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Classification Of Loan Risk-based Intelligent Knowledge Management

Posted on:2003-09-23Degree:DoctorType:Dissertation
Country:ChinaCandidate:M ZhuFull Text:PDF
GTID:1116360095953841Subject:Control theory and control engineering
Abstract/Summary:PDF Full Text Request
The credit risk management is a permanent topic in commercial bank to which has been paid attention by many foreign governments especially after the Asia Finance crisis' broken out. Our government also has clearly asked the commercial bank to improve the quality of credit assets and prevent credit risks. Meanwhile our government has advocated carrying out the study of loan risk management by modern information technology. To strengthen the financial supervision, "The principle of loans risk classification" was published in 1998 and the real risk management is carried out in china. According to the requirement, the paper combined with the task-"the research of loans risk management based on the integration of ANN (Artificial Neural Network) and ES (Expert System)" which is supported by National Science Foundation of China. Set forth using AI (Artificial Intelligence) technology on the basis of Knowledge Management to research the loans risk management and control.Through the study of the style of knowledge description, the paper elaborates the numerical model knowledge, the symbolic experience knowledge and the instantial swatch knowledge. These three kinds of different knowledge have been used in loans risk classification in the paper. Just as the using of the Object Oriented technique and the AI (include ES, ANN, and the integrated of ANN and ES) technique, can the paper realize the five-grade loans risk classification.In this paper, the study content and the concretely achievement are as follows:1. Unifying the definitions of information and knowledge on the layer of epistemology, we put forward the concept of knowledge management. According to human's knowledge structure, we divide knowledge by three kind of description. Such interchange, permeation among the descriptions can make the knowledge to be shared together and enforce the reuse ability of knowledge. These knowledge descriptions can be used in the model of loans risk classification.2. On the analysis of financial affairs in classifying loans risk, bank has large of finance report forms samples. It can make correct judge by calculating various rates and comparing them with other enterprise. So the bank has the numerical model knowledge and the instantial swatch knowledge. As neural network has the features of fault-toleration, robust and self-learning, it can acquire the knowledge hidden in the samples through learning. Thus, we provide a kind of method realized by VC++ to analyze the ability of paying back loans of enterprises based on BP neural network. In the method, we have discussed the pretreatment of financial indexes, the choice of the number of hidden-unit, the initial weight and so on, and got a 17 × 11 × 5 topology structure. The results have proved that this method is more effective than usual financial analysis.3. In the loans risk classification, non-financial affairs belong to typical symbolic knowledge. As Knowledge-Based System (KBS) is such a calculating procedure utilizing knowledge and reckon to resolve the questions only can be resolved by human specialists that it is has advantages other procedures do not have to treat symbolic knowledge. It can reduce searching complexity by enlightening knowledge. We have analyzed the structure of non-financial affairs, established system dependency diagram and designed the Knowledge Base. At last we have built up the non-financial affairs prototype system based on KBS by VC++.4. Integrating ANN with ES, we have brought about the change between the symbolic knowledge and the instantial knowledge. The importance of rule extraction from trained ANN is that of using the ANN for the 'Learning' of impliedly rules within swatch knowledge, and then expressing with rules. The goal in rule refinement is to use combination of ANN learning and rule extraction techniques to produce a 'better' set of symbolic-rules which can then be applied back in the original problem domain. In the rule refinement process, the initial rule base is inserted into an ANN by programming some of the...
Keywords/Search Tags:Knowledge Management (KM), Artificial Intelligence (AI), Knowledge-Based System (KBS), Artificial Neural Network (ANN), Loans Risk Classification
PDF Full Text Request
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