个人理财规划决策系统
摘要
随着中国经济的快速增长,人民物质生活水平显著提高,理财已经成为人们经济活动的重要的组成部分.但由于这个新兴的行业尚处起步阶段,银行等金融机构提供的理财规划建议缺乏统一的标准,分析结果不准确,直接影响客户的投资方向和细节,甚至会误导客户。为此,需要一套科学规范、高效制式的理财规划决策系统来解决上述问题。
本人根据农行天津港保税区分行个人金融部的实际业务需求,用了历时5个月的时间分析了理财规划决策工作流程和2000多个客户数据,采用b/s模式,应用现有的Webshpere平台开发工具和农业银行统一的Sybase数据库,利用农行天津分行现有的经营管理网框架,独立设计了本套系统。为了提高设计和使用的灵活性,重视界面、操作以及对应模型的选择,此系统应用MVC模式将原业务分解成8个模型,8个视图和4个控制。此外,本系统应用了数据挖掘技术中的关联规则,由频繁项生成算法(Apriori)实现完成。最后利用农行天津港保税区分行个人理财中心的十套个人客户报告,结合注册理财规划师的建议,去试验系统的分析决策能力。最后,本文在算法和系统设计的基础上,对系统功能进行了实现,开发了个人理财规划决策系统,并结合实例对系统的应用效果进行了描述。
此系统在农行统一的内部网平台上开发,与其它各套生产系统与办公系统接口标准一致,提高开发效率。此系统开发完成后可以极大地提高农行个人客户经理对客户的服务层次,准确及时地为客户提供理财建议,轻松实现个人客户的理财目标。过去手工用一周完成的工作量,应用此系统在一个小时之内就可完成提高了效率,节省了成本,提高了客户的忠诚度。
Along with the fast growing of Chinese economy, the level of people material life is improved remarkably; financing has been becoming the important constituent of the people economic activity. But as this new profession still stick around the start stage, the financing plan which suggested by banks and other financial organs lacked the united standard, resulted the inaccurate analysis, which affected the investment trends and details directly, even could mislead the customer’s decision. Therefore, a set of more scientific, highly effective financing decision system has been needed urgently to solve the above problems.
According to the actual business demands in the personal financial department of Agricultural Bank of China Tianjin Port Free Trade Zone branch, by using the b/s pattern, adopting the webshpere platform and the Sybase database of ABC, utilizing the current framework of operation and management network, I analyze the financial plan, the decision-making workflow and the data of more than 2000 customers within five months, and then design this system individually. In order to improve the designing flexibility, pay attention to the interface, operation, and the choice of corresponding model, the original business has been divided into eight models, eight view-windows and four masters by using MVC pattern. Moreover, exercised the associated rules from data mining techniques, this system is realized by the frequency algorithm (Apriori). Finally, testing the ability of analysis and decision, we use ten sets of individual reports, which are draw from the Individual Financing Center of the Agricultural Bank of China Tianjin Free Trade Zone branch, and the suggestions of registered financiers. Finally, in the algorithm and the system designing foundation of this article, this system has carried on the realization of the system function, developed the individual financing plan and decision system, and carried on the description to the system practical effect ion unified the example.
Developed on the unified intranet platform of the ABC, the system has been consistent with the standard of the production system and the work system; all of the technologies are prepared for enhancing developing efficiency. Provided by individual customer managers, the customer’s service level can be improved enormously after the totally developed system, moreover, the financing suggestions to the customers can be provided more prompt and accurate, the individual target even can be reached easily. The work load which completed manually with a week in the past, but can be completed within an hour upon on this system, not only improving efficiency but also saving cost, and enhancing the customers’loyalty.
