Study On Mode Between Contribution Degree Of Bank Customers And Transaction Behavior Based On Big Data

Banking is the main body of China’s financial system,the current financial industry to accelerate market-oriented reforms,integrated financial markets,pattern of multi-agent,multi-level,multi-field gradually formed.Private financial and industrial capital,finance new form of cross-border Internet competition is intensifying to form a new financial ecosystem.Big data,cloud computing,Internet,the rapid development of Internet of Things technology has greatly changed the financial channels and tools.Driving new technologies,customer business model,development model and financial needs will be sustained and profound changes.Banking services cover a wide range.This paper considers the decision tree C5.0 algorithm to deal with the advantages of discrete data according to the two branches of a bank in a transaction data,in the theory of data and data mining methods.C5.0 decision tree algorithm is used to model the customer contribution degree and trading behavior.Modeler software and application of SPSS Company is deal with massive data.The study will use data prepared in accordance with the classification C5.0 customer contribution.Data is divided into several subgroups.Correlation is observed with the contribution of other attributes.Use C5.0 and C & RT Classification Method to discuss trading behavior of the high contribution in exploring the relationship between the customers’ personal data and overall contribution.Studies have shown that high degree of contribution accounts for more number of male customers based in the study of the high contribution.Conversely,the low contribution of the customer base more women.Major contribution to customer based on between thirty and forty.Sixty years of age is the main contribution of low customers.The trend has nothing to do with sex.Research on the behavior characteristics of high contribution customers,the high contribution of the customers has the following characteristics: Frist,do not use those deposits.Second,use loan business and the collection of business moderately.Third,use notes、telephone banking,online banking and remitters high frequency.This study presents a model of development of data mining research bank customer transactions behavior.Knowledge can be obtained,but statistics and other traditional methods can not get the knowledge.It helps to improve the decision-making ability of the bank.