Data Mining and Financial Data Analysis

Most marketers understand the price of collecting financial data, but also realize the contests of leveraging this knowledge to generate intelligent, proactive pathways back to the customer. 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're able to anticipate, as an alternative to simply answer, customer needs in addition to financial need. In this accessible introduction, we supplies a business and technological overview of data mining and outlines how, as well as sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis. founders

Objective:
1. The main objective of mining techniques is always to discuss how customized data mining tools should be intended for financial data analysis.

2. Usage pattern, due to the purpose can be categories as reported by the requirement for financial analysis.

3. Build 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 knowledge or we are able to 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 a few steps in the process of knowledge discovery in database, such as
1. Data cleaning. (To take out nose and inconsistent data)
2. Data integration. (Where multiple data bank could possibly be combined.)
3. Data selection. (Where data relevant to the analysis task are retrieved from your database.)
4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for example)
5. Data mining. (A necessary process where intelligent methods are utilized for to extract data patterns.)
6. Pattern evaluation. (To distinguish the truly interesting patterns representing knowledge based on some interesting measures.)
7. Knowledge presentation.(Where visualization and data representation techniques are employed to present the mined knowledge for the user.)

Data Warehouse:
A knowledge warehouse is often a repository of data collected from multiple sources, stored within a unified schema and which in turn resides at the single site.

Text:
A lot of the banks and financial institutions 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 offer insurance services and stock investment services.

There are various forms of analysis available, however in this case we want to give one analysis known as "Evolution Analysis".

Data evolution analysis is used for that object whose behavior changes after a while. Even though this might include characterization, discrimination, association, classification, or clustering of your energy related data, means we can easily say this evolution analysis is performed 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 as well as quality, that gives the power for analysis information mining. Ideas discuss few cases like,
Eg, 1. Suppose we've got currency markets data with the recent years available. And we'd prefer to put money into shares of best companies. A data mining study of currency markets data may identify stock evolution regularities for overall stocks and also for the stocks of particular companies. Such regularities could help predict future trends in stock market prices, contributing our selection regarding stock investments. capital

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

Eg, 3. Loan payment prediction and customer credit analysis are critical to the process of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify key elements and eliminate irrelevant one.

Factors linked to the risk of loan repayments like term in the loan, debt ratio, payment to income ratio, credit rating and more. Banking institutions than decide whose profile shows relatively low risks in line with the critical factor analysis.
We can easily carry out the task faster and develop a modern-day presentation with financial analysis software. The products 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 that assist we attract clients.
To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, a lot more than 90% from the market. Although all of the packages are marketed as financial analysis software, they don't really all perform every function essential for full-spectrum analyses. It will allow us give a unique service to clients.