【摘要】 为了提高信用评分系统的预测准确性和稳定性,建立了基于反向传播(BP)算法神经网络的信用评分系统,并提出信用评分系统预测力和预测稳定性验证的新方法.结合信用评分问题的实际特点建立了模型并确定了参数,然后采用一种正向选入法确定输入变量,进行模型训练,并通过引入接收器操作特征曲线的分析理论、曲线面积(AUC)值及信息理论等评价方式,对所构造的神经网络信用评分系统预测力进行评价,最后利用自抽样法构造出多个验证样本来评估信用评分系统的稳定性.与传统的逻辑信用评分系统的比较结果表明,BP神经网络信用评分系统具有更高的预测准确性和稳定性,其AUC值平均提高0.036 7,AUC值的标准误差平均降低0.005.更多还原

【Abstract】 A credit scoring system based on BP(back propagation) neural network model is presented in order to improve the accuracy and stability of the prediction.A new method for evaluating the accuracy and stability of the system is also presented.Combining with the features of credit scoring problems,a neural network model is established and the parameters are determined.Then the input variables are decided by a forward selecting method to train the model.The prediction ability of the proposed credit scoring system is valuated through introducing theory of ROC(receiver operating characteristics) curves,AUC(area under curve) value and information theory,and compared to the traditional logistic credit scoring system.Finally,the checking samples are created by means of bootstrap method to validate the stability of the system.The experiment results show that the credit scoring system based on BP neural network is more accurate and stable than the logistic credit scoring system,and averagely,the AUC value is increased by 0.036 7 and its standard error is decreased by 0.005.更多还原