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.

►
Updating credit scoring model with new screening attributes is
proposed. ► Various relationships between existing and new attributes
are investigated. ► Factor Analysis, ANOVA, and logistic regression are
used to confirm the best model.