Managing loan customers using misclassification patterns of credit scoring model

Yoon Seong Kim, So Young Sohn

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)567-573
Number of pages7
JournalExpert Systems with Applications
Issue number4
Publication statusPublished - 2004 May

Bibliographical note

Funding Information:
This work was supported by grant No. (R04-2002-000-20003-0) from the Basic Research Program of the Korea Science & Engineering Foundation.

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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