Managing loan customers using misclassification patterns of credit scoring model

Authors:
Yoon Seong Kim;So Young Sohn
Affiliations:
Department of Computer Science and Industrial Systems Engineering, Yonsei University, 134 Shinchon-dong, Seoul 120-749, South Korea;Department of Computer Science and Industrial Systems Engineering, Yonsei University, 134 Shinchon-dong, Seoul 120-749, South Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2004

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Abstract

A number of credit scoring models have been developed to evaluate credit risk of new loan applicants and existing loan customers, respectively. This study proposes a method to manage existing customers by using misclassification patterns of credit scoring model. We divide two groups of customers, the currently good and bad credit customers, into two subgroups, respectively, according to whether their credit status is misclassified or not by the neural network model. In addition, we infer the characteristics of each subgroup and propose management strategies corresponding to each subgroup.