Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines Softcover reprint of hardcover 1st ed. 2008 Edition

Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines Softcover reprint of hardcover 1st ed. 2008 Edition book cover

Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines Softcover reprint of hardcover 1st ed. 2008 Edition

Author(s): Lean Yu (Author), Shouyang Wang (Author), Kin Keung Lai (Author), Ligang Zhou (Author)

  • Publisher: Springer
  • Publication Date: October 19, 2010
  • Edition: Softcover reprint of hardcover 1st ed. 2008
  • Language: English
  • Print length: 260 pages
  • ISBN-10: 3642096557
  • ISBN-13: 9783642096556

Book Description

Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.

Editorial Reviews

From the Back Cover

Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.

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