Análise de risco de crédito com o uso de modelos de regressão logística, redes neurais e algoritmos genéticos / Credit risk analysis applying logistic regression, neural networks models and genetic algorithms

AUTOR(ES)

Eric Bacconi Gonçalves

DATA DE PUBLICAÇÃO

2005

RESUMO

Most of the large Brazilian institutions which work with credit concession use credit models to evaluate the risk of consumer loans. Any improvement in techniques that results in the precision increase of a prediction model, will provide financial gains to the institution. The first phase of this study introduces concepts of credit and risk. Subsequently, with a sample set of applicants from a large Brazilian financial institution, three credit scoring models are built applying three different techniques: Logistic Regression, Neural Networks and Genetic Algorithms. Finally, the quality and the performance of these models are evaluated and compared, and the best one is identified. The results obtained by the logistic regression model and neural network model are good and very similar, but the first one is slightly better. The results obtained with the genetic algorithm model are also good, but a little bit inferior. This study shows proceedings to be adopted by a financial institution in order to identify the best credit model to evaluate the risk of consumer loans. The use of the proper model will help the definition of an adequate business strategy and increase profits.

ASSUNTO(S)

credit scoring models redes neurais regressão logística credit risk algoritmos genéticos risco de crédito modelos de credit scoring neural networks genetic algorithms logistic regression

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