An Integrated Genetic-Based Model of Naive Bayes Networks for Credit Scoring

Inappropriate management in some fields such as credit allocation has imposed too many losses tofinancial institutions and even has forced some of them to go bankrupt. Moreover, large volume data setscollected by credit departments has necessitated utilizing highly accurate models with less complexities.Credit scoring models with classification and forecasting customers into two groups good and bad candramatically reduce risks of granting credits to customers.In this paper, a novel integrated approach for credit scoring problem is presented. This approach utilizesrough sets for feature selection during the data pre-processing phase and also adopts two hybridsequences, Na ve Bayes networks and genetic algorithm, to classify customers. In order to assess thecompetitive performance of the proposed approach, it has been executed on three credit scoring datasetsfrom the University of California Irvine Machine Learning Repository. Computational results demonstratethat our approach has superior performance in terms of classification accuracy and achieves higheroverall classification rate as compared to several other previous studies.