Research Of Small-sized Enterprises Credit Risk Management Model Based On Fuzzy Analytical Hierarchy Process

This paper's theme is commercial bank's credit risk management for small-sized enterprises. The reason to study this subject mainly is the characteristics of the current economic development stage: our economy is in a socio-economic transition. Non-public economy has been developing rapidly. The financing demand of small-sized enterprises, mostly belong to non-public enterprises which are not only numerous but also are increasing, is very strong; however, because of the imperfection of social credit system, the commercial banks take a great risk by lending to small-sized enterprises. In addition, the Zhejiang private economy is typical and representative in our country. This is a practical problem which is failure to solve in long-term time for Zhejiang's commercial bank to give small-sized enterprises credit. How to improve the credit for small-sized enterprises is of great theoretical significance and practical guide.This paper firstly describes some theory about credit risk management of commercial banks, and then discusses the research status about credit both of national and international; and then analyzes the characteristics of China's small-sized enterprises credit and financing situation; and then by combining the theory with practice and using AHP-based systems engineering methods, attempts to establish a credit rating model system of small-sized enterprises; finally, makes a case study and reaches a conclusionThis paper introduces the fuzzy AHP from engineering system into the credit risk management of commercial bank. It restores the fuzzy nature of credit rating and uses an analysis method applicable to a combination of qualitative and quantitative ratings issues. We can reduce the unfavorable factors caused by traditional expert method as far as possible. The method based on qualitative analysis carries out the multi-level evaluation mathematically and quantitatively. It can not only consider qualitative and quantitative indicators at the same time, but also make the assessment more scientific, accurate and reasonable to improve the management of credit assets and prevent the occurrence of adverse credit.