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Explainability in Models for Predicting the Probability of Default in Compliance with International Standard IFRS 9

This article presents a non-exhaustive analysis of Probability of Default (PD) prediction alternative models, but its main objective is to test a set of multivariate techniques that seek to facilitate the understanding of different Machine Learning models for the estimation of PD, in line with the requirements of IFRS 9 for Expected Loss models, applied to the Mortgage portfolio of the United States.The models with the best results and the process that was carried out for the use of the different methods are shown, the tests carried out are discussed and the models suitable for the prediction of PD are identified, as well as their explanatory analysis that seek to facilitate your understanding and to some extent your review. Model exercised show the Artificial Neural Network models as the best candidates in the testing phase.