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Data Mining and Financial Data Analysis
Most marketers understand the value of collecting financial data, but also the challenges of leveraging this knowledge to create intelligent, proactive routes to the customer. Data mining – the techniques and techniques to identify and track patterns within data – helps businesses sift through layers of unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs and financial needs. In this accessible introduction, we provide a business and technical overview of data mining and outline how, with better business processes and complementary techniques, data mining can strengthen and redefine financial analytics.
1. The main objective of mining techniques is to discuss how data mining tools optimized for financial data analysis should be developed.
2. In terms of usage patterns, objectives, categories can be as per the need of financial analysis.
3. Develop a tool for financial analysis through data mining techniques.
Data mining is the process of extracting or mining knowledge for large amount of data or we can call data mining “Knowledge Mining for Data” or we can call Knowledge Discovery in Database (KDD). Data mining means: data collection, database construction, data management, data analysis and understanding.
The process of knowledge discovery in a database has some steps, viz
1. Data cleaning. (remove nose and inconsistent data)
2. Data integration. (Where multiple data sources can be combined.)
3. Data selection. (Where data relevant to the analysis task is retrieved from the database.)
4. Data conversion. (where data is transformed or aggregated by performing summarization or aggregation operations into forms suitable for mining, for example)
5. Data Mining. (A necessary process where intelligent methods are applied to extract data patterns.)
6. Form assessment. (To identify really interesting patterns of knowledge representation based on some interesting measures.)
7. Knowledge Presentation. (Where visualization and knowledge representation techniques are used to present the knowledge to the user.)
A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which typically resides at a single site.
Most of the banks and financial institutions offer a wide range of banking services like checking, savings, business and personal customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.
There are various types of analysis available, but in this case we would like to give an analysis called “Evolution Analysis”.
Data evolution analysis is used for objects whose behavior changes over time. Although it may involve characterization, discrimination, association, classification, or clustering of time-related data, we can say that developmental analysis is done through time series data analysis, sequence or periodic pattern matching, and similarity-based data analysis.
Data collected from the banking and financial sectors are often relatively complete, reliable and of high quality, which facilitates analysis and data mining. Here we discuss some events like,
For example, 1. Suppose we have stock market data available for the past few years. And we want to invest in shares of the best companies. A data mining study of stock exchange data can identify stock development regularities for overall stocks and stocks of particular companies. Such regularities can help predict future trends in stock market prices, contributing to our decision making regarding stock investments.
For example, 2. One wants to see the loan and revenue change based on the minimum, maximum, total, average, and other statistical information along with monthly, region, and other factors. Data warehouses, facilitating comparative analysis and outlier analysis all play an important role in financial data analysis and mining.
For example, 3. Loan repayment forecasting and customer credit analysis are critical to a bank’s business. There are many factors that can influence loan repayment performance and customer credit ratings. Data mining can help identify important factors and eliminate irrelevant ones.
Factors related to the risk of loan repayment such as loan tenure, debt ratio, payment to income ratio, credit history and many more. Rather than banks deciding whose profile shows relatively low risk based on critical factor analysis.
We can get the job done faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analysis into easy-to-understand graphic presentations. And there’s a bonus: such software can vault our practice to a more advanced business consulting level and help us attract new clients.
To help us find the program that best fits our needs—and our budget—we’ve examined some of the top packages that vendors estimate represent more than 90% of the market. Although all packages are marketed as financial analysis software, not all of them perform all the functions required for full-spectrum analysis. This should allow us to provide a unique service to customers.
ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-sized enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if consequences of small changes. A roll forward feature prepares budget or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.
Customized financial analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry using the Risk Management Association (RMA) database. It also highlights key ratios that require improvement and year-to-year trend analysis. A unique function, return calculation, calculates the appropriate asset base to support profit targets or existing sales and profits. Its DuPont Model analysis shows how each ratio affects return on equity.
Financial Analysis The CS reviews and compares the client’s financial position to business peers or industry standards. It can compare multiple locations of a single business to determine which is most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which lets them provide aggregated financial indicators to peers or industry standards, showing clients how their businesses compare.
iLumen regularly collects customer financial information to provide ongoing analysis. It also provides benchmarking information comparing the client’s financial performance with industry peers. The system is web-based and can monitor customer performance on a monthly, quarterly and yearly basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that display the company’s performance for a period. Analytics tools are viewed through customized dashboards.
PlanGuru by New Horizon Technologies can generate client-ready consolidated balance sheets, income statements and cash flow statements. The program includes tools for data analysis, projections, forecasting and budgeting. It also supports multiple scenarios. The system can calculate up to 21 financial ratios as well as breakeven points. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in Professional and Advisory versions. An add-on, called Business Analyzer, calculates the benchmark.
ProfitCents by Sageworks is web-based, so it doesn’t require any software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and the best software applications. It also provides analysis of different types of businesses for non-profits and sole proprietorships. The company offers free consulting, training and customer support. It is also available in Spanish.
Benefit System fx by CCH Tax & Accounting provides a wide range of benefit driver financial diagnostics and analysis. It provides data in spreadsheet form and can perform benchmarking calculations against industry standards. The program can track up to 40 periods.
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