Data Mining and Financial Data Analysis

Most marketers see the price of collecting financial data, and also realize troubles of leveraging this information to produce intelligent, proactive pathways time for the customer. Data mining - technologies and methods for recognizing and tracking patterns within data - helps businesses search through layers of seemingly unrelated data for meaningful relationships, where they could anticipate, as an alternative to simply reply to, customer needs along with financial need. Within this accessible introduction, we offers a business and technological breakdown of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis. investors

Objective:

1. The target of mining techniques is to discuss how customized data mining tools must be intended for financial data analysis.

2. Usage pattern, in terms of the purpose could be categories as reported by the requirement for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for your variety of information or we can say data mining is "knowledge mining for data" or also we can easily say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are many steps in the whole process of knowledge discovery in database, such as

1. Data cleaning. (To get rid of nose and inconsistent data)

2. Data integration. (Where multiple repository might be combined.)

3. Data selection. (Where data highly relevant to your analysis task are retrieved through the database.)

4. Data transformation. (Where data are transformed or consolidated into forms befitting mining by performing summary or aggregation operations, as an illustration)

5. Data mining. (An important process where intelligent methods are applied in to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge determined by some interesting measures.)

7. Knowledge presentation.(Where visualization files representation techniques are utilized to present the mined knowledge on the user.)

Data Warehouse:

An information warehouse is a repository of information collected from multiple sources, stored within unified schema and which will resides in a single site.

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The majority of the banks and banking institutions give a wide verity of banking services including checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are various forms of analysis available, but in it we would like to give one analysis generally known as "Evolution Analysis".

Data evolution analysis can be used for the object whose behavior changes as time passes. Of course this can sometimes include characterization, discrimination, association, classification, or clustering of time related data, means we can easily say this evolution analysis is completed with the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis. funding

Data collect from banking and financial sectors in many cases are relatively complete, reliable and high quality, giving the facility for analysis files mining. Take a look at discuss few cases such as,

Eg, 1. Suppose we have stock exchange data with the previous few years available. And we would want to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks as well as the stocks of particular companies. Such regularities might help predict future trends in store market prices, contributing our making decisions regarding stock investments.

Eg, 2. One may prefer to view the debt and revenue change by month, by region through additional factors in addition to minimum, maximum, total, average, and also other statistical information. Data ware houses, supply the facility for comparative analysis and outlier analysis are all play important roles in financial data analysis and mining.

Eg, 3. Payment prediction and customer credit analysis are critical to the business of the lending company. There are many factors can strongly influence house payment performance and customer credit rating. Data mining can help identify critical indicators and eliminate irrelevant one.

Factors in connection with the risk of loan repayments like term from the loan, debt ratio, payment to income ratio, credit score and many more. Financial institutions than decide whose profile shows relatively low risks in line with the critical factor analysis.

We could do the task faster and make up a newer presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there is a bonus: Such software can vault our practice to a more advanced business consulting level that assist we attract new customers.

To help you us discover a program that most closely fits our needs-and our budget-we examined many of the leading packages that represent, by vendors' estimates, more than 90% of the market. Although all of the packages are marketed as financial analysis software, they do not all perform every function essential for full-spectrum analyses. It will let us provide a unique plan to clients.