Machine Learning for Accounts Receivable Payment Forecasting

Abstract

Account receivables (AR) are a business's most valuable asset. This research paper explores how supervised machine learning can predict payment outcomes for open invoices, specifically addressing the common challenge of maintaining consistent income for small to medium enterprises (SMEs). We developed a model using various machine learning techniques, including linear regression, random forest regressor, decision tree classifier, Linear SVR, and XGB Regressor, by training it on actual AR data. This model assists collectors in forecasting debt payment.