Hybrid Expert System For Enterprise Credit Evaluation Study

Credit assessment is the evaluation of the likelihood for enterprise to repay their loans and interests. It is very important and valuable to evaluate enterprise properly with the assistance of credit assessment models. However,including both quantitative and qualitative analysis,credit assessment is a rather complicated procedure in which many aspects should be considered. Thus,traditional credit assessment models,such as financial ratio analysis,multiple discriminate analysis and so on,cannot solve this problem effectively,completely and perfectly.With the development of AI,there are scholars who apply neural network to credit assessment and have already got some promising results. But in practice,neural network can still not illustrate the whole credit assessment process perfectly. In this paper,a new method based on mixed-expert system is introduced. In the prototype system constructed in this paper,we use rule-based expert system to deal with qualitative factors and neural network to trait quantitative data. Thus,a mixed-expert system is more suitable to the enterprise credit assessment.This work is contributed to the design and the construction of the traditional expert system part of the mixed system. Firstly,we deal with sample data provided by a bank in Fujian and abstract financial ratios by statistical methods;secondly,we construct knowledge base according to area-knowledge of credit assessment,and design a priority-based forward-inference engine and a fact-based automatic explaining mechanism;thirdly,we adopt object-oriented technology to analysis the system and construct object model and functional model of expert system;finally,by using ACCESS database to construct system knowledge base,and C++ Builder software to program,we develop a credit assessment prototype system based on mix-expert system. The experimental results show that the system constructed by this method has the virtues of robustness,flexibility,explicable ability and self-study capability,since the new method has got both the merits of expert system and of neural network. The content of this paper is arranged as following:Chapter 1 introduces the concept of credit,credit risk and credit assessment,as well as the history and development of credit assessment;Chapter 2 introduces the history of AI technology,and the background of expert system and neural network. Characters and disadvantages of expert system and neural network are presented respectively and the necessity of combining expert system and neural network is lightened;Chapter 3 shows the process of dealing with sample data,including the treatment of exceptional data and factor analysis,and puts forward the concrete framework of the mixed-expert credit assessment system;Chapter 4 introduces concept of object-oriented technology,and constructs object model and functional model after analyzing the whole system. It also illustrates the implementation of concrete classes by an example of rule class and the inference algorithm in the form of pseudocode;Chapter 5 introduces the structure of the whole system,the major functional models and their interfaces,and the characteristic of the system is also generalized;Chapter 6 summarizes the whole work,and points out the remaining deficiencies as well as the prospective of this method.