Experience-Rating Mechanisms in Auto Insurance: Implications for High-Risk, Low-Risk, and Novice Drivers

Isotupa, K. P. Sapna; Kelly, Mary; Kleffner, Anne

doi:10.1080/10920277.2019.1572524

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…They do, however, become more skilled and experienced as a result of being in their cars for extended lengths of time (Azaare et al, 2021). As a result of their competence and experience, they are less likely to be involved in an accident (Isotupa et al, 2019). Therefore, insurers and policymakers could find other variables more technological in pricing instead of relying on this classical variable, which ends up with high premiums for policyholders just because they have cars with high CC.…”

Section: Discussionmentioning

confidence: 99%

“…Automobile insurance was introduce to safeguard policyholders from potentially enormous financial losses as well as losses to third parties. However, the premium, which is computed based on the policyholder’s risk, can influence the decision to purchase a policy (Alhassan, 2016; Alhassan et al, 2015; Alhassan & Biekpe, 2016a; Azaare et al, 2021; Bülbül & Baykal, 2016; Isotupa et al, 2019). Majority of studies look at premium estimates from a traditional perspective on the basis of claims frequency and severity from policyholders’ (Azaare et al, 2022; Deniut et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Automobile insurance, particularly the third-party (TP) category, is mandatory in most countries (Aeron-Thomas, 2002; Bülbül & Baykal, 2016; Isotupa et al, 2019; Lemaire, 1998). Automobile use has embedded morbidity and mortality risk, as well as loss through theft and fire in several countries (Lemaire, 2004), and thus its associated insurance was developed to handle the potential loss (Boucher et al, 2009; Frangos & Vrontos, 2001).…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Effects of Classical Auto Insurance Rating Variables on Premium in ARDL: Is the high Policyholders’ Premium in Ghana Justified?

2022

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To better understand the actual rating variables that affects Auto insurance Policyholders’ premium, this paper attempts to provide empirical evidence to justify which ones are significant and needed to be considered by insurers by adopting the autoregressive distributed lag (ARDL) model. In satisfying all the conditions for ARDL application, unit root, Heteroskedasticity, normality, dynamic stability and serial correlation tests were conducted. We estimate the effects of each rating variable on Premium taking into consideration whether the policy of the insured is Third-Party or Comprehensive. These rating variables in the ARDL model serves as the independent variables that establishes the short and long-run relationships between them and the Premium as the dependent variable. The results suggest that not all the classical rating variables used in the market significantly impact Premium. Whiles, to some extent, we found a varying degree of variables impact on Premium depending on the insurance type, the autos cubic capacity, which plays a cogent role on the basic Premium in Ghana, is insignificant. Also, policyholders’ age characteristics are statistically significant but are excluded in the premium calculations. Thus, this paper shows the need to consider all the other possible rating variables, including policyholders’ age into the Ghanaian insurance pricing system, whiles autos cubic capacity considering the weight it put on the basic Premium should be re-examined. This would help to obtain a financially balanced and optimal pricing system for policyholders.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the Effects of Classical Auto Insurance Rating Variables on Premium in ARDL: Is the high Policyholders’ Premium in Ghana Justified?

2022

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“…Furthermore, regulation efforts have been made to improve fairness by restricting the use of some risk factors, such as gender, eliminating gender discrimination (Ryan 1986;Schmeiser et al 2014). However, since the fairness of the classification mechanism is part of the standard regulatory goals, charging an excessive premium for high-risk drivers may conflict with the fairness objective (Isotupa et al 2019). In Isotupa et al (2019), two experience-rating mechanisms were used to rate a group of drivers, where findings conclude that high driving record (DR) classes cause the highest risk drivers to pay unsustainable excessive premiums.…”

Section: Introductionmentioning

confidence: 99%

“…However, since the fairness of the classification mechanism is part of the standard regulatory goals, charging an excessive premium for high-risk drivers may conflict with the fairness objective (Isotupa et al 2019). In Isotupa et al (2019), two experience-rating mechanisms were used to rate a group of drivers, where findings conclude that high driving record (DR) classes cause the highest risk drivers to pay unsustainable excessive premiums. The driving record in auto insurance refers to the total number of years of no accidents, and it is often capped at a maximum level, for example, six years.…”

Section: Introductionmentioning

confidence: 99%

Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data

Xie

Luo

2022

Risks

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The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor adjustments to risk relativities allowed by regulation rules, the rates charged eventually may not align with the empirical risk relativities calculated from insurance loss data. Therefore, investigating the relationship between the premium rates and loss costs at different risk factor levels becomes important for studying insurance fairness, particularly from rate regulation perspectives. This work applies statistical models to rate and classification data from the automobile statistical plan to investigate the disparities between insurance premiums and loss costs. The focus is on major risk factors used in the rate regulation, as our goal is to address fairness at the industry level. Various statistical models have been constructed to validate the suitableness of the proposed methods that determine a fixed effect. The fixed effect caused by the disparity of loss cost and premium rates is estimated by those statistical models. Using Canadian data, we found that there are no significant excessive premiums charged at the industry level, but the disparity between loss cost and premiums is high for urban drivers at the industry level. This study will help better understand the extent of auto insurance fairness at the industry level across different insured groups characterized by risk factor levels. The proposed fixed-effect models can also reveal the overall average loss ratio, which can tell us the fairness at the industry level when compared to loss ratios by the regulation rules.

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Validating the Vitals assessment: A replication study on cognitive assessments and commercial driving risk

Atkin,

Scott,

Singhal

2024

Journal of Transport & Health

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Experience-Rating Mechanisms in Auto Insurance: Implications for High-Risk, Low-Risk, and Novice Drivers

Cited by 4 publications

References 23 publications

Exploring the Effects of Classical Auto Insurance Rating Variables on Premium in ARDL: Is the high Policyholders’ Premium in Ghana Justified?

Exploring the Effects of Classical Auto Insurance Rating Variables on Premium in ARDL: Is the high Policyholders’ Premium in Ghana Justified?

Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data

Validating the Vitals assessment: A replication study on cognitive assessments and commercial driving risk

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