Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines / Edition 1by Lean Yu, Shouyang Wang, Kin Keung Lai, Ligang Zhou
Pub. Date: 06/03/2008
Publisher: Springer Berlin Heidelberg
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
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.
- Springer Berlin Heidelberg
- Publication date:
- Edition description:
- Product dimensions:
- 6.40(w) x 9.30(h) x 0.90(d)
Table of Contents
Part I Credit Risk Analysis with Computational Intelligence: An Analytical Survey: Credit Risk Analysis with Computational Intelligence: A Review,- Part II 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 ,- Part III 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,- Part IV SVM Ensemble Learning for Credit Risk Analysis: Credit Risk Evaluation Using a Multistage 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.
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