Credit portfolio default in the Dominican Republic: a predictive machine learning approach

What it is about

A study that identifies the variables with the greatest impact on predicting a borrower’s default in the Dominican financial system. It selects 39 variables inspired by the default prediction literature and a machine learning model called CatBoost, and uses SHAP values to assess the interaction between variables and identify the relevance of each.

The model fit, measured by the area under the ROC curve, was 86%, with an F1 score of 50%.