There are many factors can strongly influence loan payment performance and customer credit rating. Loan payment prediction and customer credit analysis are critical to the business of the bank. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.Įg, 3. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.Įg, 2. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. And we would like to invest in shares of best companies. Suppose we have stock market data of the last few years available.
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Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.ĭata collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. There are different types of analysis available, but in this case we want to give one analysis known as "Evolution Analysis".ĭata evolution analysis is used for the object whose behavior changes over time. Some also offer insurance services and stock investment services. Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)Ī data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)ħ. (An essential process where intelligent methods are applied in order to extract data patterns.)Ħ. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)ĥ. (Where data relevant to the analysis task are retrieved from the database.)Ĥ. (Where multiple data source may be combined.)ģ.
There are some steps in the process of knowledge discovery in database, such asġ. Means data mining is : data collection, database creation, data management, data analysis and understanding. Develop a tool for financial analysis through data mining techniques.ĭata mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.ģ. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.Ģ. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.ġ.
Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer.
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