Examinando por Autor "Arango Correa, Diana Marcela"

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    Comparación entre el método tradicional y algunos basados en inteligencia artificial para el estudio del riesgo crediticio en instituciones financieras colombianas
    (Universidad EAFIT, 2018) Arango Correa, Diana Marcela; Colmenares Colmenares, Laura Juliana; Rave Contreras, Isabel Cristina; Martínez Negrete, Milton Alfonso
    Artificial intelligence models are an open problem for application in various fields of science and search of variable relationships especially when the distribution of events doesn’t depend on a linear function; through this work we want to compare the traditional method most used for credit behavior monitoring with advanced models of artificial intelligence -- The guides that exist in Colombia for management of credit risk are given by the Financial Superintendence of Colombia, international standards such as Basel II, Basel III and Solvency are based on the logistic regression and the discriminant analysis, models used by financial institutions in Colombia to measure credit behavior, thus we carried out an investigation to explore the utility of new models -- This paper addresses one of the traditional methods used in financial institutions, that is, logistic regression, and compares it with alternative methods such as neural networks and random forests -- From the literature review and using a database provided by a banking entity, the dependent variables and the response variable are selected, the logistic regression models, random forests and neural networks are calibrated in the Microsoft Azure Machine Learning application and they are compared to each other with indicators of precision and accuracy such as ROC (from receiver operating characteristic) curve and confusion matrix, obtaining for the models of artificial intelligence, results as good as the traditional one; so they can be used by the financial sector as alternate and / or complementary methods in the analysis of credit risk