Data Mining and Financial Data Analysis

Most marketers see the worth of collecting financial data, but in addition realize the challenges of leveraging this knowledge to create intelligent, proactive pathways to the buyer. Data mining - technologies and methods for recognizing and tracking patterns within data - helps businesses dig through layers of seemingly unrelated data for meaningful relationships, where they're able to anticipate, instead of simply react to, customer needs and also financial need. In this accessible introduction, we supplies a business and technological introduction to data mining and outlines how, as well as sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

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

2. Usage pattern, with regards to the purpose may be categories as per the need for financial analysis.

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

Data mining:

Data mining is the method for extracting or mining knowledge for your large quantity of information or we can say data mining is "knowledge mining for data" or also we are able to say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are a few steps in the process of knowledge discovery in database, like

1. Data cleaning. (To remove nose and inconsistent data)

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

3. Data selection. (Where data tightly related 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 to to extract data patterns.)

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

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

Data Warehouse:

A data warehouse is really a repository of information collected from multiple sources, stored under a unified schema and which usually resides with a single site. Reg cf

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Most of the banks and finance institutions give you a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also provide insurance services and stock investment services.

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

Data evolution analysis can be used to the object whose behavior changes with time. Even though this can include characterization, discrimination, association, classification, or clustering of your time related data, means we can say this evolution analysis is completed from the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, giving the power for analysis files mining. Ideas discuss few cases including,

Eg, 1. Suppose we've stock exchange data from the last few years available. And we would love to purchase shares of best companies. An information mining study of stock trading game data may identify stock evolution regularities for overall stocks but for the stocks of particular companies. Such regularities can help predict future trends available market prices, contributing our selection regarding stock investments.

Eg, 2. One may prefer to observe the debt and revenue change by month, by region and by additional circumstances in addition to minimum, maximum, total, average, and also other statistical information. Data ware houses, provide the facility for comparative analysis and outlier analysis each one is 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 lender. There are lots of factors can strongly influence payment performance and customer credit standing. Data mining could help identify critical factors and eliminate irrelevant one.

Factors associated with the chance of loan instalments like term with the loan, debt ratio, payment to income ratio, credit score and many more. Financial institutions than decide whose profile shows relatively low risks in accordance with the critical factor analysis.

We could perform task faster and develop a newer presentation with financial analysis software. They condense complex data analyses into easy-to-understand graphic presentations. And there is a bonus: Such software can vault our practice to some more advanced business consulting level that assist we attract new business. funding

To aid us locate a program that most closely fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, more than 90% from the market. Although all of the packages are marketed as financial analysis software, they don't all perform every function necessary for full-spectrum analyses. It should allow us to supply a unique plan to clients.