Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vecto

Description: Bio-Inspired Credit Risk Analysis by Lean Yu, Shouyang Wang, Kin Keung Lai, Ligang Zhou Credit risk analysis is one of the most important topics in the field of financial risk management. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher 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. Notes Presentation of some of the most important advancements in credit risk analysis with SVM and some fully novel intelligent models for credit risk analysis 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. Table of Contents Credit Risk Analysis with Computational Intelligence: An Analytical Survey.- Credit Risk Analysis with Computational Intelligence: A Review.- Unitary SVM Models with Optimal Parameter Selection for Credit Risk Evaluation.- Credit Risk Assessment Using a Nearest-Point-Algorithm-based SVM with Design of Experiment for Parameter Selection.- Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection.- Hybridizing SVM and Other Computational Intelligent Techniques for Credit Risk Analysis.- Hybridizing Rough Sets and SVM for Credit Risk Evaluation.- A Least Squares Fuzzy SVM Approach to Credit Risk Assessment.- Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVM Model.- Evolving Least Squares SVM for Credit Risk Analysis.- SVM Ensemble Learning for Credit Risk Analysis.- Credit Risk Evaluation Using a Multistage SVM Ensemble Learning Approach.- Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach.- An Evolutionary-Programming-Based Knowledge Ensemble Model for Business Credit Risk Analysis.- An Intelligent-Agent-Based Multicriteria Fuzzy Group Decision Making Model for Credit Risk Analysis. Long 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. Feature Presentation of some of the most important advancements in credit risk analysis with SVM and some fully novel intelligent models for credit risk analysis Details ISBN3642096557 Author Ligang Zhou Publisher Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Year 2010 ISBN-10 3642096557 ISBN-13 9783642096556 Format Paperback Publication Date 2010-10-19 Imprint Springer-Verlag Berlin and Heidelberg GmbH & Co. K Place of Publication Berlin Country of Publication Germany DEWEY 332.7 Edition 1st Short Title BIO-INSPIRED CREDIT RISK ANALY Language English Media Book Subtitle Computational Intelligence with Support Vector Machines Pages 244 Edition Description Softcover reprint of hardcover 1st ed. 2008 Alternative 9783540778028 Audience Professional & Vocational Illustrations XVI, 244 p. We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:96239659;

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