A credit rating model of microfinance based on fuzzy cluster analysis and fuzzy pattern recognition: Empirical evidence from Chinese 2,157 small private businesses
Issue title: Some highlights on fuzzy systems and data mining
Guest editors: Shilei Sun, Silviu Ionita, Eva Volná, Andrey Gavrilov and Feng Liu
Affiliations: [a] College of Economics & Management, Northwest A & F University, Yangling, Shaanxi, China
| [b] UNSW Business School, The University of New South Wales, Sydney, New South Wales, Australia
Correspondence:
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Corresponding author. Baofeng Shi, College of Economics & Management, Northwest A & F University, Yangling, Shaanxi, China. Tel.: +86 15388615466; Fax: +86 029 87081209; E-mail: [email protected]; [email protected].
Abstract: Small private businesses provide employment for citizens. Their revenue and profit also contribute to GDP. Therefore, they are an important part of economic development in China. However, the key factor that can impede the development of small private businesses is financial problems. In order to solve this problem, we set up a credit rating model to analyze the credit status of small private businesses. The contributions of the paper are threefold. First, this paper introduces a novel technique that divides the customers’ credit ratings by using a fuzzy cluster analysis, as well as distinguishes the customer’s credit level by utilizing a fuzzy pattern recognition approach, which is helpful to evaluate and predict the customer’s credit level. Second, the proposed model predicts the credit rating of a new loan customer by utilizing the lattice degree of nearness between the center vector of each credit rating and the data vector of a new loan applicant. This seems to offer a new insight into the credit rating of customers. Third, by utilizing the microfinance data of 2,157 Chinese small private businesses, the empirical results indicate that our research is not only significant for assessing the credit status in China’s small private businesses, but also serves as a useful tool for worldwide customers’ credit ratings.
Keywords: Credit rating, fuzzy cluster, fuzzy pattern recognition, microfinance, small private business
