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

Authors: Dr. Lean Yu, Prof. Dr. Shouyang Wang, Prof. Dr. Kin Keung Lai, Dr. Ligang Zhou



Presentation of some of the most important advancements in credit risk analysis with SVM and some fully novel intelligent models for credit risk analysis

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.

Table of contents (11 chapters)

Credit Risk Analysis with Computational Intelligence: A Review

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 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

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