Optimal bipartite scorecards

In many retail banking applications, scorecards used for assessing creditworthiness must be simple and interpretable. For this reason, the industry has favoured logistic regression models based on categorised variables. In this paper we describe an extension of such models based on an optimal partit...

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Bibliographic Details
Published inExpert systems with applications Vol. 29; no. 3; pp. 684 - 690
Main Authors Hand, David J., Sohn, So Young, Kim, Yoonseong
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.10.2005
Subjects
Credit scoring
Logistic model
Retail banking
Scorecard
Logistic model
Scorecard
Retail banking
Credit scoring
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Summary:In many retail banking applications, scorecards used for assessing creditworthiness must be simple and interpretable. For this reason, the industry has favoured logistic regression models based on categorised variables. In this paper we describe an extension of such models based on an optimal partition of the applicant population into two subgroups, with categorised logistic models being built in each part. Such bipartite models have the merits of yielding improved predictive accuracy, while retaining interpretive simplicity. They have been used in the industry before, but only in an ad hoc way, with no effort being made to find the optimal division. Some examples and properties of the resulting models are described.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2005.04.032