本人根据农行天津港保税区分行个人金融部的实际业务需求,用了历时5个月的时间分析了理财规划决策工作流程和2000多个客户数据,采用b/s模式,应用现有的Webshpere平台开发工具和农业银行统一的Sybase数据库,利用农行天津分行现有的经营管理网框架,独立设计了本套系统。为了提高设计和使用的灵活性,重视界面、操作以及对应模型的选择,此系统应用MVC模式将原业务分解成8个模型,8个视图和4个控制。此外,本系统应用了数据挖掘技术中的关联规则,由频繁项生成算法(Apriori)实现完成。最后利用农行天津港保税区分行个人理财中心的十套个人客户报告,结合注册理财规划师的建议,去试验系统的分析决策能力。最后,本文在算法和系统设计的基础上,对系统功能进行了实现,开发了个人理财规划决策系统,并结合实例对系统的应用效果进行了描述。
此系统在农行统一的内部网平台上开发,与其它各套生产系统与办公系统接口标准一致,提高开发效率。此系统开发完成后可以极大地提高农行个人客户经理对客户的服务层次,准确及时地为客户提供理财建议,轻松实现个人客户的理财目标。过去手工用一周完成的工作量,应用此系统在一个小时之内就可完成提高了效率,节省了成本,提高了客户的忠诚度。
Along with the fast growing of Chinese economy, the level of people material life is improved remarkably; financing has been becoming the important constituent of the people economic activity. But as this new profession still stick around the start stage, the financing plan which suggested by banks and other financial organs lacked the united standard, resulted the inaccurate analysis, which affected the investment trends and details directly, even could mislead the customer’s decision. Therefore, a set of more scientific, highly effective financing decision system has been needed urgently to solve the above problems.
According to the actual business demands in the personal financial department of Agricultural Bank of China Tianjin Port Free Trade Zone branch, by using the b/s pattern, adopting the webshpere platform and the Sybase database of ABC, utilizing the current framework of operation and management network, I analyze the financial plan, the decision-making workflow and the data of more than 2000 customers within five months, and then design this system individually. In order to improve the designing flexibility, pay attention to the interface, operation, and the choice of corresponding model, the original business has been divided into eight models, eight view-windows and four masters by using MVC pattern. Moreover, exercised the associated rules from data mining techniques, this system is realized by the frequency algorithm (Apriori). Finally, testing the ability of analysis and decision, we use ten sets of individual reports, which are draw from the Individual Financing Center of the Agricultural Bank of China Tianjin Free Trade Zone branch, and the suggestions of registered financiers. Finally, in the algorithm and the system designing foundation of this article, this system has carried on the realization of the system function, developed the individual financing plan and decision system, and carried on the description to the system practical effect ion unified the example.
Developed on the unified intranet platform of the ABC, the system has been consistent with the standard of the production system and the work system; all of the technologies are prepared for enhancing developing efficiency. Provided by individual customer managers, the customer’s service level can be improved enormously after the totally developed system, moreover, the financing suggestions to the customers can be provided more prompt and accurate, the individual target even can be reached easily. The work load which completed manually with a week in the past, but can be completed within an hour upon on this system, not only improving efficiency but also saving cost, and enhancing the customers’loyalty.
引文
[1]黄浩,个人理财概念及理论浅谈,科技情报开发与经济,2005年,05,16~19
[2]赵立航,我国个人理财服务的历史发展,上海经济研究,2007年,03,25
[3]华泰保险学院,个人理财六步走[EB/OL],http://www.huatailife.com/html/2008/01/2008012461100.html ,2008
[4]张洪斌等编著,WebSphere Server平台的J2EE实例开发指南,北京:机械工业出版社,2006,155~190
[5] Marty Hall,Larry Brown著赵学良译,Servlet与JSP核心编程(第2版),北京:清华大学出版社,2004,46~70
[6] IBM, struts MVC的一种开放源码实现[EB/OL],http://www.ibm.com/developerments/cn/java/j-struts/#6 ,2007
[7]数据挖掘讨论组,数据挖掘入门,http://datamining.126.com
[8]郭世亮,数据仓库的设计与优化,计算机世界报,2002,13,21~25
[9]何炎祥、彭锋、李世平、宋文欣,分布式数据开采研究,小型微型计算机系统,2001, 22, 2, 191~194
[10]夏红霞等,银行数据仓库系统的设计,计算机应用,2002,3,12~13
[11] Gray Craig Peter Jakab著,张云涛龚玲张燕译IBM WebSphere Studio Web应用开发,北京:机械工业出版,2004.2,120~139
[12] Jim Arlow Ila Neustadt著,方贵宾胡辉良译, UML 2.0和统一过程(原书第2版),北京:机械工业出版社,2006.6,68~110
[13]中国农业银行,Pcrm农行个人优质客户系统使用手册,北京:农行总行,2007,15~19
[14]晓函,数据仓库在银行经营管理中的作用,海脉网络经济周刊,2000,9,14,18~25
[15]何炎祥、张戈、石莉、李超、黄浩,关联规则的维护技术,计算机工程与应用,2002,10,72~79
[16]孙智著,理财规划建议书案例集(内部资料),2007,6~52
[17] Huang z.Extensions to the k-means algorithm for clustering large data sets with categorical values[J].Data Ming and Knowledge Discovery,1998.2:283~304.
