Bio-Inspired Credit Risk Analysis: Computational Intelligence with Support Vector Machines / Edition 1

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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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Product Details

  • ISBN-13: 9783540778028
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 6/5/2008
  • Edition description: 2008
  • Edition number: 1
  • Pages: 244
  • Product dimensions: 6.40 (w) x 9.30 (h) x 0.90 (d)

Table of Contents

Pt. I Credit Risk Analysis with Computational Intelligence: An Analytical Survey 1

1 Credit Risk Analysis with Computational Intelligence: A Review 3

Pt. II Unitary SVM Models with Optimal Parameter Selection for Credit Risk Evaluation 25

2 Credit Risk Assessment Using a Nearest-Point-Algorithm-based SVM with Design of Experiment for Parameter Selection 27

3 Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection 41

Pt. III Hybridizing SVM and Other Computational Intelligent Techniques for Credit Risk Analysis 57

4 Hybridizing Rough Sets and SVM for Credit Risk Evaluation 59

5 A Least Squares Fuzzy SVM Approach to Credit Risk Assessment 73

6 Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVM Model 85

7 Evolving Least Squares SVM for Credit Risk Analysis 105

Pt. IV SVM Ensemble Learning for Credit Risk Analysis 133

8 Credit Risk Evaluation Using a Multistage SVM Ensemble Learning Approach 135

9 Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach 157

10 An Evolutionary-Programming-Based Knowledge Ensemble Model for Business Credit Risk Analysis 179

11 An Intelligent-Agent-Based Multicriteria Fuzzy Group Decision Making Model for Credit Risk Analysis 197

References 223

Subject Index 239

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