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A Look into the Customer Payment Insights Feature in Dynamics 365 Finance
Posted on: August 20, 2022 | By: Hope Jin | Microsoft Dynamics AX/365, Microsoft Dynamics Manufacturing
It can be difficult to predict when customers will pay their invoices. This lack of insight leads to less accurate cash flow forecasts, collections processes that start too late, and orders that are released to customers who may default on their payment. To solve these problems, Microsoft Dynamics 365 Finance will be introducing the Customer payment insights feature. This feature will help organizations predict when a customer invoice will be paid which will help organizations create collections strategies that improve the probability of being paid on time.
To get the first look at this new feature continue reading this blog. This blog describes the payment insights capability that helps improve understanding of individual customers’ typical payment practices.
Payment predictions will enable organizations to improve their business processes by helping them easily identify the invoices that are likely to be paid late, and to take appropriate measures that improve their chances of getting paid on time.
Using a machine learning model, which leverages historical invoices, payments and customer data, Customer payment insights (Preview) more accurately predicts when a customer will pay an outstanding invoice.
For each open invoice, Customer payment insights (Preview) can predict three payment probabilities:
- Probability of payment being made on time
- Probability of payment being made late
- Probability of payment being made very late
Customer payment insights (Preview) also provides an aggregated view of expected payments, which can help organizations understand the total payment amount they can expect from a customer in one of the three buckets, On time, Late and Very late.
Also, each invoice is assigned a probability of payment on time. If the probability of payment on time is less than 50%, the invoices are tagged with a red circle to indicate that these invoices may require collections attention.
Customer Payment Insights (Preview) also provides contextual information to explain the prediction, such as the top factors that influenced the predictions, the current state of business with the customer, and details about the historical customer payment behavior. In many businesses, the collections process has been a reactive activity; the collections process doesn’t start until invoices come due.
With Customer payment insights (Preview), organizations can be more proactive about collections. They no longer have to wait for the transactions to become due to start the collection process. Instead, they can use the payment prediction capability to determine whether proactive collections will improve the probability of being paid on time. Payment prediction also gives businesses the information needed to justify starting the collection process early.
Developing and deploying an AI solution is hard. It takes a team of data scientists, subject matter experts and engineers, working for an extended period of time to formulate, develop, deploy, and maintain a usable AI solution. We are making it easy to deploy and use AI solutions in Finance. We are prepackaging AI solutions in Finance that are built on top of Microsoft AI Builder. An end user, with the single click of button, can deploy the AI solution and start leveraging the benefits of intelligent predictions. If an organization isn’t satisfied with the accuracy of predictions, a power user, again using a single click, can enter the AI builder extension experience, and then select or deselect the fields used to generate predictions. Once ready, they can train and publish the changes, and the newly trained model will be automatically picked up for predictions in Finance.
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