Managing loan customers using misclassification patterns of credit scoring model

Yoon Seong Kim; So Young Sohn

doi:10.1016/j.eswa.2003.10.013

Managing loan customers using misclassification patterns of credit scoring model

Yoon Seong Kim, So Young Sohn

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

41 Citations (Scopus)

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.

Original language	English
Pages (from-to)	567-573
Number of pages	7
Journal	Expert Systems with Applications
Volume	26
Issue number	4
DOIs	https://doi.org/10.1016/j.eswa.2003.10.013
Publication status	Published - 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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10.1016/j.eswa.2003.10.013

Cite this

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title = "Managing loan customers using misclassification patterns of credit scoring model",

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.",

author = "Kim, {Yoon Seong} and Sohn, {So Young}",

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.",

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AU - Sohn, So Young

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AB - 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.

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ER -

Managing loan customers using misclassification patterns of credit scoring model

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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