Credit Risk Analysis Applying Logistic Regression , Neural Networks and Genetic Algorithms Models

Credit models are useful to evaluate the risk of consumer loans. The application of the technique with greater precision of a prediction model will provide financial returns to the institution. In this study a sample set of applicants from a large Brazilian financial institution was focused on in order to develop three models each one based on one of the alternative techniques: Logistic Regression, Neural Networks and Genetic Algorithms. Finally, the quality and performance of these models are evaluated and compared to identify the best one. Results obtained by the logistic regression and neural network models are good and very similar, although the former is slightly better. The genetic algorithm model is also efficient, but somewhat inferior. This study illustrates the procedures to be adopted by a financial institution in order to identify the best credit model to evaluate the risk of consumer loans and thereby get increasing profits.