数据挖掘算法在银行理财产品营销中的应用研究

发布时间：2018-05-15 17:03

本文选题：数据挖掘 + 关联规则　；参考：《郑州大学》2013年硕士论文

【摘要】：如何将数据仓库及数据挖掘的相关技术运用到银行金融产品的规划与销售中,是目前我国金融行业较为迫切需要研究的领域。该领域研究的内容包括数据挖掘技术的研究、更加有效的挖掘算法设计、客户关系管理系统的重新构建等方面。本文具体探讨了银行理财产品销售分析系统在实施过程中的若干关键技术,同时提出了一种有效挖掘负关联规则的方法。银行客户的众多行为中,存在着正、负关联规则。传统的关联规则算法仅反应了正项之间的关联关系,无法解决负关联的问题。本文提出了一种有效的算法(GA_PNAR),用以解决银行客户行为负关联的问题。GA_PNAR算法首先利用Apriori算法生成频繁项集,之后利用基于相关系数的NRGA算法生成含有所有负项的关联规则,在所有规则生成后,利用遗传算法优选生成的规则。GA_PNAR算法是一款非常有前途的发现规则的方法。当前针对银行营销的方法主要考虑的是客户的基本属性,没有全面考虑客户的价值属性以及行为属性。营销方案的设计也主要是对客户进行细分,通过对客户的基本属性如投资期限、风险性偏好等进行聚类分析。根据聚类分析的结果,将客户的聚类特征与理财产品的特征结合,为客户提供理财方案。在这种方案中客户的投资期限、风险性偏好等属性往往通过测试获得,存在很大的不准确性和失真。本文将GA_PNAR方法应用于银行客户-产品之间关联规则的发现,该过程选取的客户数据主要是客户行为属性。相比常规的聚类分析,客户-理财产品关联规则分析,能够为银行客户提供更加精确、专业化的理财产品指导。另外,在该模型的数据预处理阶段采用了云模型的方法对数值型属性进行了概念化分层,该方法可以有效地解决数值分层的模糊性问题。最后,本文提出了一个银行理财产品营销系统的设计方案。论文对于国内金融行业实施结构化数据挖掘技术、部署企业级数据仓库、完善客户分类、加强客户关系管理、市场销售分析、金融产品规划、市场需求动态分析等各个方面均有一定的借鉴和现实指导意义。
[Abstract]:How to apply the related technology of data warehouse and data mining to the planning and sale of bank financial products is the most urgent need to be studied in our financial industry. The research in this field includes the research of data mining technology, the more effective mining algorithm, the re construction of the customer relationship management system and so on. In this paper, some key technologies in the implementation process of bank financial products sales analysis system are discussed, and an effective method for mining negative association rules is proposed.
In many behavior of bank customers, there are positive and negative association rules. The traditional association rule algorithm only reflects the relationship between positive items and can not solve the problem of negative correlation. This paper proposes an effective algorithm (GA_PNAR) to solve the negative association of bank customer behavior. The.GA_PNAR algorithm is first generated by the Apriori algorithm. Frequent itemsets, then use the NRGA algorithm based on correlation coefficients to generate association rules containing all negative items. After all rules are generated, the rule.GA_PNAR algorithm generated by genetic algorithm is a very promising method of discovering rules.
At present, the main consideration of the method of banking marketing is the basic attribute of the customer. It does not fully consider the value attribute and the behavior attribute of the customer. The design of the marketing plan is mainly to subdivide the customer, and through the clustering analysis of the basic attribute of the customer, such as the term of investment and the risk preference, etc., according to the result of the cluster analysis, The customer's clustering characteristics and the characteristics of financial products are combined to provide a financial plan for customers. In this scheme, the time limit of the customer's investment and the risk preference are often obtained by testing, and there is a lot of inaccuracy and distortion. This paper applies the GA_PNAR method to the discovery of Association rules between customers and products of the bank. Customer data are mainly customer behavior attributes. Compared to conventional clustering analysis, customer financial product association rules analysis can provide more accurate and professional financial product guidance for bank customers. In addition, a cloud model is used in the data preprocessing stage of the model to conceptualize the numerical attributes. The method can effectively solve the fuzziness of numerical layering. Finally, this paper proposes a design scheme for the marketing system of bank financial products.
This paper has a certain reference and practical significance for the domestic financial industry to implement structured data mining technology, deploy enterprise data warehouse, improve customer classification, strengthen customer relationship management, market sales analysis, financial product planning, market demand dynamic analysis and so on.

【学位授予单位】：郑州大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP311.13

【参考文献】