Data Mining and Financial Data Analysis

Most marketers see the worth of collecting financial data, but in addition realize the challenges of leveraging this data to generate intelligent, proactive pathways to the customer. Data mining - technologies and methods for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they are able to anticipate, rather than simply respond to, customer needs as well as financial need. Within this accessible introduction, we offers 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. crowdfunding

Objective:

1. The main objective of mining techniques would be to discuss how customized data mining tools should be produced for financial data analysis.

2. Usage pattern, in terms of the purpose may 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 process for extracting or mining knowledge to the plethora of data or we can easily say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are a few stages in the entire process of knowledge discovery in database, for example

1. Data cleaning. (To take out nose and inconsistent data)

2. Data integration. (Where multiple data source might be combined.)

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

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

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

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

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

Data Warehouse:

A knowledge warehouse is a repository of knowledge collected from multiple sources, stored under a unified schema and which in turn resides in a single site.

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The majority of the banks and loan companies give a wide verity of banking services for example checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some offer insurance services and stock investment services. investors

There are different kinds of analysis available, but also in this situation we should give one analysis generally known as "Evolution Analysis".

Data evolution analysis can be used for the object whose behavior changes after a while. Even if this may include characterization, discrimination, association, classification, or clustering of your energy related data, means we are able to say this evolution analysis is done with the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors tend to be relatively complete, reliable and quality, that gives the facility for analysis files mining. Have a look at discuss few cases including,

Eg, 1. Suppose we have stock exchange data with the previous few years available. And we would prefer to spend money on shares of best companies. A knowledge mining study of stock trading game data may identify stock evolution regularities for overall stocks but for the stocks of particular companies. Such regularities could help predict future trends on hand market prices, contributing our decisions regarding stock investments.

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

Eg, 3. House payment prediction and customer credit analysis are critical to the process of the lending company. There are numerous factors can strongly influence house payment performance and customer credit standing. Data mining could help identify critical factors and eliminate irrelevant one.

Factors associated with the potential risk of loan repayments like term from the loan, debt ratio, payment to income ratio, credit score and many more. The banks than decide whose profile shows relatively low risks in accordance with the critical factor analysis.

We can easily perform task faster and create a newer presentation with financial analysis software. They condense complex data analyses into easy-to-understand graphic presentations. And there's a bonus: Such software can vault our practice to some more advanced business consulting level which help we attract new customers.

To assist us find a program that most closely fits our needs-and our budget-we examined many of the leading packages that represent, by vendors' estimates, greater than 90% in the market. Although all of the packages are marketed as financial analysis software, they just don't all perform every function necessary for full-spectrum analyses. It will allow us to provide a unique want to clients.
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