Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgements
- 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
7 - Reserving in Specific Contexts
Published online by Cambridge University Press: 27 October 2017
- Frontmatter
- Contents
- Preface
- Acknowledgements
- 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
Summary
Introduction
This chapter provides details of selected key features of reserving in a range of specific contexts. For each territory/class considered, matters such as a description of the class, data types and example reserving methods are given. The drafting of this chapter has taken into account input from individuals or firms who have particular knowledge and experience in each of the relevant areas, as noted in the acknowledgements in Section 1.6. The generic matters that are relevant to all reserving exercises, such as understanding the business, checking the data and using homogeneous data groupings, are not repeated here, as they are already covered in the earlier chapters. Similarly, the generic points related to the application of individual reserving methods are also not repeated here, as they are covered in the description of each method in the relevant chapters.
The purpose of collating these details is to give some preliminary information in relation to reserving in the relevant contexts. For each context, it is assumed that themain purpose of the reserving exercise is to determine a point estimate of the reserves for financial reporting purposes. Partly as a consequence of this, the reserving methods referred to are mostly deterministic in nature. In practice, stochastic methods are also likely to be used for some purposes in a number of the different contexts. Where more than one reserving method is mentioned, it is common in most contexts for the final selected results to be based on a combination of different methods, which may vary across the cohorts, as explained in general terms in Section 5.7.
This chapter is not intended to be an instruction manual for the application of the reserving methods described in the earlier chapters, and does not represent any form of recommended approach, guidance or actuarial/professional standard. In practice, for each territory/class there will be many variations and additional detail beyond that described here, which in most cases will be learned through several years of practical experience. Furthermore, in some cases, the application of reserving methods will differ from that described here, perhaps significantly so, due to the specific circumstances.
The various subdivisions of data given for each territory/class are not mutually exclusive and combinations of them may often be used in practice.
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- Claims Reserving in General Insurance , pp. 410 - 464Publisher: Cambridge University PressPrint publication year: 2017