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How data mining can help financial data analysis?
Financial data collected in the banking and financial market are relatively done, reliable, and of huge quality, which supports systematic data analysis and data mining. Therefore it can present a few typical cases which are as follows −
Design and construction of data warehouses for multidimensional data analysis and data mining − Data warehouses are required to be constructed for banking and financial records. Multidimensional data analysis methods must be used to analyze the general features of such data.
For instance, one can like to view the debt and revenue changes by month, by region, by sector, and by several elements, along with the maximum, minimum, total, average, trend, and other statistical data.
Loan payment prediction and customer credit policy analysis − Loan payment prediction and customer credit analysis are essential to the business of a bank. Some elements can powerfully or weakly influence loan payment implementation and user credit rating.
Data mining methods, including attribute selection and attribute relevance ranking, can help identify important elements and remove irrelevant ones. For instance, factors associated with the risk of loan payments involve loan-to-value ratio, term of the loan, debt ratio (total amount of monthly debt versus the total monthly income), payment-to-income ratio, user income level, education level, residence region, and credit history.
Classification and clustering of customers for targeted marketing − Classification and clustering techniques can be used for customer group identification and targeted marketing. For instance, one can use classification to recognize the most important factors that can influence a user’s decision regarding banking. Customers with the same behaviors regarding loan payments can be recognized by multidimensional clustering methods. These can help identify user groups, relate a new user with an appropriate customer group, and facilitate targeted marketing.
Detection of money laundering and other financial crimes − It can detect money laundering and other financial crimes, it is essential to integrate data from several databases (like bank transaction databases, and civil or state criminal history databases), considering they are potentially associated with the study. Multiple data analysis tools can be used to detect unusual patterns, including high amounts of cash flow at specific periods, by specific groups of customers.
It is useful tools such as data visualization tools (to show transaction activities using graphs by time and by groups of users), linkage analysis tools (to recognize links between different customers and activities), classification tools (to filter unrelated attributes and rank the highly associated ones), clustering tools (to group different methods), outlier analysis tools (to detect unusual amounts of fund transfers or multiple activities), and sequential design analysis tools (to features unusual access sequences).
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