Many techniques have been proposed for credit risk assessment, from statistical models to
artificial intelligence methods. During the last few years, different approaches to classifier
ensembles have successfully been applied to credit scoring problems, demonstrating to be
generally more accurate than single prediction models. The present paper goes one step
beyond by introducing composite ensembles that jointly use different strategies for diversity
induction. Accordingly, the combination of data resampling algorithms (bagging and …