Risk Classification as an Incentive to Taxpayers’ Compliance

2018 ◽  
Vol 19 (2) ◽  
Keyword(s):  
Risk Classification

A Novel Approach for Labelling Health Insurance Data for Cardiovascular Disease Risk Classification

2016 ◽  
Author(s):  
César L. C. Mattos ◽  
Amauri H. Souza Júnior ◽  
Ajalmar R. Rocha Neto ◽  
Guilherme A. Barreto ◽  
Ronaldo F. Ramos ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Health Insurance ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Risk Classification ◽  
Health Insurance Data ◽  
Novel Approach ◽  
Insurance Data

scholarly journals Immunohistochemical biomarkers are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk classification in endometrial cancer

2021 ◽  
Author(s):  
S.W. Vrede ◽  
W.J. van Weelden ◽  
N.C.M. Visser ◽  
J. Bulten ◽  
L.J.M. van der Putten ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Risk Classification

scholarly journals Designing a Bonus-Malus system reflecting the claim size under the dependent frequency–severity model

2021 ◽  
pp. 1-25
Author(s):  
Rosy Oh ◽  
Joseph H.T. Kim ◽  
Jae Youn Ahn
Keyword(s):  
Recent Literature ◽  
Insurance Industry ◽  
Random Effect ◽  
Random Effect Model ◽  
Partial Solution ◽  
Risk Classification ◽  
Transition Rule ◽  
Auto Insurance ◽  
Dependent Frequency ◽  
Effect Model

In the auto insurance industry, a Bonus-Malus System (BMS) is commonly used as a posteriori risk classification mechanism to set the premium for the next contract period based on a policyholder's claim history. Even though the recent literature reports evidence of a significant dependence between frequency and severity, the current BMS practice is to use a frequency-based transition rule while ignoring severity information. Although Oh et al. [(2020). Bonus-Malus premiums under the dependent frequency-severity modeling. Scandinavian Actuarial Journal 2020(3): 172–195] claimed that the frequency-driven BMS transition rule can accommodate the dependence between frequency and severity, their proposal is only a partial solution, as the transition rule still completely ignores the claim severity and is unable to penalize large claims. In this study, we propose to use the BMS with a transition rule based on both frequency and size of claim, based on the bivariate random effect model, which conveniently allows dependence between frequency and severity. We analytically derive the optimal relativities under the proposed BMS framework and show that the proposed BMS outperforms the existing frequency-driven BMS. Later, numerical experiments are also provided using both hypothetical and actual datasets in order to assess the effect of various dependencies on the BMS risk classification and confirm our theoretical findings.

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