关于某银行借贷客户群体分类的实证分析

发布时间：2019-03-15 08:01

【摘要】：银行客户向银行提出贷款申请,银行应对不同的客户群体进行区别决策,其获得的收益也有所不同,于是银行的满意程度也就不同。所以银行要建立合适的信贷模型去评估借贷的风险,充分了解其客户群体的类型以及其借贷需求。本文主要是关于某银行基本面信贷风险定量的实证分析,文章基本上介绍了 logistic regression模型和神经网络模型的应用范围、假设条件、应用条件、模型的常见分类以及假设检验和回归系数的解释、模型参数的意义。本文案例叙述了 12万客户在某银行的贷款记录,通过建立logistic模型和神经网络模型观察并去寻找符合银行要求并需要贷款的潜在客户群体。本文从统计分析的角度,量化地分析了某银行客户群的分类。本文的结构主要为四个部分。第一部分主要是关于本文研究的主要内容、目的、意义以及国内外文献综述;第二部分是关于logistic模型理论的概括,介绍了 logistic模型的基本原理、应用范围、分类、应用和假设条件、数学模型以及参数估计和检验标准,模型解释等;第三部分仍然是关于logistic模型的解释,主要是通过对logistic 回归模型的建模解释本文案例的实际意义;第四部分我们通过对神经网络模型原理的介绍和神经网络的建模过程,揭示了该案例中银行客户群体的具体分类情况;第五部分是两个模型的比较与总结。
[Abstract]:Bank customers apply for loans to banks, banks should make different decisions on different customer groups, and their income is different, so the degree of satisfaction of banks is different. Therefore, banks should establish appropriate credit models to assess the risk of borrowing and fully understand the types of their clients and their lending needs. This paper is mainly about a bank fundamental credit risk quantitative empirical analysis, the article basically introduced the logistic regression model and neural network model of the scope of application, assumptions, application conditions, The common classification of model, hypothesis test and interpretation of regression coefficient, the significance of model parameters. This paper describes the loan records of 120000 customers in a bank. Through the establishment of logistic model and neural network model, we observe and find out the prospective customers who meet the bank's requirements and need loans. From the angle of statistical analysis, this paper quantitatively analyzes the classification of a bank's customer group. The structure of this paper is mainly divided into four parts. The first part is mainly about the main content, purpose, significance and domestic and foreign literature review; The second part is a summary of the theory of logistic model, which introduces the basic principle, application scope, classification, application and hypothesis condition, mathematical model, parameter estimation and test standard, model interpretation and so on. The basic principle, application scope, classification, application and hypothesis condition of logistic model are introduced. The third part is still about the interpretation of the logistic model, mainly through the modeling of the logistic regression model to explain the practical significance of this case; In the fourth part, we introduce the principle of the neural network model and the modeling process of the neural network to reveal the specific classification of the bank customer group in this case; the fifth part is the comparison and summary of the two models.
【学位授予单位】：华中师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：C815

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