This book reviews the application of different statistical and Soft-Computing (SC) techniques in insurance-related problems. The book has been divided into 5 chapters, with the following structure: Chapter 1 provides a comprehensive review of SC techniques in insurance problems. A review of the application of these methods in insurance problems and a case study in a real problem completes this first chapter. Chapters 2 and 3 describe two different real applications of SC techniques in insurance. In this chapter, the authors describe the main concepts related to the algorithm and discuss different application of the algorithm in the insurance sector. Chapter 3 is devoted to a state-of-the-art technique in neural computation (Support Vector Machines, SVM). Chapter 4 presents a new mathematical tool based on modal intervals that allow us to process interval data (reflecting the fact that the data we are working with is not exact) and to interpret semantically the results obtained. Chapter 5 concentrates on two well-known risk measures: the Value at Risk and the Tail Value at Risk. The authors present a new analytical expression of the Tail Value at Risk using the Normal-Power approximation and they analyse its precision
Foreword; A Review of Computational Intelligence Algorithms in Insurance Applications; Rough Sets in Insurance Sector; Prediction of Claims & Selection of Risk Factors in Automobile Insurance Using Support Vector Machines, Genetic Algorithms & Classification Trees; Tail Value at Risk. An Analysis with the Normal-Power Approximation; Financial Applications of Modal Interval Analysis; Index.
