出版社：Associação Brasileira de Engenharia de Produção
摘要：Paper aims: This paper presents a comparison of the performances of the Bayesian additive regression trees (BART), Random Forest (RF) and the logistic regression model (LRM) for the development of credit scoring models.Originality: It is not usual the use of BART methodology for the analysis of credit scoring data.The database was provided by Serasa-Experian with information regarding direct retail consumer credit operations.The use of credit bureau variables is not usual in academic papers.Research method: Several models were adjusted and their performances were compared by using regular methods.Main findings: The analysis confirms the superiority of the BART model over the LRM for the analyzed data.RF was superior to LRM only for the balanced sample.The best-adjusted BART model was superior to RF.Implications for theory and practice: The paper suggests that the use of BART or RF may bring better results for credit scoring modelling.
