Invoice Payment Prediction

Accounts Receivable (AR) is the source of financial difficulty for firms when collections are not efficiently managed. Inadvertent invoice payments can lead to financial instability. In spite of having historical data to assess the risk of invoice/ customer delinquency, most of the organizations are not assessing the risk due to lack of predictive modeling expertise in their organizations. Inefficiencies in prioritizing the invoices to focus on, leads to significant delays in AR collections or write-offs.

Jade Solution

Jade has developed Invoice Outcome Prediction solution which predicts what invoices will be paid late and by how many days the invoices could be defaulted Following are the key data elements that are captured from the ERP system to achieve the desired results.

  • Customer Name
  • Invoice base amount
  • Payment terms
  • Invoice category
  • Invoice created date
  • Invoice due date

Jade Global gathers the historical information to arrive at the credit rating of the customer, which includes the total invoices paid, number of invoices that were late, and the ratio of invoices paid versus late average days late. For new customers where historical information is absent, information like customer credit worthiness, organizational profile, financial details etc. are gathered from public domains and credit rating agencies for predicting the invoice outcome. Jade used supervised learning algorithms and built statistical models to predict the outcome of the invoice payment.

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Different risk level of each customer and their invoices

Invoice Payment Prediction

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Business Benefits

Jade Global Invoice Outcome Prediction solution gives you a much needed heads-up and prevents any kind of defaulting in the invoice payments.
This can bring in financial stability by:

  • Prioritizing delinquent invoices for actions based on the expected time of payment, one can optimize the use of collections resources
  • Taking preemptive actions on invoices that are likely to become delinquent can drive down the collection time
  • Predicting collection probabilities, most suitable payment terms, and schedules for a given customer