Yu / Wang / Lai Bio-Inspired Credit Risk Analysis

Computational Intelligence with Support Vector Machines
1. Auflage 2008
ISBN: 978-3-540-77803-5
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 244 Seiten

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.

Autoren/Hrsg.

Yu, Lean

Wang, Shouyang

Lai, Kin Keung

Zhou, Ligang

Inhaltsverzeichnis

1;Preface;5
2;List of Figures;9
3;List of Tables;11
4;Table of Contents;13
5;Part I Credit Risk Analysis with Computational Intelligence: An Analytical Survey;18
5.1;1 Credit Risk Analysis with Computational Intelligence: A Review;19
5.1.1;1.1 Introduction;19
5.1.2;1.2 Literature Collection;21
5.1.3;1.3 Literature Investigation and Analysis;23
5.1.4;1.4 Implications on Valuable Research Topics;39
5.1.5;1.5 Conclusions;40
6;Part II Unitary SVM Models with Optimal Parameter Selection for Credit Risk Evaluation;41
6.1;2 Credit Risk Assessment Using a Nearest-Point- Algorithm- based SVM with Design of Experiment for Parameter Selection;43
6.1.1;2.1 Introduction;43
6.1.2;2.2 SVM with Nearest Point Algorithm;45
6.1.3;2.3 DOE-based Parameter Selection for SVM with NPA;49
6.1.4;2.4 Experimental Analysis;51
6.1.5;2.5 Conclusions;54
6.2;3 Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection;57
6.2.1;3.1 Introduction;57
6.2.2;3.2 Methodology Description;59
6.2.3;3.3 Experimental Study;63
6.2.4;3.4 Conclusions;70
7;Part III Hybridizing SVM and Other Computational Intelligent Techniques for Credit Risk Analysis;73
7.1;4 Hybridizing Rough Sets and SVM for Credit Risk Evaluation;75
7.1.1;4.1 Introduction;75
7.1.2;4.2 Preliminaries of Rough Sets and SVM;77
7.1.3;4.3 Proposed Hybrid Intelligent Mining System;79
7.1.4;4.4 Experiment Study;84
7.1.5;4.5 Concluding Remarks;88
7.2;5 A Least Squares Fuzzy SVM Approach to Credit Risk Assessment;89
7.2.1;5.1 Introduction;89
7.2.2;5.2 Least Squares Fuzzy SVM;90
7.2.3;5.3 Experiment Analysis;97
7.2.4;5.4 Conclusions;100
7.3;6 Evaluating Credit Risk with a Bilateral-Weighted Fuzzy SVM Model;101
7.3.1;6.1 Introduction;101
7.3.2;6.2 Formulation of the Bilateral-Weighted Fuzzy SVM Model;105
7.3.3;6.3 Empirical Analysis;111
7.3.4;6.4 Conclusions;118
7.4;7 Evolving Least Squares SVM for Credit Risk Analysis;121
7.4.1;7.1 Introduction;121
7.4.2;7.2 SVM and LSSVM;124
7.4.3;7.3 Evolving LSSVM Learning Paradigm;127
7.4.4;7.4 Research Data and Comparable Models;135
7.4.5;7.5 Experimental Results;139
7.4.6;7.6 Conclusions;147
8;Part IV SVM Ensemble Learning for Credit Risk Analysis;149
8.1;8 Credit Risk Evaluation Using a Multistage SVM Ensemble Learning Approach;151
8.1.1;8.1 Introduction;151
8.1.2;8.2 Previous Studies;154
8.1.3;8.3 Formulation of SVM Ensemble Learning Paradigm;156
8.1.4;8.4 Empirical Analysis;164
8.1.5;8.5 Conclusions;170
8.2;9 Credit Risk Analysis with a SVM-based Metamodeling Ensemble Approach;173
8.2.1;9.1 Introduction;173
8.2.2;9.2 SVM-based Metamodeling Process;176
8.2.3;9.3 Experimental Analyses;189
8.2.4;9.4 Conclusions;193
8.3;10 An Evolutionary-Programming-Based Knowledge Ensemble Model for Business Credit Risk Analysis;195
8.3.1;10.1 Introduction;195
8.3.2;10.2 EP-Based Knowledge Ensemble Methodology;197
8.3.3;10.3 Research Data and Experiment Design;204
8.3.4;10.4 Experiment Results;205
8.3.5;10.5 Conclusions;211
8.4;11 An Intelligent-Agent-Based Multicriteria Fuzzy Group Decision Making Model for Credit Risk Analysis;213
8.4.1;11.1 Introduction;213
8.4.2;11.2 Methodology Formulation;217
8.4.3;11.3 Experimental Study;222
8.4.4;11.4 Conclusions and Future Directions;237
9;References;239
10;Subject Index;255
11;Biographies of Four Authors of the Book;259