Risk analysis

  • Authors:
  • Ira J. Haimowitz;Tim K. Keyes

  • Affiliations:
  • Senior Manager, Information Science, U.S. Pharmaceuticals, Pfizer Inc., New York;Senior Director, Quality and Digitization, General Electric Capital Services, Stamford, Connecticut

  • Venue:
  • Handbook of data mining and knowledge discovery
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this chapter, we discuss both quantitative and qualitative aspects of data mining, as a process for uncovering information related to risk exposure buried in large amounts of data, and its exploitation for enterprise benefit. Section 1 introduces a general foundation for the application of data mining in a risk setting. Section 2 offers some typical business problems data mining can help address. Section 3 suggests potential uses of company internal and external information in the data mining/risk analysis process. Sections 4 and 5 delve into a more technical discussion on data mining methods, tools, and applications. Finally, we focus on the most important and challenging task in data mining--implementation of results--in Section 6.