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中文摘要：
在调查和文献基础上，进行信用风险来源识别、评级指标分类和评级方法选择，构建商业银行内部信用评级模型，以期在授信审批环节提高信用风险管理水平．基于某商业银行2008-2013年小微企业实际信贷数据，运用线性判别分析、二项逻辑回归和十种基于不同学习算法的BP神经网络模型构建内部信用评级模型，并在评级指标体系中加入宏观经济变量，使度量风险的稳健性进一步得到提升．最后通过四种方法对不同模型的结果和评级有效性进行了对比分析，认为基于Levenbery-Marquardt学习算法的NN10模型具有最优的评级有效性．
英文摘要：
On the basis of investigation and literature research, conducting risk source identification, rating indicators classification and rating methods assessment, the paper constructs commercial bank＇s internal credit rating models to improve the credit risk management in the credit approval procedures. Based on the credit data of small and micro enterprises in a commercial hank from 2008 to 2013, using the linear discriminant analysis, logistic regression and 10 types of BP neural network relying on different learning algorithms, internal credit rating models are constructed with macroeconomic variables, which may further improve the robustness of risk measurement. Finally, results and rating effectiveness of different models are analyzed and compared, and show that the NN10 model based on Levenbery-Marquardt learning algorithm performs optimal rating effectiveness.
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