基于数据挖掘的商业银行个人信用风险评估平台设计与实现-毕业论文文档.doc

毕业设计(论文) 论 文 题 目： 学 生 姓 名： 学 生 学 号： 专 业 班 级： 学 院 名 称： 指 导 老 师： 学 院 院 长： 201年5月日 Design and Implementation of Commercial Banks Credit Risk Assessment Based on Data Mining Abstract The development of computer technology and information technology bring us convenience, but also brought a series of problems, one of the problems is the relationship between the amount of data as well as the explosive growth of data between the more and more complex, and how these massive data processing discover hidden potential data theoretic value and practical value has become the focus of attention in all areas of life. As Chinas economy continues to develop, income and consumption level of urban and rural residents has significantly improved, consumer credit business has become one of the main business of commercial banks, commercial banks in China but there is a lack of personal credit risk assessment. Therefore, studying how to use data mining techniques to analyze customer credit risk from the banks existing customer data, has important theoretical and practical significance. Firstly, the concept, development status data mining are introduced. Secondly, the data mining algorithms are introduced, we analyzed the system will use data mining algorithms. Then, combined with problems encountered by commercial banks in customer credit risk assessment needs analysis. On this basis, the proposed individual credit risk assessment model based on BP neural network and decision tree algorithm based commercial bank, and the proposed model has been improved and validated commercial bank personal credit risk provides a feasible solution program evaluation. Key Words: Data mining; BP neural network; decision tree; Credit Risk  目录 第1章 绪论 1 1.1研究背景和研究意义 1 1.1.1研究背景 1 1.1.2研究意义 3 1.2国内外研究综述 4 1.2.1数据挖掘研究现状 4 1.2.2商业银行信用风险研究现状 6 1.3论文的主要工作和内容结构 7 第2章 数据挖掘算法及相关技术 9 2.1数据挖掘的概念 9 2.2数据挖掘的过程 9 2.3数据挖掘的常用算法 10 2.3.1人工神经网络 11 2.3.2决策树 11 2.3.3遗传算法 12 2.3.4近邻算法 13 2.3.5 k-means算法