Prediction of invoice payment status in account payable business process

Abstract

Account payables are amount owed to vendors for goods and services delivered to a company. Vendors raise invoices which go through several processing steps before they are paid by a company. Companies have contractual obligations with vendors for paying the invoices within a stipulated time. Invoices that exceed this time attract penalty and affect vendor satisfaction to work with the company. It is very critical for large firms dealing with thousands of vendors for their day to day operations to meet the service level agreements with vendors to avoid penalties. Any assistance for practitioners, warning them of potential invoices that can breach the service level agreements, can help them in minimizing the penalties. In this research, we model the problem of identifying delayed invoices as a supervised classification task. There are three characteristics of this problem which are challenging from a classification perspective: (i) the status of an invoice is affected by other invoices that are simultaneously being processed, as there are limited resources to process the huge volume of invoices, (ii) feature engineering to capture the temporal aspect of the invoice and having the optimal representation of the multiple data entries created per invoice, and (iii) the number of paid late invoices are much smaller in percentage compared to paid on time invoices in the training data set, hence the classes are imbalanced. The results obtained by training an ensemble of classifiers show that penalties can be avoided on more than 82% of the invoices which are currently being penalized.