System and Method on Order Management Using Neural Networks and Risk Modeling

Abstract

The transactions of goods and services between enterprise service providers are often driven by contracts and purchase orders. Every month thousands of invoices are billed to customers who settle them based on the usage of services. Considering the vast number of purchase orders that are signed, it requires considerable manual effort by the service provider to process and manage them. Moreover, the invoice's billed data may not be maintained in the same cloud system as the purchase orders. This leads to complexity with data mapping between the two data sets. Sometimes the invoices may get into a dispute due to over exhaustion of allocated funds or may be billed to an expired purchase order. Hence managing the billing service is a huge undertaking along with increased cost.To address these challenges, we developed an order manage- ment system that transforms the monitoring of purchase orders to increase renewals as well as decrease disputes. The system includes an automated purchase order-invoice data mapping model along with a risk analytics model that evaluates the orders against the invoices billed. The output is the set of actionable and non actionable insights based on customer portfolio, risk level as well as market trends in usage of services. We illustrate our method with some promising results on data of one of the world's largest IT service providers.