While traditional models are often interpretable, they struggle to capture complex data patterns. In contrast, MLP neural networks excel in modeling these patterns, offering potentially higher accuracy.

The study evaluates these models using historical data from Royal FrieslandCampina N.V. and addresses the key question: How does an MLP neural network compare to traditional models in predicting accurate trade credit limits? Although the MLP network performs well, its lack of interpretability makes it less ideal for business use. More explainable models, like decision trees, are often preferred within corporations due to the need for transparent decision-making.

This research highlights the trade-off between accuracy and interpretability in credit limit management and introduces a novel application of MLP neural networks to the field. It contributes valuable insights to the ongoing discussion on optimizing trade credit limits, providing a fresh perspective on approaches typically used by financial institutions.