Data Mining Technology In The Personal Credit Analysis

Data mining(DM) means the process of adopting cryptic and potential helpful information from the Data. It's one kind of brand new Data analysis technology and is popular used in the field of banking finance, insurance, government, education, transportation and etc. enterprises as well as national defense scientific research. The universality of Data mining application and its great economic and social benefit attract the research of this field amongst many specialists and research institutes.This thesis expatiates on the state-of-the-art of DM technique, with emphasis on data mining algorithms such as clustering analysis, classification analysis, dependence analysis and statistical analysis. A comparative study of three popular DM tools(IBM Intelligent Miner, SPSS Clementine and SAS Enterprise Miner)is carried out. The future trends of DM technology are also revealed.With more and more abundant data of the bank, the mass data are described as "the data are abundant, but the information is lacking ". As a result, the data collected in the large-scale database turned to "the data grave". Because the bank policy-maker lack the tool which can extract the value information from mass data. Many important decisions of the bank are not based on data with rich information in the database, but on policy-maker's intuition. Data analysis through the Data Mining tool may discover the important data pattern. That transforms the bank data grave into "the gold bar" of knowledge. There are significant to finance organization by appliance DM technique. Up to date, DM technique has been successfully applied to some internationally distinguished banks such as American Firstar Bank, Bank One Bank, and AIB. This thesis addresses the application of DM technique to banking. In contrast, DM technique has not yet been widely used in domestic banks.The thesis addresses the application of DM technology in banking with the help of the development of the Personal credit system of a certain bank. This paper did many study works, such as get data, data transform, conformity data and straighten out data, ensure the correctness, consistency, integrality and dependability of the data. Specifically, i) statistical and clustering analysis in terms of the Personal credit customer's gender and age are conducted; ii) Analyzing Personal Loan, As well as data mining the Non-performing loans that benefits the risk management and the development of new services, benefits the develop of bank trade, offer the scientific gist to decision-making by the leader of bank.At the end of the present thesis, some related problems to be solved are listed, and future work is indicated.