Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1401890.1402014 | KDD |
Keywords | Field | DocType |
newly-created invoice,invoice-to-cash collection,customized collection action,multiple industry,effective management,overall financial performance,multiple firm,financial difficulty,collections strategy,predictive analysis,accounts receivable,collection time,knowledge discovery,design,prediction model,economics,transaction data,supervised learning,predictive modeling | Data mining,Computer science,Invoice,Supervised learning,Order to cash,Cash collection,Knowledge extraction,Transaction data,Payment,Accounts receivable | Conference |
Citations | PageRank | References |
3 | 0.46 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sai Zeng | 1 | 33 | 8.85 |
| Prem Melville | 2 | 1518 | 84.77 |
| Christian A. Lang | 3 | 241 | 14.88 |
| Ioana Boier-Martin | 4 | 82 | 6.22 |
| Conrad Murphy | 5 | 3 | 0.46 |