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 asset allocation, asset and liability management, cash-flow analysis, claim costs, classification, fraud detection, insolvency, investments, loss distributions, marketing, pricing and premiums, rate-making, retention, survival analysis, and underwriting. Contents: Insurance Applications of Neural Networks, Fuzzy Logic, and Genetic Algorithms; Practical Applications of Neural Networks in Property and Casualty Insurance; An Integrated Data Mining Approach to Premium Pricing for the Automobile Insurance Industry; Population Risk Management: Reducing Costs and Managing Risk in Health Insurance; Using Neural Networks to Predict in the Marketplace; Merging Soft Computing Technologies in Insurance-Related Applications; Robustness in Bayesian Models for BonusOCoMalus Systems; Using Data Mining for Modeling Insurance Risk and Comparison of Data Mining and Linear Modeling Approaches; System Intelligence and Active Stock Trading; The Algebra of Cash Flows: Theory and Application; and other papers. Readership: Graduate students, academics, researchers and practitioners involved with actuarial science, insurance, statistics and management science."