Using behavioral analytics to predict customer invoice payment

Bahrami, Mohsen and Bozkaya, Burçin and Balcısoy, Selim (2020) Using behavioral analytics to predict customer invoice payment. Big Data, 8 (1). pp. 25-37. ISSN 2167-6461 (Print) 2167-647X (Online)

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Experiences from various industries show that companies may have problems collecting customer invoice payments. Studies report that almost half of the small- and medium-sized enterprise and business-to-business invoices in the United States and United Kingdom are paid late. In this study, our aim is to understand customer behavior regarding invoice payments, and propose an analytical approach to learning and predicting payment behavior. Our logic can then be embedded into a decision support system where decision makers can make predictions regarding future payments, and take actions as necessary toward the collection of potentially unpaid debt, or adjust their financial plans based on the expected invoice-to-cash amount. In our analysis, we utilize a large data set with more than 1.6 million customers and their invoice and payment history, as well as various actions (e.g., e-mail, short message service, phone call) performed by the invoice-issuing company toward customers to encourage payment. We use supervised and unsupervised learning techniques to help predict whether a customer will pay the invoice or outstanding balance by the next due date based on the actions generated by the company and the customer's response. We propose a novel behavioral scoring model used as an input variable to our predictive models. Among the three machine learning approaches tested, we report the results of logistic regression that provides up to 97% accuracy with or without preclustering of customers. Such a model has a high potential to help decision makers in generating actions that contribute to the financial stability of the company in terms of cash flow management and avoiding unnecessary corporate lines of credit.
Item Type: Article
Uncontrolled Keywords: behavioral analytics; invoice collection; invoice to cash; logistic regression; machine learning; predictive analytics
Divisions: Sabancı Business School
Sabancı Business School > Operations Management and Information Systems
Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: Selim Balcısoy
Date Deposited: 16 Jun 2020 17:17
Last Modified: 04 Aug 2023 21:47

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