We discussed the benefits and advantages of data mining, but what is necessary to sucessfully create a good data mining strategy?
Here are some basics:
1. Business Understanding: The objectives and problems of business are determined and converted to data mining problem. A preliminary plan is prepared.
2. Understanding the data: The data is collected initially. Information in relation to structure, quality and subset of data are found out.
3. Data Preparation: Final data set is constructed. After sorting and arranging the data and removing unwanted data, the modeling tools are directly applied on final data set. This is particularly important in this age of large data sets and Big Data.
4. Modeling: There are various modeling techniques like decision trees, rule induction, case base reasoning, visualization techniques, nearest neighbor technique, clustering algorithms etc. Best suited modeling technique is selected Models are combined with different parameters They are compared and ranked for validity and accuracy.
5. Evaluation: Models and steps in modeling are verified with business goals.
6. Deployment: Depending on the assessment and process review, a report is prepared or new data mining project is again set up.
Application of Data Mining in the Banking Sector:
Data mining carry various analyses on collected data to determine the consumer behavior with reference to product, price and distribution channel. The reaction of the customers for the existing and new products can also be known based on which banks will try to promote the product, improve quality of products and service and gain competitive advantage. Bank analysts can also analyze the past trends, determine the present demand and forecast the customer behavior of various products and services in order to grab more business opportunities and anticipate behavior patterns. Data mining technique also helps to identify profitable customers from non-profitable ones.
Another major area of development in banking is Cross selling i.e banks makes an attractive offer to its customer by asking them to buy additional product or service. For example, Home loan with insurance facilities and so on. With the help of data mining technique, banks are able to analyze which products and service are availed by most of the customers in cross selling and which type of consumers prefer to purchase cross selling products and so on.
2. Risk Management:
Banks provide loan to its customers by verifying the various details relating to the loan such as amount of loan, lending rate, repayment period, type of property mortgaged, demography, and income and credit history of the borrower. Customers with bank for longer periods, with high income groups are likely to get loans very easily. Even though, banks are cautious while providing loan, there are chances for loan defaults by customers. Data mining technique helps to distinguish borrowers who repay loans promptly from those who don't. It also helps to predict when the borrower is at default, whether providing loan to a particular customer will result in bad loans etc.
Bank executives by using Data mining technique can also analyze the behavior and reliability of the customers while selling credit cards too. It also helps to analyze whether the customer will make prompt or delay payment if the credit cards are sold to them.
3. Fraud detection:
Sometimes the given demographics and transaction history of the customers are likely to defraud the bank. Data mining technique helps to analyze such patterns and transactions that lead to fraud.
4. Customer Retention:
Today in this competitive environment, customers have wide range of products and services provided by different banks. Hence, banks have to cater the needs of the customer by providing such products and services which they prefer. This will result in customer loyalty and customer retention.
Data mining techniques helps to analyze the customers who are loyal from those who shift to other banks for better services. If the customer is shifting from his bank to another, reasons for such shifting and the last transaction performed before shifting can be known which will hep the banks to perform better and retain its customers.
Data mining techniques help companies particularly banking, telecommunication, insurance and retail marketing to build accurate customer profile based on customer behavior.Thus,it is becoming a necessity in this competitive environment to analyze the data from data warehouse containing hundreds of gigabytes or terabytes of data.