Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257920 
Authors: 
Year of Publication: 
2019
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 7 [Issue:] 3 [Article No.:] 82 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-18
Publisher: 
MDPI, Basel
Abstract: 
The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these models from their predecessors, and the early sections recount relevant archaeological vignettes from the history of loss reserving. However, the larger part of the paper is concerned with the granular models and machine learning models. Their relative merits are discussed, as are the factors governing the choice between them and the older, more primitive models. Concluding sections briefly consider the possible further development of these models in the future.
Subjects: 
neural networks
loss reserving
machine learning
granular models
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
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