关键词: 数据挖掘, 门限Logistic, 小企业主, 信用评分

Abstract: As an international experience, credit scoring technology can effectively solve the problems of small business loans, such as high cost, high risk and asymmetric information. This paper selected 12 data mining models (including the threshold Logistic model which was improved by this paper) which may be suitable for the topic. Three banks’ microscopic customer data sets (sample size was 30488,1000 and 700 respectively) were employed in the case study. This paper assessed the performance of the 12 credit scoring models by using 10-fold cross validation and the expected misclassification costs methods. Analysis results and robustness tests showed that the improved threshold Logistic model outperforms other approaches while the combination methods based on decision trees also performs well. This paper is useful for the domestic commercial banks to establish appropriate credit scoring models for small business owners loan. The implementation of such models can be expected to promote the micro-finance data statistics, and then the macro-government finance statistics.

Key words: Data Mining, Threshold Logistic, Small Business Owners, Credit Scoring