【作者】 叶凯；

【导师】 王树林；

【作者基本信息】 哈尔滨工业大学 ， 国际贸易学， 2006， 硕士

【摘要】 随着中国经济的快速发展,各种个人消费贷款的规模迅速扩大。但是,由于目前国内商业银行对零售业务的风险管理水平较低,管理手段与方法相对落后,缺乏一套有效的个人信用评估方法,这严重阻碍了个人消费信贷业务的发展。因此,开发出一套适合于中国特征的,能够有效降低信用风险的个人信用评估方法,对社会经济的发展具有十分重要的意义。本文运用国内外银行最经常使用的判别分析、Logistic回归、人工神经网络3种方法对收集的数据资料进行处理,并且在对结果进行分析比较的基础上,建立一种新的信用评估方法——个人信用混合两阶段评估方法。即将个人信用评估模型的建立分成两个阶段,在第一阶段通过对判别分析、Logistic回归、人工神经网络3种方法的单独应用所得结果的分析和比较,得到人工神经网络模型的拟合精度最优,Logistic回归预测的稳健性最优的结论,作为第二阶段建立二者混合模型的基础。第二阶段利用Logistic分析建立的模型输出预测结果---概率P,并将P作为新增变量和其他自变量一起作为神经网络的输入,利用先验知识对人工神经网络加以提示,来优化神经网络。由此得到了预测稳健性得到改善的更有效的信用评估模型。这种个人信用混合两阶段评估方法可以有效降低商业银行的信用风险,更好地实现银行利润最大化的目标。更多还原

【Abstract】 With the rapid development of Chinese economy, the scale of various personal consume expands quickly. But, because of the low risk management level over the retail trade from the interior commercial banks, relatively backward management means and methods, lack of an effective personal credit evaluation method, all severely hindered the development of credit business of personal consume. Therefore, it is very important for the development of social economy to develop an evaluation method of personal credit, which is suitable for the Chinese character and can effectively lower the credit risk.The paper uses the three methods that are judge and analysis, logistic return and artificial nerve network, which are often used in banks home and abroad, to handle the collected data and form a mixed two-stage evaluation method of personal credit based on the result from the analysis and comparison of the data, that is to divide the personal credit evaluation model into two stages. At the first stage, such conclusions are made that the imitation and synthesis accuracy of artificial nerve network and the predict stability of logistic return are the best through the analysis and comparison of the results from the separate application of the three methods: judge and analysis, Logistic return and artificial nerve network, which is the basement of mixture model of the two in the second stage. The second stage uses the output based on the Logistic analysis model to predict the result---Rate P, puts Rate P as a new variable into the nerve network with other independent variable and gives directions to the artificial nerve network to optimize nerve network. At last we get a more effective credit evaluation model from the improvement of model of imitation and analysis accuracy and predict stability. This research on the mixed two- stage evaluation method of personal credit can reach the goal, that is to effectively lower the credit risk of commercial banks and realize maximize of the bank profits.更多还原