Bio-Inspired Credit Risk Analysis

Computational Intelligence with Support Vector Machines
Softcover Nachdruck of hardcover 1. Auflage 2008
ISBN: 978-3-642-09655-6
Verlag: Springer

Buch, Englisch, 244 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 400 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Yu, Lean

Zhou, Ligang

Wang, Shouyang

Lai, Kin Keung

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Risikomanagement
• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Bankwirtschaft
• Technische Wissenschaften Technik Allgemein Bionik, Biomimetik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

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.