MonroeGarcia's website

Data Mining and Financial Data Analysis

Most marketers understand the price of collecting financial data, but additionally 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 dig through layers of seemingly unrelated data for meaningful relationships, where they could anticipate, as an alternative to simply reply to, customer needs as well as financial need. With this accessible introduction, we gives a business and technological summary of data mining and outlines how, as well as sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.  scout


1. The attention of mining techniques is to discuss how customized data mining tools needs to be created for financial data analysis.

2. Usage pattern, with regards to the purpose could be categories as reported by the requirement of financial analysis.

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

Data mining:

Data mining is the procedure for extracting or mining knowledge for your variety 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 many measures in the entire process of knowledge discovery in database, including

1. Data cleaning. (To take out nose and inconsistent data)

2. Data integration. (Where multiple repository could be combined.)

3. Data selection. (Where data strongly related the learning 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, as an example)

5. Data mining. (An important process where intelligent methods are applied in to extract data patterns.)

6. Pattern evaluation. (To distinguish the truly interesting patterns representing knowledge according to some interesting measures.)

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

Data Warehouse:

A data warehouse is often a repository of knowledge collected from multiple sources, stored under a unified schema and which in turn resides with a single site.


Almost all of the banks and finance institutions provide a wide verity of banking services including checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different forms of analysis available, in it you want to give one analysis referred to as "Evolution Analysis".

Data evolution analysis is used for your object whose behavior changes over time. Although this might include characterization, discrimination, association, classification, or clustering of your energy related data, means we can say this evolution analysis is completed over 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 facility for analysis and knowledge mining. Take a look at discuss few cases including,

Eg, 1. Suppose we now have stock market data of the recent years available. And we would want to invest in shares of best companies. A knowledge mining study of currency markets data may identify stock evolution regularities for overall stocks but for the stocks of particular companies. Such regularities may help predict future trends in store market prices, contributing our decisions regarding stock investments.

Eg, 2. One may love to see the debt and revenue change by month, by region by additional circumstances along with minimum, maximum, total, average, and 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. Payment prediction and customer credit analysis are critical to the business of the lending company. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan repayments like term of the loan, debt ratio, payment to income ratio, credit rating and more. Banking institutions than decide whose profile shows relatively low risks according to the critical factor analysis. datamining

We are able to carry out the task faster and make up a more sophisticated 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 a more advanced business consulting level which help we attract new business.

To aid us look for 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 the packages are marketed as financial analysis software, they do not all perform every function necessary for full-spectrum analyses. It must allow us to give a unique plan to clients.