Data Mining by Mehmed Kantardzic (good book recommendations TXT) 📕

bonds, portfolio management, commodity-price prediction, and mergers and acquisitions analyses; it has also been used in forecasting financial disasters. Daiwa Securities, NEC Corporation, Carl & Associates, LBS Capital Management, Walkrich Investment Advisors, and O’Sallivan Brothers Investments are only a few of the financial companies who use neural-network technology for data mining. A wide range of successful business applications has been reported, although the retrieval of technical details is not always easy. The number of investment companies and banks that mine data is far more extensive than the list mentioned earlier, but you will not often find them willing to be referenced. Usually, they have policies not to discuss it. Therefore, finding articles about banking companies who use data mining is not an easy task, unless you look at the SEC reports of some of the data-mining companies who sell their tools and services. There, you will find customers such as Bank of America, First USA Bank, Wells Fargo Bank, and U.S. Bancorp.

The widespread use of data mining in banking has not been unnoticed. Bank Systems & Technology commented that data mining was the most important application in financial services in 1996. For example, fraud costs industries billions of dollars, so it is not surprising to see that systems have been developed to combat fraudulent activities in such areas as credit card, stock market, and other financial transactions. Fraud is an extremely serious problem for credit-card companies. For example, Visa and MasterCard lost over $700 million in 1995 from fraud. A neural network-based credit card fraud-detection system implemented in Capital One has been able to cut the company’s losses from fraud by more than 50%. Several successful data-mining systems are explained here to support the importance of data-mining technology in financial institutions.

U.S. Treasury Department

Worth particular mention is a system developed by the Financial Crimes Enforcement Network (FINCEN) of the U.S. Treasury Department called “FAIS.” FAIS detects potential money-laundering activities from a large number of big cash transactions. The Bank Secrecy Act of 1971 required the reporting of all cash transactions greater than $10,000, and these transactions, of about 14 million a year, are the basis for detecting suspicious financial activities. By combining user expertise with the system’s rule-based reasoner, visualization facilities, and association-analysis module, FIAS uncovers previously unknown and potentially high-value leads for possible investigation. The reports generated by the FIAS application have helped FINCEN uncover more than 400 cases of money-laundering activities, involving more than $1 billion in potentially laundered funds. In addition, FAIS is reported to be able to discover criminal activities that law enforcement in the field would otherwise miss, for example, connections in cases involving nearly 300 individuals, more than 80 front operations, and thousands of cash transactions.

Mellon Bank, USA

Mellon Bank has used the data on existing credit-card customers to characterize their behavior and they try to predict what they will do next. Using IBM Intelligent Miner, Mellon developed a credit card-attrition model to predict which customers will stop using Mellon’s credit card in the next few months. Based on the prediction results, the bank can take marketing actions to retain these customers’ loyalty.

Capital One Financial Group

Financial companies are one of the biggest users of data-mining technology. One such user is Capital One Financial Corp., one of the nation’s largest credit-card issuers. It offers 3000 financial products, including secured, joint, co-branded, and college-student cards. Using data-mining techniques, the company tries to help market and sell the most appropriate financial product to 150 million potential prospects residing in its over 2-terabyte Oracle-based data warehouse. Even after a customer has signed up, Capital One continues to use data mining for tracking the ongoing profitability and other characteristics of each of its customers. The use of data mining and other strategies has helped Capital One expand from $1 billion to $12.8 billion in managed loans over 8 years. An additional successful data-mining application at Capital One is fraud detection.

American Express

Another example of data mining is at American Express, where data warehousing and data mining are being used to cut spending. American Express has created a single Microsoft SQL Server database by merging its worldwide purchasing system, corporate purchasing card, and corporate-card databases. This allows American Express to find exceptions and patterns to target for cost cutting. One of the main applications is loan application screening. American Express used statistical methods to divide loan applications into three categories: those that should definitely be accepted, those that should definitely be rejected, and those which required a human expert to judge. The human experts could correctly predict if an applicant would, or would not, default on the loan in only about 50% of the cases. Machine learning produced rules that were much more accurate—correctly predicting default in 70% of the cases—and that were immediately put into use.

MetLife, Inc.

MetLife’s Intelligent Text Analyzer has been developed to help automate the underwriting of 260,000 life insurance applications received by the company every year. Automation is difficult because the applications include many free-form text fields. The use of keywords or simple parsing techniques to understand the text fields has proven to be inadequate, while the application of full semantic natural-language processing was perceived to be too complex and unnecessary. As a compromise solution, the “information-extraction” approach was used in which the input text is skimmed for specific information relevant to the particular application. The system currently processes 20,000 life-insurance applications a month and it is reported that 89% of the text fields processed by the system exceed the established confidence-level threshold.

Bank of America (USA)

Bank of America is one of the world’s largest financial institutions. With approximately 59 million consumer and small business relationships, 6,000 retail banking offices and more than 18,000 ATMs, Bank of America is among the world’s leading wealth management companies and is a global leader in corporate and investment banking and trading across a broad range of asset classes. Bank of America identified savings of $4.8 million in2 years (a 400% return on investment) from use of a credit risk management system provided by