Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines
Language: English
Published by Springer 03.06.2008., 2008
- Hardcover
- Used
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260 Seiten Ausgetragenes Bibliotheksexemplar, top erhalten B05-03-05B Sprache: Englisch Gewicht in Gramm: 536.
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- Title
- Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines
- Author
- Yu, Lean, Shouyang Wang and Kin Keung Lai:
- Publisher
- Springer 03.06.2008.
- Publication year
- 2008
- Condition
- Sehr gut
- Binding
- 8° Gebundene Ausgabe
- Language
- English
- ISBN 10
- 3540778020
- ISBN 13
- 9783540778028
- Edition
- Auflage: 2008.
- Item weight
- 536 grams
- Seller catalogs
- Fachbuch
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
avelibro OHG
Dinkelscherben, Germany
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avelibro OHG
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