[18] HU Cai-Ping, QIN Xiao-Lin, A Survey of Spatial Data Mining Research[J], Computer Science ,34,5, 2007
[19] S.Thirumurugan, L.Suresh, Statistical Spatial Clustering using Spatial Data mining[A], IEEE[C]. 2008
[20] Jeffrey Hsu. Data Mining Trends and Developments: The Key Data Mining Technologies and Applications for the 21st Century [A], In Proc of the 19th Annual Conference for Information Systems Educators [C], Texas, USA, 2002, V19: 224b.
[21] Sanjay Chawla, Shashi Shekhar, Weili Wu, Uygar Ozesmiy, Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest Locations[J], ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 2000: 70-77.
[22] Jiawei Han, Krzysztof Koperski, Nebojsa Stefanovic, GeoMiner: A System Prototype for Spatial Data Mining [A]. SIGMOD Conference[C], 1997: 553-556.
[23] Wang Lizhen,Xie Kunging,Chen Tao,et al. Efficient discovery of multilevel spatial association rules using partitions[J].Information and Software Technology,2005,47:829-840
[2]赵立航,我国个人理财服务的历史发展,上海经济研究,2007年,03,25
[3]华泰保险学院,个人理财六步走[EB/OL],http://www.huatailife.com/html/2008/01/2008012461100.html ,2008
[4]张洪斌等编著,WebSphere Server平台的J2EE实例开发指南,北京:机械工业出版社,2006,155~190
[5] Marty Hall,Larry Brown著赵学良译,Servlet与JSP核心编程(第2版),北京:清华大学出版社,2004,46~70
[6] IBM, struts MVC的一种开放源码实现[EB/OL],http://www.ibm.com/developerments/cn/java/j-struts/#6 ,2007
[7]数据挖掘讨论组,数据挖掘入门,http://datamining.126.com
[8]郭世亮,数据仓库的设计与优化,计算机世界报,2002,13,21~25
[9]何炎祥、彭锋、李世平、宋文欣,分布式数据开采研究,小型微型计算机系统,2001, 22, 2, 191~194
[10]夏红霞等,银行数据仓库系统的设计,计算机应用,2002,3,12~13
[11] Gray Craig Peter Jakab著,张云涛龚玲张燕译IBM WebSphere Studio Web应用开发,北京:机械工业出版,2004.2,120~139
[12] Jim Arlow Ila Neustadt著,方贵宾胡辉良译, UML 2.0和统一过程(原书第2版),北京:机械工业出版社,2006.6,68~110
[13]中国农业银行,Pcrm农行个人优质客户系统使用手册,北京:农行总行,2007,15~19
[14]晓函,数据仓库在银行经营管理中的作用,海脉网络经济周刊,2000,9,14,18~25
[15]何炎祥、张戈、石莉、李超、黄浩,关联规则的维护技术,计算机工程与应用,2002,10,72~79
[16]孙智著,理财规划建议书案例集(内部资料),2007,6~52
[17] Huang z.Extensions to the k-means algorithm for clustering large data sets with categorical values[J].Data Ming and Knowledge Discovery,1998.2:283~304.
[18] HU Cai-Ping, QIN Xiao-Lin, A Survey of Spatial Data Mining Research[J], Computer Science ,34,5, 2007
[19] S.Thirumurugan, L.Suresh, Statistical Spatial Clustering using Spatial Data mining[A], IEEE[C]. 2008
[20] Jeffrey Hsu. Data Mining Trends and Developments: The Key Data Mining Technologies and Applications for the 21st Century [A], In Proc of the 19th Annual Conference for Information Systems Educators [C], Texas, USA, 2002, V19: 224b.
[21] Sanjay Chawla, Shashi Shekhar, Weili Wu, Uygar Ozesmiy, Extending Data Mining for Spatial Applications: A Case Study in Predicting Nest Locations[J], ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 2000: 70-77.
[22] Jiawei Han, Krzysztof Koperski, Nebojsa Stefanovic, GeoMiner: A System Prototype for Spatial Data Mining [A]. SIGMOD Conference[C], 1997: 553-556.
[23] Wang Lizhen,Xie Kunging,Chen Tao,et al. Efficient discovery of multilevel spatial association rules using partitions[J].Information and Software Technology,2005,47:829-840