A Stacked generalization framework for credit risk assessment

Abstract

Credit risk assessment has been a major research topic in finance during the past two decades. A significant part of this research has been focused on the development and application of a variety of classification methodologies for designing efficient credit risk assessment models. The methodologies used, originate from different disciplines, including statistics/ econometrics, operational research and artificial intelligence. Most of the previous studies have been focused on the selection of an appropriate methodology to develop a credit risk assessment model and the comparison with other techniques to investigate the relative efficiency of the methods. On the contrary, this study proposes the combination of different methods in a stacked generalization context. Stacked generalization is a general framework for combining different classification models into an aggregate estimate that is expected to perform better that the individual models. This approach is employed to combine credit risk assessment models developed through different classification methods into a combined model. The results obtained from the application of the proposed methodology in two credit risk assessment problems are quite encouraging.