TY - JOUR
T1 - Predictive risk analysis using a collective risk model
T2 - Choosing between past frequency and aggregate severity information
AU - Oh, Rosy
AU - Lee, Youngju
AU - Zhu, Dan
AU - Ahn, Jae Youn
N1 - Funding Information:
Rosy Oh was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( 2019R1A6A1A11051177 and 2020R1I1A1A01067376 ). Jae Youn Ahn was supported by an NRF grant funded by the Korean Government ( 2020R1F1A1A01061202 ).
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/1
Y1 - 2021/1
N2 - 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.
AB - 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.
KW - A posteriori risk classification
KW - Bühlmann premium
KW - Collective risk model
KW - Predictive analysis
KW - Premium
UR - http://www.scopus.com/inward/record.url?scp=85096902778&partnerID=8YFLogxK
U2 - 10.1016/j.insmatheco.2020.11.002
DO - 10.1016/j.insmatheco.2020.11.002
M3 - Article
AN - SCOPUS:85096902778
VL - 96
SP - 127
EP - 139
JO - Insurance: Mathematics and Economics
JF - Insurance: Mathematics and Economics
SN - 0167-6687
ER -