Although most of the studies of support vector machines (SVM) models focused on the algorithm improvement or parameters tuning, the performance of SVM models also depends on datasets, based on which the models were constructed. This paper investigate the impact of data comparability on performance of SVM models for credit scoring. After giving several examinations into data comparability and its impairing factors, we collect two practical datasets for credit scoring and then carry out several experiments to construct and test SVM models. According to the experiments' results, it has been clarified that SVM models can classify training datasets perfectly whatever data comparability may be, if we choose appropriate kernel function and related parameters. However, the performance of SVM models to classify new data depends heavily on data comparability. If data comparability is low, the accuracy for classifying test datasets is proportionally low and fluctuates irregularly. It is obvious that guaranteeing data comparability is more important and effective than improving algorithm or turning parameters of SVM models.

: As one kind of regional finance institutions (RFIs), shinkin banks have closer relations with development of regional economies than city banks. This paper aims to apply principal components analysis to evaluate the performance of shinkin banks' contribution to regional economies in northeast area of Japan. Different from the existing studies, this paper is the first attempt to use principal components analysis to analysis of shinkin banks. Meanwhile, we limit our analysis to 27 shinkin banks in the northeast area of Japan and put our emphasis on extracting an effective indicator to measure the contributions of the shinkin banks to regional economies and clarify the issues for the shinkin banks to improve or strengthen their regional contribution.