数据挖掘在我国保险业中的应用.doc

数据挖掘在我国保险业中的应用 摘要 数据挖掘是一项较新的数据库技术，它基于大量数据所构成的数据库，从中发现潜在的、有价值的信息称为知识，用于支持决策。数据挖掘是一项数据库应用技术，存在广泛的实际应用需求，因此，无论数据挖掘的理论研究，还是数据挖掘的应用实践，都是有意义的。本文首先分析了我国保险业中数据挖掘技术应用的现状，介绍数据挖掘理论，接着重点研究保险业中数据挖掘主题的选择与设计上。在国外已有研究基础上，结合我国保险业的实际需要，提出了我国保险业中的客户细分、客户流失、购物篮分析和交叉销售、客户满意度分析、客户忠诚度分析和客户响应度分析六个挖掘主题。通过医疗保险数据的实证分析，建立医疗保险业的客户 RC 细分模型、个人和团体客户理赔特征模型，利用 SPSS 公司的 Clementine 数据挖掘工具建立模型并进行验证与评价。关键词：保险，数据挖掘，医疗保险，模型AbstractTHE RESEARCH AND APPLICATION OFDATA MINING IN INSURANCEAbstract Data Mining is a newer database technique which aims atdiscovering potential and valuable pattern that is called as knowledge.Theknowledge discovered can be used for decision-making. Data Mining iswidely needed in practical field, therefor; either the theorical research orthe practice of Data Mining is significative.Firstly, we analyze application of data mining technology in domesticinsurance and introduce the theory of data mining technology. Secondly, Welay stress on the selection and design of the model of data mining in insuranceindustry. Six data mining pattern of domestic insurance operation are puttedforward: Customer Segmentation, Customer Churn Analysis, Market Basketand Cross-sell Analysis, Customer Satisfaction Analysis, Customer fidelityAnalysis, Customer responsibility Analysis on foreign research and thedomestic practice needs. Medical insurance is given as a demonstration todevelop a customer RC segment model、individual and group claim charactermodel of medical insurance operation and make use of Clementine datamining tools of SPSS, establish the model and then verify and appraise it.Key words: insurance, data mining, medical insurance, model数据挖掘技术在我国保险业中的应用研究 III目 录 第一章 绪论1一、研究的背景和意义1（一）研究的背景1（二）研究的意义2二、国内外应用现状2（一）国内应用现状3（二）国外应用现状3三、研究的重点、难点及其创新点3四、本文的研究方法和内容4第二章 数据挖掘理论6一、数据挖掘概念6（一）技术上的定义6（二）商业上的定义6（三）数据挖掘与统计方法区别7二、数据挖掘分类9（一）描述型数据挖掘9（二）预言型数据挖掘 11三、数据挖掘算法 12（一）神经网络 12（二）决策树 12（三）关联规则 15（四）聚类算法 15（五）遗传算法 17（六）粗集方法 17四、数据挖掘流程 17（一）目标定义 19（二）创建目标数据集 20数据挖掘技术在我国保险业中的应用研究 IV（三）数据预处理 20（四）数据转换 21（五）建立数据挖掘模型 23（六）评估与解释 23（七）采取行动 23五、数据挖掘的发展趋势 24第三章 保险业数据挖掘主题的设计 27一、客户细分 27（一）基于客户当前价值和潜在价值的细分 27（二）基于客户风险和贡献的细分 30二、客户流失分析 33（一）客户的流失预测 34（二）建立和维持客户关系、长期留住客户的关键点 35三、购物篮分析和交叉销售 36（一）购物篮分析 36（二）交叉销售分析 36四、客户忠诚度分析 37（一）保险客户忠诚度分类 37（二）保险客户忠诚度度量 38五、客户满意度分析 41六、客户响应度分析 42第四章 实证研究 44一、SPSS 公司的 Clementine 工具介绍44（一）性能概述 44（二）产品特点 46二、医疗保险数据处理流程 48（一）目标定义 48（二）创建目标数据集 48（三）数据预处理 49数据挖掘技术在我国保险业中的应用研究 V（四）数据转换 49三、团体客户RC细分模型 51四、团体客户理赔特征分析GR2模型54五、个人理赔风险特征分析IR2模型57六、采取行动 61第五章 结束语：总结与展望62致谢 63参考文献 64附录一 团单客户细分模型66附录二 团单客户理赔风险特征模型69附录三 个人客户理赔风险特征模型72个人简历 75在学期间的研究成果及发表的学术论文 75数据挖掘技术在我国保险业中的应用研究 VICatalogueChapter 1 Preface.11.1 Background and Meaning of Research11.1.1 Background of Research.11.1.2 Meaning of Research .21.2 Domestic and Foreign Current Situation of Application.21.2.1 Domestic Current Situation of Application21.2.2 Foreign Current Situation of Application.31.3 Focal point , Difficult Point and Innovation of Research.31.4 Method and Content of Research.4Chapter 2 Theory of Data Mining 52.1 Concept of Data Mining52.1.1 Concept in the Technology.52.1.2 Concept in the Business.52.1.3 Differentiation of Data Mining and Statistical Method.62.2 Classification of Data Mining.72.2.1 Descriptive Data Dining .82.2.2 Predicative Data Mining92.3 Algorithms of Data Mining .102.3.1 Kohonen Networks.112.3.2 Decision Trees112.3.3 Association Rules.132.3.4 Clustering Algorithms .142.3.5 Genetic Algorithms.152.3.6 Rough Set Theory.162.4 Process of Data Mining.162.4.1 Definition of Target.172.4.2 Construct Data Set of Target18数据挖掘技术在我国保险业中的应用研究 VII2.4.3 Handling Data Set182.4.4 Transformation of Data192.4.5 Construct Model of Data Mining.202.4.6 Evaluation and Explanation.212.4.7 Deployment212.5 Developing Trends of Data Mining.22Chapter 3 Thematic Design of Data Mining in the Insurance.243.1 Customer Segmentation.243.1.1 Customer Segmentation Based on Current Value andPotential Value243.1.2 Customer Segmentation Based on Risk and Contribution273.2 Customer Churn Analysis293.2.1 Predication of Customer Churn.303.2.2 Keypoint of Establishing and Maintaining CustomerRelationship and Retention.313.3 Market Basket and Cross-sell Analysis323.3.1 Market Basket Analysis.323.3.2 Cross-sell Analysis.323.4 Customer Fidelity Analysis.333.4.1 Classification of Customer Fidelity in the Insurance.333.4.2 measurement of Customer fidelity in the insurance.343.5 Customer Satisfaction Analysis.373.6 Customer responsibility Analysis.38Chapter 4 Empirical Research404.1 Introduction of Spsss Clementine Implement.404.1.1 Overview of Performance404.1.2 Characteristic of Product424.2 Process of Handling Data of Medical Insurance434.2.1 Definition of Target.434.2.2 Construct Data Set of Target.44数据挖掘技术在我国保险业中的应用研究 VIII4.2.3 Handling Data Set.454.2.4 Transformation of Data.454.3 RC Segmentation Model of Group Customer.464.4 Claim Character Analysis of Group CustomerGR2 Model.504.5 Claim Character Analysis of Group CustomerIR2