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Adaptive consumer credit classification / N. G. Pavlidis in Journal of the operational research society (JORS), Vol. 63 N° 12 (Décembre 2012) 

Public ISBD [article]  in Journal of the operational research society (JORS) > Vol. 63 N° 12 (Décembre 2012) . - pp. 1645-1654 Titre : Adaptive consumer credit classification Type de document : texte imprimé Auteurs : N. G. Pavlidis, Auteur ; D. K. Tasoulis, Auteur ; N. M. Adams, Auteur Année de publication : 2013 Article en page(s) : pp. 1645-1654 Note générale : operational research Langues : Anglais (eng) Mots-clés : credit  scoring;  logistic  regression;  population  drift;  online  learning;  H-measure Index. décimale : 001.424 Résumé : Credit scoring methods for predicting creditworthiness have proven very effective in consumer finance. In light of the present financial crisis, such methods will become even more important. One of the outstanding issues in credit risk classification is population drift. This term refers to changes occurring in the population due to unexpected changes in economic conditions and other factors. In this paper, we propose a novel methodology for the classification of credit applications that has the potential to adapt to population drift as it occurs. This provides the opportunity to update the credit risk classifier as new labelled data arrives. Assorted experimental results suggest that the proposed method has the potential to yield significant performance improvement over standard approaches, without sacrificing the classifier's descriptive capabilities. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n12/abs/jors201215a.html [article] Adaptive consumer credit classification [texte imprimé] / N. G. Pavlidis, Auteur ; D. K. Tasoulis, Auteur ; N. M. Adams, Auteur . - 2013 . - pp. 1645-1654. operational research Langues : Anglais (eng) in Journal of the operational research society (JORS) > Vol. 63 N° 12 (Décembre 2012) . - pp. 1645-1654 Mots-clés : credit  scoring;  logistic  regression;  population  drift;  online  learning;  H-measure Index. décimale : 001.424 Résumé : Credit scoring methods for predicting creditworthiness have proven very effective in consumer finance. In light of the present financial crisis, such methods will become even more important. One of the outstanding issues in credit risk classification is population drift. This term refers to changes occurring in the population due to unexpected changes in economic conditions and other factors. In this paper, we propose a novel methodology for the classification of credit applications that has the potential to adapt to population drift as it occurs. This provides the opportunity to update the credit risk classifier as new labelled data arrives. Assorted experimental results suggest that the proposed method has the potential to yield significant performance improvement over standard approaches, without sacrificing the classifier's descriptive capabilities. DEWEY : 001.424 ISSN : 0160-5682 En ligne : http://www.palgrave-journals.com/jors/journal/v63/n12/abs/jors201215a.html