Major factors that influence the model include historical data about the customer's payment behavior and the current state of business with the customer.
Watch the following demonstration of payment predictions.