2008 UsingPredictiveAnalysistoImprov

(Zeng et al., 2008) ⇒ Sai Zeng, Prem Melville, Christian A. Lang, Ioana Boier-Martin, and Conrad Murphy. (2008). “Using Predictive Analysis to Improve Invoice-to-cash Collection.” In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008). doi:10.1145/1401890.1402014

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Abstract

It is commonly agreed that accounts receivable (AR) can be a source of financial difficulty for firms when they are not efficiently managed and are underperforming. Experience across multiple industries shows that effective management of AR and overall financial performance of firms are positively correlated. In this paper we address the problem of reducing outstanding receivables through improvements in the collections strategy. Specifically, we demonstrate how supervised learning can be used to build models for predicting the payment outcomes of newly-created invoices, thus enabling customized collection actions tailored for each invoice or customer. Our models can predict with high accuracy if an invoice will be paid on time or not and can provide estimates of the magnitude of the delay. We illustrate our techniques in the context of real-world transaction data from multiple firms. Finally, simulation results show that our approach can reduce collection time up to a factor of four compared to a baseline that is not model-driven.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 UsingPredictiveAnalysistoImprov	Prem Melville Sai Zeng Christian A. Lang Ioana Boier-Martin Conrad Murphy			Using Predictive Analysis to Improve Invoice-to-cash Collection				10.1145/1401890.1402014