Data Mining and Financial Data Analysis
Most marketers view the valuation on collecting financial data, but in addition realize the challenges of leveraging this data to make intelligent, proactive pathways returning to 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're able to anticipate, rather than simply answer, customer needs and also financial need. With this accessible introduction, we gives 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. Reg a
1. The main target of mining techniques is always to discuss how customized data mining tools needs to be created for financial data analysis.
2. Usage pattern, in terms of the purpose might be categories as per the requirement for financial analysis.
3. Create a tool for financial analysis through data mining techniques.
Data mining is the process for extracting or mining knowledge for that large quantity of knowledge or we are able to 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 measures in the whole 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 might be combined.)
3. Data selection. (Where data relevant to case study 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 illustration)
5. Data mining. (A vital process where intelligent methods are applied in order to extract data patterns.)
6. Pattern evaluation. (To recognize the truly interesting patterns representing knowledge depending on some interesting measures.)
7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge on the user.)
A data warehouse is often a repository of data collected from multiple sources, stored within a unified schema and which will resides at the single site.
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 provide insurance services and stock investment services.
There are various forms of analysis available, but in this case we want to give one analysis known as "Evolution Analysis". seed
Data evolution analysis is employed for that object whose behavior changes over time. Even though this can include characterization, discrimination, association, classification, or clustering of your time related data, means we are able to say this evolution analysis is done over 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 high quality, that gives the facility for analysis files mining. Here we discuss few cases including,
Eg, 1. Suppose we've stock exchange data of the last few years available. And we might prefer to put money into shares of best companies. An information mining study of stock exchange data may identify stock evolution regularities for overall stocks but for the stocks of particular companies. Such regularities can help predict future trends on hand market prices, contributing our decisions regarding stock investments.
Eg, 2. You can prefer to view 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 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 important to the business of the lender. There are numerous factors can strongly influence loan payment performance and customer credit rating. Data mining can help identify critical factors and eliminate irrelevant one.
Factors linked to the chance of loan instalments like term of the loan, debt ratio, payment to income ratio, credit ranking and others. Banking institutions than decide whose profile shows relatively low risks according to the critical factor analysis.
We could carry out the task faster and develop a modern-day 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 some more advanced business consulting level and help we attract new business.
To assist us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, greater than 90% from the market. Although each of the packages are marketed as financial analysis software, they don't all perform every function required for full-spectrum analyses. It will permit us to give a unique want to clients.