Introduction: We have already studied principles and methods for mining relational data, data warehouses, and complex types of data (including stream data, time-series and sequence data, complex structured data, spatiotemporal data, multimedia data, heterogeneous multi database data, text data, and Web data). Because data mining is a relatively young discipline with wide and diverse applications, there is still a nontrivial gap between general principles of data mining and application specific, effective data mining tools.
Data Mining for Financial Data Analysis
Most banks and financial institutions offer a wide variety of banking services (such as checking and savings accounts for business or individual customers), credit (such as business, mortgage, and automobile loans), and investment services (such as mutual funds). Some also offer insurance services and stock investment services.
Financial data collected in the banking and financial industry is often relatively complete, reliable, and of high quality, which facilitates systematic data analysis and data mining. Here we present a few typical cases:
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Design and construction of data warehouses for multidimensional data analysis and data mining: Like many other applications, data warehouses need to be constructed for banking and financial data. Multidimensional data analysis methods should be used to analyze the general properties of such data. For example, one may like to view the debt and revenue changes by month, by region, by sector, and by other factors, along with maximum, minimum, total, average, trend, and other statistical information. Data warehouses, data cubes, multi feature and discovery-driven data cubes, characterization and class comparisons, and outlier analysis all play important roles in financial data analysis and mining.
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Loan payment prediction and customer credit policy analysis: Loan payment prediction and customer credit analysis are critical to the business of a bank. Many factors can strongly or weakly influence loan payment performance and customer credit rating. Data mining methods, such as attribute selection and attribute relevance ranking, may help identify important factors and eliminate irrelevant ones. For example, factors related to the risk of loan payments include loan-to-value ratio, term of the loan, debt ratio (total amount of monthly debt versus the total monthly income), payment to- income ratio, customer income level, education level, residence region, and credit history. Analysis of the customer payment history may find that, say, payment-to income ratio is a dominant factor, while education level and debt ratio are not. The bank may then decide to adjust its loan-granting policy so as to grant loans to those customers whose applications were previously denied but whose profiles show relatively low risks according to the critical factor analysis.
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Classification and clustering of customers for targeted marketing: Classification and clustering methods can be used for customer group identification and targeted marketing. For example, we can use classification to identify the most crucial factors that may influence a customer’s decision regarding banking. Customers with similar behaviors regarding loan payments may be identified by multidimensional clustering techniques. These can help identify customer groups, associate a new customer with an appropriate customer group, and facilitate targeted marketing.
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Detection of money laundering and other financial crimes: To detect money laundering and other financial crimes, it is important to integrate information from multiple databases (like bank transaction databases, and federal or state crime history databases), as long as they are potentially related to the study. Multiple data analysis tools can then be used to detect unusual patterns, such as large amounts of cash flow at certain periods, by certain groups of customers. Useful tools include data visualization tools (to display transaction activities using graphs by time and by groups of customers), linkage analysis tools (to identify links among different customers and activities), classification tools (to filter unrelated attributes and rank the highly related ones), clustering tools (to group different cases), outlier analysis tools (to detect unusual amounts of fund transfers or other activities), and sequential pattern analysis tools (to characterize unusual access sequences).
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Data mining for the Retail Industry: The retail industry is a major application area for data mining, since it collects huge amounts of data on sales, customer shopping history, goods transportation, consumption, and service. The quantity of data collected continues to expand rapidly, especially due to the increasing ease, availability, and popularity of business conducted on the Web, or e-commerce. Today, many stores also have websites where customers can make purchases on-line. Some businesses, exist solely on-line, without any brick-and-mortar (i.e., physical) store locations. Retail data provide a rich source for data mining.
Retail data mining can help identify customer buying behaviors, discover customer shopping patterns and trends, improve the quality of customer service, achieve better customer retention and satisfaction, enhance goods consumption ratios, design more effective goods transportation and distribution policies, and reduce the cost of business.
A few examples of data mining in the retail industry are outlined as follows.
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Design and construction of data warehouses based on the benefits of data mining: Because retail data cover a wide spectrum (including sales, customers, employees, goods transportation, consumption, and services), there can be many ways to design a data warehouse for this industry. The levels of detail to include may also vary substantially. The outcome of preliminary data mining exercises can be used to help guide the design and development of data warehouse structures. This involves deciding which dimensions and levels to include and what preprocessing to perform in order to facilitate effective data mining.
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Multidimensional analysis of sales, customers, products, time, and region: The retail industry requires timely information regarding customer needs, product sales, trends, and fashions, as well as the quality, cost, profit, and service of commodities. It is therefore important to provide powerful multidimensional analysis and visualization tools, including the construction of sophisticated data cubes according to the needs of data analysis. , is a useful data structure in retail data analysis because it facilitates analysis on aggregates with complex conditions.
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Analysis of the effectiveness of sales campaigns: The retail industry conducts sales campaigns using advertisements, coupons, and various kinds of discounts and bonuses to promote products and attract customers. Careful analysis of the effectiveness of sales campaigns can help improve company profits. Multidimensional analysis can be used for this purpose by comparing the amount of sales and the number of transactions containing the sales items during the sales period versus those containing the same items before or after the sales campaign. Moreover, association analysis may disclose which items are likely to be purchased together with the items on sale, especially in comparison with the sales before or after the campaign.
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Customer retention—analysis of customer loyalty: With customer loyalty card information, one can register sequences of purchases of particular customers. Customer loyalty and purchase trends can be analyzed systematically. Goods purchased at different periods by the same customers can be grouped into sequences. Sequential pattern mining can then be used to investigate changes in customer consumption or loyalty and suggest adjustments on the pricing and variety of goods in order to help retain customers and attract new ones.
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Product recommendation and cross-referencing of items: By mining associations from sales records, one may discover that a customer who buys a digital camera is likely to buy another set of items. Such information can be used to form product recommendations. Collaborative recommender systems use data mining techniques to make personalized product recommendations during live customer transactions, based on the opinions of other customers. Product recommendations can also be advertised on sales receipts, in weekly flyers, or on the Web to help improve customer service, aid customers in selecting items, and increase sales. Similarly, information such as “hot items this week” or attractive deals can be displayed together with the associative information in order to promote sales.
