Data Mining - Mehmed Kantardzic [277]
Evolutionary Technologies, Inc. 4301 Westbank Drive, Austin, TX 78746, USA Phone: 512-327-6994
Information Advantage, Inc. 12900 Whitewater Drive, Suite 100, Minnetonka, MN 55343, USA Phone: 612-938-7015
IntelligenceWare, Inc. 55933 W. Century Blvd., Suite 900, Los Angeles, CA 90045, USA Phone: 310-216-6177
Microsoft Corporation One Microsoft Way, Redmond, WA 98052, USA Phone: 206-882-8080
Computer Associates International, Inc. One Computer Associates Plaza, Islandia, NY 11788-7000, USA Phone: 516-342-5224
APPENDIX B
DATA-MINING APPLICATIONS
Many businesses and scientific communities are currently employing data-mining technology. Their number continues to grow, as more and more data-mining success stories become known. Here we present a small collection of real-life examples of data-mining implementations from the business and scientific world. We also present some pitfalls of data mining to make readers aware that this process needs to be applied with care and knowledge (both, about the application domain and about the methodology) to obtain useful results.
In the previous chapters of this book, we have studied the principles and methods of data mining. Since data mining is a young discipline with wide and diverse applications, there is a still a serious gap between the general principles of data mining and the domain-specific knowledge required to apply it effectively. In this appendix, we examine a few application domains illustrated by the results of data-mining systems that have been implemented.
B.1 DATA MINING FOR FINANCIAL DATA ANALYSIS
Most banks and financial institutions offer a wide variety of banking services such as checking, savings, business and individual customer transactions, investment services, credits, and loans. Financial data, collected in the banking and financial industry, are often relatively complete, reliable, and of a high quality, which facilitates systematic data analysis and data mining to improve a company’s competitiveness.
In the banking industry, data mining is used heavily in the areas of modeling and predicting credit fraud, in evaluating risk, in performing trend analyses, in analyzing profitability, as well as in helping with direct-marketing campaigns. In the financial markets, neural networks have been used in forecasting stock prices, options trading, rating 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