Loss Reserving Models: Granular and Machine Learning Forms

Greg Taylor
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Greg Taylor: School of Risk and Actuarial Studies, University of New South Wales, Kensington, NSW 2052, Australia

Risks, 2019, vol. 7, issue 3, 1-18

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.

Keywords: granular models; loss reserving; machine learning; neural networks (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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