Most marketers view the price of collecting financial data, and also realize the contests of leveraging this information to produce intelligent, proactive pathways returning to the buyer. 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're able to anticipate, instead of simply reply to, customer needs as well as financial need. In this accessible introduction, we gives a business and technological introduction to data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.
1. The attention of mining techniques is usually to discuss how customized data mining tools needs to be created for financial data analysis.
2. Usage pattern, with regards to the purpose can be categories as reported by the dependence on financial analysis.
3. Produce a tool for financial analysis through data mining techniques.
Data mining is the process for extracting or mining knowledge for the plethora of data or we are able to 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 steps in the entire process of knowledge discovery in database, such as
1. Data cleaning. (To take out nose and inconsistent data)
2. Data integration. (Where multiple data source could be combined.)
3. Data selection. (Where data strongly related your analysis task are retrieved through the 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. (An essential process where intelligent methods are applied in to extract data patterns.)
6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge according to some interesting measures.)
7. Knowledge presentation.(Where visualization information representation techniques are employed to present the mined knowledge on the user.)
A data warehouse is really a repository of data collected from multiple sources, stored under a unified schema and which in turn resides at a single site.
A lot of the banks and loan companies give you a wide verity of banking services like checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some provide insurance services and stock investment services.
There are different forms of analysis available, however in this case we should give one analysis called "Evolution Analysis".
Data evolution analysis is employed for the object whose behavior changes after a while. Of course this can include characterization, discrimination, association, classification, or clustering of your energy related data, means we can easily 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 in many cases are relatively complete, reliable as well as quality, giving the power for analysis information mining. Here we discuss few cases including,
Eg, 1. Suppose we now have stock trading game data of the recent years available. And we would like to spend money on shares of best companies. An information mining study of currency markets data may identify stock evolution regularities for overall stocks and also for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decisions regarding stock investments.
Eg, 2. One could love to look at the debt and revenue change by month, by region and by additional factors together with minimum, maximum, total, average, as well as 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. artificial Intelligence
Eg, 3. House payment prediction and customer credit analysis are essential to the process of the lending company. There are many factors can strongly influence house payment performance and customer credit standing. Data mining may help identify critical indicators and eliminate irrelevant one.
Factors related to potential risk of loan instalments like term of the loan, debt ratio, payment to income ratio, credit score and others. Financial institutions than decide whose profile shows relatively low risks based on the critical factor analysis.
We could carry out the task faster and make up a newer presentation with financial analysis software. These items 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 which help we attract new business.
To aid us locate a program that most closely fits our needs-and our budget-we examined a few 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 really all perform every function necessary for full-spectrum analyses. It will allow us give a unique service to clients.