Classification of non-performing loans portfolio using Multilayer Perceptron artificial neural networks - Flávio Clésio Silva de Souza (GEPROS: Gestão da Produção, Operações e Sistemas, 2014)
The purpose of the present research is to apply a Multilayer Perceptron (MLP) neural network technique to create classification models from a portfolio of Non-Performing Loans (NPLs) to classify this type of credit derivative. These credit derivatives are characterized as the amount of loans that were not paid and are already overdue more than 90 days. Since these titles are, because of legislative motives, moved by losses, Credit Rights Investment Funds (FDIC) performs the purchase of these debts and the recovery of the credits. Using the Multilayer Perceptron (MLP) architecture of Artificial Neural Network (ANN), classification models regarding the posterior recovery of these debts were created. To evaluate the performance of the models, evaluation metrics of classification relating to the neural networks with different architectures were presented. The results of the classifications were satisfactory, given the classification models were successful in the presented economics costs structure.