Updating a credit-scoring model based on new attributes without realization of actual data

Yong Han Ju and So Young Sohn

European Journal of Operational Research, 2014, vol. 234, issue 1, 119-126

Abstract: Funding small and medium-sized enterprises (SMEs) to support technological innovation is critical for national competitiveness. Technology credit scoring models are required for the selection of appropriate funding beneficiaries. Typically, a technology credit-scoring model consists of several attributes and new models must be derived every time these attributes are updated. However, it is not feasible to develop new models until sufficient historical evaluation data based on these new attributes will have accumulated. In order to resolve this limitation, we suggest the framework to update the technology credit scoring model. This framework consists of ways to construct new technology credit-scoring model by comparing alternative scenarios for various relationships between existing and new attributes based on explanatory factor analysis, analysis of variance, and logistic regression. Our approach can contribute to find the optimal scenario for updating a scoring model.

Keywords: Finance; Credit-scoring model; Exploratory factor analysis (EFA); Logistic regression analysis; ANOVA; Small and medium enterprise (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:234:y:2014:i:1:p:119-126

DOI: 10.1016/j.ejor.2013.02.030

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