Assessment of financial risk prediction models with multi-criteria decision making methods

Authors:
Jose Salvador Sánchez;Vicente García;Ana Isabel Marqués
Affiliations:
Dep. Computer Languages and Systems --- Institute of New Imaging Technologies, Universitat Jaume I, Castelló de la Plana, Spain;Dep. Computer Languages and Systems --- Institute of New Imaging Technologies, Universitat Jaume I, Castelló de la Plana, Spain;Dep. Business Administration and Marketing, Universitat Jaume I, Castelló de la Plana, Spain
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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Abstract

A wide range of classification models have been explored for financial risk prediction, but conclusions on which technique behaves better may vary when different performance evaluation measures are employed. Accordingly, this paper proposes the use of multiple criteria decision making tools in order to give a ranking of algorithms. More specifically, the selection of the most appropriate credit risk prediction method is here modeled as a multi-criteria decision making problem that involves a number of performance measures (criteria) and classification techniques (alternatives). An empirical study is carried out to evaluate the performance of ten algorithms over six real-life credit risk data sets. The results reveal that the use of a unique performance measure may lead to unreliable conclusions, whereas this situation can be overcome by the application of multi-criteria decision making techniques.