Using predictive analysis to improve invoice-to-cash collection

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