Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

8-2018

Abstract

One of the main costs associated with Accounts receivable (AR) collection is related to the intervention actions taken to remind customers to pay their outstanding invoices. Apart from the cost, intervention actions may lead to poor customer satisfaction, which is undesirable in a competitive industry. In this paper, we studied the payment behavior of invoices for customers of a logistics company, and used predictive modeling to predict if a customer will pay the outstanding invoices with high probability, in an attempt to reduce intervention actions taken, thus reducing cost and improving customer relationship. We defined a pureness measure to classify customers into two groups, those who paid all their invoices on time (pureness = 1) versus those who did not pay their invoices (pureness = 0), and then use their attributes to train predictive models, to predict for customers who partially paid their invoices on time (0 < pureness < 1), to determine those who will pay with high probability. Our results show that a Neural Network model was able to predict with high accuracy and further concluded that for a 0.1 unit increase in pureness measure, the customer is 1.132 times more likely to pay on time.

Keywords

accounts receivable, predictive modelling, intervention actions, customer level

Discipline

Accounting | Computer Sciences | Operations and Supply Chain Management

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 14th International Conference on Data Science (ICDATA 2018), Las Vegas, Nevada, July 30 - August 2

First Page

23

Last Page

29

ISBN

1601324812

Publisher

CREA

City or Country

Las Vegas, NV

Embargo Period

10-1-2018

Additional URL

https://csce.ucmss.com/cr/books/2018/LFS/CSREA2018/ICD8000.pdf

Download

Share

COinS