索赔模型:颗粒和机器学习形式
引用次数: 3
摘要
本文的目的是调查损失保留的颗粒模型和机器学习模型的最新发展，并比较这两个家族，以评估它们未来的发展潜力。最好是在这些模型从它们的前辈进化的背景下理解这一点，前面的部分叙述了损失保存历史上相关的考古小插曲。然而，本文的大部分内容是关于颗粒模型和机器学习模型的。讨论了它们的相对优点，以及决定在它们和更古老、更原始的模型之间进行选择的因素。结论部分简要地考虑了这些模型在未来可能的进一步发展。本文章由计算机程序翻译，如有差异，请以英文原文为准。
Claim Models: Granular and Machine Learning Forms
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.
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.
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