Evolutionary Radial Basis Functions for Credit Assessment

• Published in: Applied intelligence (Dordrecht, Netherlands) Vol. 22; no. 3; pp. 167 - 181
• Main Authors: Lacerda, Estefane, Carvalho, Andr C. P. L. F., Braga, Ant nio P dua, Ludermir, Teresa Bernarda
• Format: Journal Article
• Language: English
• Published: Boston Springer Nature B.V 01-05-2005
• Subjects: Artificial intelligence; Business information; Computer applications; Credit ratings; Expert systems; Genetic algorithms; Neural networks; Risk assessment; Studies
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10791-005-6617-0

Credit analysts generally assess the risk of credit applications based on their previous experience. They frequently employ quantitative methods to this end. Among the methods used, Artificial Neural Networks have been particularly successful and have been incorporated into several computational tools. However, the design of efficient Artificial Neural Networks is largely affected by the definition of adequate values for their free parameters. This article discusses a new approach to the design of a particular Artificial Neural Networks model, RBF networks, through Genetic Algorithms. It presents an overall view of the problems involved and the different approaches employed to optimize Artificial Neural Networks genetically. For such, several methods proposed in the literature for optimizing RBF networks using Genetic Algorithms are discussed. Finally, the model proposed by the authors is described and experimental results using this model for a credit risk assessment problem are presented.[PUBLICATION ABSTRACT]