View : 46 Download: 0

Predictive risk analysis using a collective risk model: Choosing between past frequency and aggregate severity information

Title
Predictive risk analysis using a collective risk model: Choosing between past frequency and aggregate severity information
Authors
Oh R.Lee Y.Zhu D.Ahn J.Y.
Ewha Authors
안재윤
SCOPUS Author ID
안재윤scopus
Issue Date
2021
Journal Title
Insurance: Mathematics and Economics
ISSN
0167-6687JCR Link
Citation
Insurance: Mathematics and Economics vol. 96, pp. 127 - 139
Keywords
A posteriori risk classificationBühlmann premiumCollective risk modelPredictive analysisPremium
Publisher
Elsevier B.V.
Indexed
SCIE; SSCI; SCOPUS WOS scopus
Document Type
Article
Abstract
The typical risk classification procedure in the insurance field consists of a priori risk classification based on observable risk characteristics and a posteriori risk classification where premiums are adjusted to reflect claim histories. While using the full claim history data is optimal in a posteriori risk classification, some insurance sectors only use partial information to determine the appropriate premium to charge. Examples include auto insurance premiums being calculated based on past claim frequencies, and aggregate severities used to decide workers’ compensation. The motivation is to have a simplified and efficient a posteriori risk classification procedure, customized to the context involved. This study compares the relative efficiency of the two simplified a posteriori risk classifications, that is, those based on frequency and severity. It provides a mathematical framework to assist practitioners in choosing the most appropriate practice. © 2020 Elsevier B.V.
DOI
10.1016/j.insmatheco.2020.11.002
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE