David Hindley

Claims Reserving in General Insurance (eBook, PDF)

• Format: PDF

Produktbeschreibung

This is a comprehensive and accessible reference source that documents the theoretical and practical aspects of all the key deterministic and stochastic reserving methods that have been developed for use in general insurance. Worked examples and mathematical details are included, along with many of the broader topics associated with reserving in practice. The key features of reserving in a range of different contexts in the UK and elsewhere are also covered. The book contains material that will appeal to anyone with an interest in claims reserving. It can be used as a learning resource for actuarial students who are studying the relevant parts of their professional bodies' examinations, as well as by others who are new to the subject. More experienced insurance and other professionals can use the book to refresh or expand their knowledge in any of the wide range of reserving topics covered in the book.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 26. Oktober 2017
• Englisch
• ISBN-13: 9781108514842
• Artikelnr.: 56705717

Autorenporträt

David Hindley is an actuary who has spent over thirty years specialising in general insurance, during which time he has gained extensive practical experience of claims reserving in the UK and elsewhere. He is currently a Non-Executive Director with a number of general insurance companies. He has been a member of the Council of the Institute of Actuaries and Chair of its General Insurance Board. He is currently a member of the Actuarial Council, part of the UK Financial Reporting Council. He received an Outstanding Contribution award at the UK Actuarial Profession's 2012 General Insurance conference, in recognition of his contribution to General Insurance research.

Inhaltsangabe

1. Introduction; 2. Data; 3. Deterministic reserving methods; 4. Stochastic reserving methods; 5. Reserving in practice; 6. Selected additional reserving topics; 7. Reserving in specific contexts; Appendix A. Mathematical details for mean squared error of prediction; Appendix B. R code used for examples; References; Index.