银行客户的信用评级模型1

目录

内容摘要．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．I Abstract．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．

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II 1 导言．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 1 1.1 研究背景．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．

1 1.

2 国内外研究现状．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 1 1.

3 研究目的和意义．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 3

1.4 研究思路及框架．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 3

2 银行客户信用评级理论．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 4 2.1 银行客户信用评级基本理论．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．． 4 2.2 我国银行信用评估现状．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．

6 3 银行客户的信用评级模型实例研究．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．8 3.1 数据处理与分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．8 3.2 列联表分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．

9 3.3 判别分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．12

3.4 Logistic回归分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．16

4 结论．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．20 4.1 研究结果．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．20 4.2 研究不足与展望．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．21 参考文献．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．22

附录1：Logistic回归对应R软件代码．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．2 3

附录2：原始数据的前50个数据．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．24 致谢．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．27

内容摘要：随着经济的发展以及人民消费水平和观念的改变，个人信贷的规模逐渐扩大，消费贷款业务随之迅速发展，个人信用风险受到了商业银行和监护者越来越多的关注。但相对于企业信用风险评估，个人信用风险评估显得尤其薄弱，多数银行基本依靠信贷人员的经验和定性分析来决定，面对业务量的不断增长，信贷人员相对不足，传统的授信措施造成了审批时间长，失误上升，服务水平和管理风险的能力下降，导致银行失去客户和潜在客户，不利于银行竞争力的提高。尤其近年来，网上银行的不断发展，更是对传统银行带来了不可控的冲击，因此，构建科学合理的个人信用评估模型显得尤为重要。

本文综合考虑了国内外信用风险评估的研究现状以及研究成果，通过列联表对定性数据进行关联分析，并且运用判别分析对定量数据进行分类判别，再通过SPSS 软件进行数据处理，确定相应的判别函数，得出各个解释变量对判别分类的影响，评估的结果可以作为银行是否提供贷款的依据。另外，运用R软件进行Logistic回归，根据数据分析处理结果，确定回归系数，评估预测模型，进而运用到实际的信贷中，降低了银行的信贷风险，保障银行的长久健康发展。

关键词：信用风险评估；SPSS；判别分析；R；Logistic回归

Abstract:With the development of economy and people's consumption level and the change of the concept,Personal credit scale expands gradually, consumer loan business with rapid development of personal credit risk by commercial Banks and care more and more attention. But relative to the enterprise credit risk evaluation, personal credit risk assessment is particularly weak, most basic rely on bank credit personnel experience and qualitative analysis to determine, in the face of the growing business, relatively insufficient credit officers, the traditional credit measures caused a long time of examination and approval Error, service level and the ability to manage risk, Banks lose customers and potential customers, does not favor the bank's ability to compete. Especially in recent years, the continuous development of online banking, but also the impact on the traditional bank brings uncontrollable, as a result, build a scientific and reasonable personal credit evaluation model is particularly important.

Comprehensive consideration of the current research of credit risk assessment at home and abroad and the research results, through correlation analysis, contingency table for qualitative data and the use of discriminant analysis for quantitative data classification criterion, again through the SPSS software for data processing, determine the corresponding discriminant function, it is concluded that the explanatory variables influence on discriminant classification, the results of the assessment can be used as the basis of a bank loan. In addition, the logistic regression using R software, according to the results of data analysis, determine the regression coefficient, assess the prediction model, and then applied to the actual credit, reduce the bank's credit risk, safeguard the bank's long-term health development.

Key Words: Credit risk assessment SPSS Discriminant analysis R Logistic regression