Predicting Account Receivables with Machine Learning
This work addresses invoice payment prediction for financial industries, but it is incremental as it applies existing machine learning methods to a specific domain problem.
The paper tackled the problem of predicting invoice payment times to support financial workflows, achieving 81% prediction accuracy and demonstrating potential monthly savings of ~$1.75 million by prioritizing collector work.
Being able to predict when invoices will be paid is valuable in multiple industries and supports decision-making processes in most financial workflows. However, due to the complexity of data related to invoices and the fact that the decision-making process is not registered in the accounts receivable system, performing this prediction becomes a challenge. In this paper, we present a prototype able to support collectors in predicting the payment of invoices. This prototype is part of a solution developed in partnership with a multinational bank and it has reached up to 81% of prediction accuracy, which improved the prioritization of customers and supported the daily work of collectors. Our simulations show that the adoption of our model to prioritize the work o collectors saves up to ~1.75 million dollars per month. The methodology and results presented in this paper will allow researchers and practitioners in dealing with the problem of invoice payment prediction, providing insights and examples of how to tackle issues present in real data.