Predicting Recovery of Credit Operations on a Brazilian Bank
Rogerio Gomes Lopes, Rommel Novaes Carvalho, Marcelo Bronzo Ladeira, Ricardo Silva Carvalho · 2016
This article presents a study conducted in a Brazilian bank, in order to assist the institution account managers in the approach to customers with loans in arrears. This approach is carried out to propose alternatives to customers return to timely payments situation, but the efficiency of this approach is small, accounting for only about 6.8% of customers. A predictive model, using classification was used to help identify customers with the most potential to return to a normal situation, reaching a 85.5% accuracy rate with the winning algorithm, Gradient Boosting Method. It was implemented in the integrated Platform H2O with R language, exploring the grid mode and parallel processing models advantages.