Core-Firm Financial Structure on Reverse Factoring with Machine Learning Models

Abstract

This study presents the most significant financial factors in reverse factoring decision based on accounts receivable. We compare 4 machine learning models (GBDT, SVM, random forest, and KNN) and find that GBDT outperforms the others. The results show that the primary factors in the financial structure of companies on successful reverse factoring are book leverage, company size, and non-debt tax shield, respectively. We also conduct interpretable machine learning methods to analyze these indicators further. This study may shed light on focal firms’ reverse factoring decisions in practice.