Intelligent and other computational techniques in insurance : theory and applications / editors: A.F. Shapiro, L.C. Jain.

Format:

• Book

Contributor:

• Jain, L. C.
• Shapiro, A. F.

Series:

• Series on innovative intelligence ; v. 6.
• Series on innovative intelligence ; v. 6

Language:

English

Subjects (All):

• Artificial intelligence.
• Insurance companies--Data processing.
• Insurance--Data processing.

Physical Description:

1 online resource (690 p.)

Place of Publication:

River Edge, N.J. : World Scientific Pub., c2003.

Language Note:

English

Summary:

This book presents recent advances in the theory and implementation of intelligent and other computational techniques in the insurance industry. The paradigms covered encompass artificial neural networks and fuzzy systems, including clustering versions, optimization and resampling methods, algebraic and Bayesian models, decision trees and regression splines. Thus, the focus is not just on intelligent techniques, although these constitute a major component; the book also deals with other current computational paradigms that are likely to impact on the industry. The application areas include as

Contents:

• Contents ; Foreword ; Preface ; Part 1 Neural networks fuzzy systems and genetic algorithms ; Chapter 1. Insurance applications of neural networks fuzzy logic and genetic algorithms ; 1 Introduction ; 2 Neural network (NN) applications ; 2.1 An overview of NNs ; 2.2 Applications
• 3 Fuzzy logic (FL) applications 3.1 An overview of FL ; 3.2 Applications ; 4 Genetic algorithm (GA) applications ; 4.1 An overview of GAs ; 4.2 Applications ; 5 Comment ; Acknowledgments ; References ; Property and casualty
• Chapter 2. An introduction to neural networks in insurance 1 Introduction ; 2 Background on neural networks ; 2.1 Structure of a feedforward neural network ; 3 Example 1: simple example of fitting a nonlinear function to claim severity ; 3.1 Severity trend models
• 3.2 A one node neural network 3.3 The logistic function revisited ; 4 Example 2: using neural networks to fit a complex nonlinear function ; 4.1 The chain ladder method ; 4.2 Modeling loss development using a two-variable neural network ; 4.3 Interactions
• 5 Correlated variables and dimension reduction 5.1 Factor analysis and principal components analysis ; 5.2 Example 3: dimension reduction ; 6 Conclusion ; Acknowledgments ; References ; Chapter 3. Practical applications of neural networks in property and casualty insurance
• 1 Introduction

Notes:

• Description based upon print version of record.
• Includes bibliographical references and index.

ISBN:

• 9786611934415
• 9781281934413
• 1281934410
• 9789812794246
• 9812794247

OCLC:

879023684