Optimize Cash Collection: Use Machine learning to Predicting Invoice Payment

Abstract

Predicting invoice payment is valuable in multiple industries and supports decision-making processes in most financial workflows. However, the challenge in this realm involves dealing with complex data and the lack of data related to decisions-making processes not registered in the accounts receivable system. This work presents a prototype developed as a solution devised during a partnership with a multinational bank to support collectors in predicting invoices payment. The proposed prototype reached up to 77% of accuracy, which improved the prioritization of customers and supported the daily work of collectors. With the presented results, one expects to support researchers dealing with the problem of invoice payment prediction to get insights and examples of how to tackle issues present in real data.

Invoice level forecasting with discrete survival methods for effective forecasting of account receivables in supply chain

“…For instance, Zeng et al [4] use supervised machine learning methods, with payment times treated as "classes", while Hu [5] formulates the problem as a binary classification problem, and the focus is on whether the invoices are paid late or on time. Similarly, Appel et al [6,7] propose a method to predict whether invoices are paid within five days of being due or not, in order to support collection agents. These approaches based on machine learning methods do not incorporate time-related information, which limits their ability to make accurate predictions of account receivables.…”