Study On Banks’Data Mining Application And Utility

In today’s information society, rapidly changed Internet economy and virtualeconomy urge the financial industry to make revolutions on their management patterns.The financial market is shifting from the supply model to the demand model. With thetrend that the market competition becomes more fiercely, the business and economicalvalue of data have attracted widely attentions and become a special and professional assetin banks. It bases on the traditional asset value theories and has its special and relativecharacteristics. The study of data mining application and utility in bank combinesfinance theories, information technologies, and management science organically. Itconsiders the data as one of important resource endowments of banks, as the output ofthe operation processes, as the indicator of technology and management capability, as thereflection of the economic man preference, and as the foundation of the institutionalconstraints.Modern banks rely on the data quantity analysis and data models to create values bymore meticulous management so that they can improve their core competitiveness. Thisarticle focuses on data; describes its functions at a great length on kinds of activities suchas front-end customer services, middle-end risk controls, and back-end operationalsupports; and analyzes the data utility generated during customer transaction activitiesand operational managerial activities in great depth. This article studies the value ofdata as one of important bank asset from many aspects including data value theory, datagovernance, customer relationship management, comprehensive risk management andfinancial innovations; and then extend the study to the impact of data validation on bankbusiness and the importance of data on the value creation in banks.The data utility of banks reflects in two ways. One comes from the processing ofdata. The unhandled raw data is huge in quantity but little in value. After applyingtechnical and managerial methods to integrate, classify and cleanse, data realized valuesadded. The deeper mining and analysis on data is the gathering of mental efforts andhuman intelligence. It has utility in economy and real use value in practice. The othercomes from the application of data, especially the wide applications of the validated datathroughout lines of business in banks. By applying data utility into kinds of banks’value-added activities, the comprehensive management capability and financialinnovation can be improved dramatically. Finally, the bank’s value and assets can be increased in order to form a steady foundation to improve the core competitiveness infuture financial market competition.The chapter one of this article gives an overview of the main documents in the fieldof bank data mining application and utility study. It studies and analyzes those relativedomestic and international documents which cover value theory, data application anddata mining theory, information theory and knowledge theory; it also point out theweakness point in the current data mining application and utility theory.The chapter two expands the discussion around utility of banks’ data and theeffectiveness of data governance. After expounding the intension and extension of datadefinition, combining the characteristics of bank business, the section two specifiesapproach methods of data utility. The raw data has to be collected, cleansed,manipulated, processed and integrated to achieve modularized data. Then we can usethe cluster analysis and the classification analysis of the data mining to study data, refineand summarize the useful information. With effective utilizations, this information willbecome the professional knowledge assets which can guide the management to makebusiness decisions. Finally, this kind of knowledge assets will be kept in a data form,which will play a more important role via data movement and circulation. The sectionfour studies the data governance’s current situation and development trends to provideregulation guarantees for the improvement of data utility. As the data governance is oneof important components of company governance, this section emphasizes theimportance of organizational structure development of data governance, datastandardization, data quality control, data architecture plan and data safety managementon the improvement of the bank operation and management. This chapter alsoinnovatively designs the data quality controlling model to prevent ethics risks generatedby illegal operations from the data producers effectively, then to avoid wealth loss duringthis process.The main content of the chapter three is the data utility increase during the bankcustomer management process. Banks’ operational strategies have shifted fromproduct-oriented to customer-oriented. They try to optimize tools and methods ofcustomer relationship management, to improve customer services by mining data value,to create values for both customers and bank their own. The section one analyzes theimportance of utilizing advanced information technologies and building customerinformation management systems for banks. The article elaborates the customer classification, customer behavior analysis, customer profitability analysis and the datavalue increase in the precision marketing.The chapter four, the study on the utility of data in the risk management of bank,states that due to the importance, the complexity and the technology-dependence of therisk management in banks, we need to combine the qualitative management andquantitative analysis together in the management practice, and to use risk managementtechnology and information technology to mine and analysis on massive data of banks indepth. It is both the mandatory requirements of the regulatory system and the inevitablechoice for banks to improve their risk identification and measurement efficiency and tosave the overall cost of risk management. Effectively using data in management ofcredit risk, market risk and operational risk can maximize the value created by the riskmanagement.The chapter five combines the financial development theory and the financialinnovation theory, discusses the relationship between financial innovation and customerrelationship management, risk management, and explore the value of banking products,services and process innovation in the data. It reveals the correlation between thebuilding of the process banking, the financial product innovation, the service innovationand the improvement of the data utility. It explains the importance of buildingenterprise-class knowledge base and strengthening data services on the financialinnovation.The main innovations of this article are as follows: first, it treats data as animportant strategic resource of banks and analyzes its contribution to the growth of bankprofits, the customer relationship management, the risk management, the financialinnovation and all these competitive advantage; second, it uses the informationeconomical static game model and principal-agent theory to design the bank’s internaldata governance mechanisms and data quality control model; third, it merges thefinancial theory with the information technology based on data and studies the data utilityincrease in banks’ internal applications and in the financial innovations.