Introduction:
Most marketers understand the value of the collection of financial data, but also recognize the challenges of using this knowledge to make intelligent, proactive way to provide to the customer. Data mining - technologies and techniques for detecting and tracking patterns in data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they anticipate, rather than simply respond to customer needs and financial need may be. In this introduction, we offer an Internet business and technical overview of Data Mining and how can sound business processes and complementary technologies, data mining, strengthen and redefine financial analysis.
Objective:
1. The main objective of the mining techniques is to discuss how custom data-mining tools developed for financial data analysis.
2. Use patterns in relation to the target can be categories as per the need of a financial analysis.
3. Development of a tool for financial analysis through data mining techniques.
Data mining:
Data mining is the process for the extraction or mining knowledge about the large amount of data or data mining, we can say is, "knowledge for data mining" or we can say: Knowledge Discovery in Databases (KDD). Is data mining: data collection, database creation, data management, data analysis and understanding.
There are a number of steps in the process of gaining knowledge in the database, as
1. Data cleaning. (To remove the nose and inconsistent data)
2. Data integration. (If multiple data sources can be combined.)
3. Data selection. (If relevant information is available for the analysis task from the database.)
4. to transform data. (If the data are transformed or consolidated into forms appropriate for mining by summary or aggregation operations, for example)
5. Data Mining. (Used are an essential process by which intelligent methods to extract data patterns.)
6. Tested. (Based to take for the really interesting patterns, the knowledge of a number of interesting activities.)
7. Knowledge presentation. (Where visualization and knowledge representation techniques to me the user's knowledge are used.)
Data Warehouse:
A data warehouse is a collection of information from different sources under a single structure, which normally lives saved gathered in one place.
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Most banks and financial institutions offer a broad truth of banking services including checking, savings, business and personal transactions, credit and investments such as mutual funds, etc. Some also offer insurance and stock investment.
There are different types of analysis available, but in this case we want to give an analysis as "Evolution Analysis" to announce.
Data trend analysis for the object whose behavior changes over time used. While this may mean that, the characterization, discrimination, association, classification, clustering, or the time data, we say that this development takes place by means of analysis of time series analysis, a sequence or schedule for pattern recognition and similarity-based data analysis.
Collecting data from banks and financial sector are often relatively complete, reliable and high quality, the device provides for the analysis and data mining. Here we discuss some cases such as
Ex first Suppose we have stock market data of past years are available. And we want to invest in shares of top companies. can a data mining analysis of stock data evolution regularities identify shares for a total stocks and the stocks of individual companies. Such laws can help predict future trends in stock prices, our decision in regard to help on equity investments.
For example, second You can change how the debt and the income view by month, region and other factors, along with minimum, maximum, sum, average and other statistical information. Data Warehousing houses, so that the plant for the comparative analysis and the analysis of outliers, all play an important role in financial data analysis and mining.
Ex third Loan payment prediction and customer credit analysis are important to the business of the Bank. Many factors can make a strong impact on customer payment behavior and credit loans. Data mining can contribute to important factors and are irrelevant.
Factors with the risk of the loan payments, such as the loan term, debt payment / income ratio, credit history, and more finds. The banks have to decide whose profile shows a relatively low risk as a critical factor analysis.
We can make the task faster and a more sophisticated presentation with financial analysis software. These products condense the analysis of complex data in easily understood graphical presentations. And there's a bonus: You can use our business consulting practice to a higher level, and help us win new customers.
To help us find a program, the most studied our needs and our budget, we have some of the leading packages are estimates provided by suppliers over 90% of the market. While all packages are sold, such as financial analysis software, not every function for all full-spectrum analysis to perform necessary. It should allow us to offer a unique service for customers.