Analysis and comparison of machine learning classification models applied to credit approval

Jorge Alarcón Flores, Jiam Lopez Malca, Luiz Ruiz Saldarriaga, Christian Sarmiento Román

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


The credit granting decision is one of the most important process of all, and in whose accuracy, rests the good performance of several critical business KPI’s such as loans level, credit recoveries level and non-performing loan ratios. In the last decade, the developing of certain technology such AI and machine learning has allowed this process automation. The present paper has its main goal, the analysis of credit granting predictions to collaborate with current knowledge in this issue, giving an objective explanation of the results and suggesting following researches to be developed in order to get better results in existing mathematical algorithms. As results of the experimentation determined that the best model was Gradient Boosting, with an accuracy of 83.71%.
Idioma originalEspañol
Título de la publicación alojadaCEUR Workshop Proceedings
Número de páginas2
EstadoPublicada - 1 ene. 2017
Publicado de forma externa

Citar esto