Data Mining and Financial Data Analysis

Most marketers comprehend the worth of collecting financial data, and also realize the difficulties of leveraging this data to create intelligent, proactive pathways to the client. 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 are able to anticipate, rather than simply react to, customer needs in addition to financial need. In this accessible introduction, we gives a business and technological summary of data mining and outlines how, together with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis. artificial Intelligence


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

2. Usage pattern, due to the purpose may be categories as reported by the need 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 to the variety of knowledge or we could say data mining is "knowledge mining for data" or also we could say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

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

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

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

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

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

5. Data mining. (A vital process where intelligent methods are applied to 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 widely-used to present the mined knowledge towards the user.)

Data Warehouse:

A knowledge warehouse can be a repository of information collected from multiple sources, stored within a unified schema and which often resides with a single site. Reg cf


A lot of the banks and loan companies provide a wide verity of banking services for example 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 several varieties of analysis available, in this example we should give one analysis known as "Evolution Analysis".

Data evolution analysis is employed for that object whose behavior changes with time. Even though this can include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is conducted over the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors will often be relatively complete, reliable and quality, which provides the power for analysis and data mining. Take a look at discuss few cases like,

Eg, 1. Suppose we've currency markets data from the previous few years available. And we might want to purchase shares of best companies. An information mining study of stock trading game data may identify stock evolution regularities for overall stocks and also for the stocks of particular companies. Such regularities can help predict future trends on hand market prices, contributing our decision making regarding stock investments.

Eg, 2. One could like to observe the debt and revenue change by month, by region and by additional factors as well as minimum, maximum, total, average, and also other statistical information. Data ware houses, provide 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 important to the process of the lender. There are lots of factors can strongly influence house payment performance and customer credit rating. Data mining can help identify critical factors and eliminate irrelevant one.

Factors associated with the potential risk of loan installments 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 based on the critical factor analysis.

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

To help you us discover a program that best fits our needs-and our budget-we examined a few 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 do not all perform every function required for full-spectrum analyses. It must permit us to provide a unique plan to clients.

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