Customer level predictive modeling for accounts receivable to reduce intervention actions

• Main Authors: Cheong, Michelle L. F., SHI, Wen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: accounts receivable; predictive modelling; intervention actions; customer level; Accounting; Computer Sciences; Operations and Supply Chain Management
• Online Access: https://ink.library.smu.edu.sg/sis_research/4133; https://ink.library.smu.edu.sg/context/sis_research/article/5136/viewcontent/ICD8000.pdf
• Institution: Singapore Management University

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.