基于主成分分析法和BP神经网络的银行客户信用评价  

Evaluation on Bank Customer Credit Based on PCA and BP Neural Network

作　　者：刘春玲[1] 张焕生[1] 郝国芬[1] 贾冬青[1] 

机构地区：[1]河北工程技术高等专科学校,河北沧州061001

出　　处：《河北工程技术高等专科学校学报》2013年第4期38-41,共4页Journal of Hebei Engineering and Technical College Quarterly

摘　　要：为了加强商业银行对客户信用风险的事先控制,降低银行运营风险,需要对客户按信用等级进行分类,以便执行不同的信用风险控制策略。文中基于主成分分析法和BP神经网络法,建立了客户信用评价模型。结果表明,利用此信用风险评价模型能够准确地判断银行客户所处的信用等级,具有广泛的适用性。A research is given to determine the credit grade of commercial bank customers and classify the customers into different risk levels in order to enhance the prior controlling work for the commercial banks and to reduce the operating risk. A model of customer credit risk of commercial banks evaluation is established based on principal component analysis method（PCA）and BP neural network. The result shows that this model can be adopted in the credit risk assessment since it could give an accurate judgment of risk management. It has wide applications.

关 键 词：商业银行 信用风险 主成分分析 BP神经网络 

分 类 号：F830.5[经济管理—金融学]