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

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Springer Science & Business Media, Apr 24, 2008 - Business & Economics - 244 pages
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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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Contents

Credit Risk Analysis with Computational Intelligence A Review
3
12 Literature Collection
5
13 Literature Investigation and Analysis
7
131 What is Credit Risk Evaluation Problem?
8
133 Comparisons of Models
17
14 Implications on Valuable Research Topics
23
15 Conclusions
24
Unitary SVM Models with Optimal Parameter Selection for Credit Risk Evaluation
25
Evolving Least Squares SVM for Credit Risk Analysis
105
72 SVM and LSSVM
108
73 Evolving LSSVM Learning Paradigm
111
732 GAbased Input Features Evolution
113
733 GAbased Parameters Evolution
117
74 Research Data and Comparable Models
119
742 Overview of Other Comparable Classification Models
121
75 Experimental Results
123

Credit Risk Assessment Using a NearestPoint Algorithmbased SVM with Design of Experiment for Parameter Selection
27
22 SVM with Nearest Point Algorithm
29
23 DOEbased Parameter Selection for SVM with NPA
33
24 Experimental Analysis
35
25 Conclusions
38
Credit Risk Evaluation Using SVM with Direct Search for Parameter Selection
40
32 Methodology Description
43
322 Direct Search for Parameter Selection
45
33 Experimental Study
47
332 Parameter Selection with Genetic Algorithm
48
333 Parameters Selection with Grid Search
49
334 Experimental Results
50
34 Conclusions
54
Hybridizing SVM and Other Computational Intelligent Techniques for Credit Risk Analysis
56
Hybridizing Rough Sets and SVM for Credit Risk Evaluation
57
42 Preliminaries of Rough Sets and SVM
61
422 Basic Ideas of Support Vector Machines
62
43 Proposed Hybrid Intelligent Mining System
63
432 2DReductions by Rough Sets
64
433 Feature selection by SVM
65
434 Rule Generation by Rough Sets
66
435 General Procedure of the Hybrid Intelligent Mining System
67
44 Experiment Study
68
441 Corporation Credit Dataset
69
442 Consumer Credit Dataset
70
45 Concluding Remarks
72
A Least Squares Fuzzy SVM Approach to Credit Risk Assessment
73
52 Least Squares Fuzzy SVM
74
522 FSVM By Lin and Wang 2002
77
523 Least Squares FSVM
79
53 Experiment Analysis
81
54 Conclusions
84
Evaluating Credit Risk with a BilateralWeighted Fuzzy SVM Model
85
62 Formulation of the BilateralWeighted Fuzzy SVM Model
89
622 Formulation Process of the Bilateralweighted fuzzy SVM
91
623 Generating Membership
93
63 Empirical Analysis
95
UK Case
96
Japanese Case
98
England Case
100
64 Conclusions
102
752 Empirical Analysis of GAbased Parameters Optimization
126
753 Comparisons with Other Classification Models
129
76 Conclusions
131
SVM Ensemble Learning for Credit Risk Analysis
132
Credit Risk Evaluation Using a Multistage SVM Ensemble Learning Approach
133
82 Previous Studies
138
83 Formulation of SVM Ensemble Learning Paradigm
140
832 Creating Diverse Neural Network Classifiers
142
833 SVM Learning and Confidence Value Generation
143
834 Selecting Appropriate Ensemble Members
144
835 Reliability Value Transformation
146
84 Empirical Analysis
148
841 Consumer Credit Risk Assessment
149
842 Corporation Credit Risk Assessment
151
85 Conclusions
154
Credit Risk Analysis with a SVMbased Metamodeling Ensemble Approach
157
92 SVMbased Metamodeling Process
160
922 An Extended Metalearning Process
163
923 SVMbased Metamodeling Process
165
93 Experimental Analyses
173
932 Experimental Results
174
94 Conclusions
177
An EvolutionaryProgrammingBased Knowledge Ensemble Model for Business Credit Risk Analysis
178
102 EPBased Knowledge Ensemble Methodology
181
1021 Brief Introduction of Individual Data Mining Models
182
1022 Knowledge Ensemble based on Individual Mining Results
185
103 Research Data and Experiment Design
188
104 Experiment Results
189
1042 Identification Performance of the Knowledge Ensemble
191
1043 Identification Performance Comparisons
193
105 Conclusions
195
An IntelligentAgentBased Multicriteria Fuzzy Group Decision Making Model for Credit Risk Analysis
197
112 Methodology Formulation
201
113 Experimental Study
206
1132 Empirical Comparisons with Different Credit Datasets
208
114 Conclusions and Future Directions
221
References
223
Subject Index
238
Biographies of Four Authors of the Book
242
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