Estimation of default and pricing for invoice trading (P2B) on crowdlending platforms

Abstract This study developed several machine learning models to predict defaults in the invoice-trading peer-to-business (P2B) market. Using techniques such as logistic regression, conditional inference trees, random forests, support vector machines, and neural networks, the prediction of the default rate was evaluated. The results showed that these techniques can effectively improve the detection of defaults by up to 56% while maintaining levels of specificity above 70%. Unlike other studies on the same topic, this was performed using sampling techniques to address the imbalance of classes and using different time periods for the training and test datasets to ensure intertemporal validation and realistic predictions. For the first-time, default explainability in the invoice-trading market was studied by examining the impact of macroeconomic factors and invoice characteristics. The findings highlighted that gross domestic product, exports, trade type, and trade bands are significant factors that explain defaults. Furthermore, the pricing mechanisms of P2B platforms were evaluated with the observed and implicit probabilities of the default to analyze the price risk adjustment. The results showed that price reflects a significantly higher implicit probability of default than observed default, which in turn suggests that underlying factors exist besides the borrowers’ probability of default.