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
- 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
The main purpose of this chapter is to describe the types of data that are used for estimating claims reserves, using one or more of the methods described in Chapters 3 and 4. In this context, claims reserves are assumed to represent case reserves plus IBNER and IBNR, as defined in Section 1.1. The data that are needed for estimating categories of reserves other than claims reserves (e.g. reinsurance bad debt or unallocated loss adjustment expenses) and for certain specific types of claim (e.g. latent claims) or business are covered in Chapters 5 and 7.
The term “data” as used here is intended to mean both quantitative and qualitative information – in effect, any type of information that is used as part of the reserving process. However, since most reserving methods are applied to quantitative information, this chapter focuses on that type of data. Nevertheless, collation of qualitative information is an essential part of any reserving exercise, and so reference is also made to that type of data, with further details in general terms being given in Chapter 5 and in specific contexts in Chapter 7.
After first introducing the claims development and data triangle concepts, the remainder of this chapter gives a description of the different types of reserving data, followed by some further details related to data triangles, and then a discussion of the important topic of grouping and subdivision of reserving data. The chapter concludes with some examples of the common data quality issues encountered in practice. A more complete understanding of these issues and of other data-related practical points is best achieved when considered in the context of the application of the key reserving methods, which are described in Chapters 3 and 4. This is also partly why some of the detailed data topics related to specific methods are dealt with in these later chapters, rather than in this chapter, which focuses on more generic data topics. Hence, readers who are new to reserving may need to re-read this chapter after gaining at least an initial understanding of the key reserving methods.
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- Claims Reserving in General Insurance , pp. 16 - 39Publisher: Cambridge University PressPrint publication year: 2017