Dependence modeling of multivariate longitudinal hybrid insurance data with dropout

Frees, Edward W.; Bolancé, Catalina; Guillén, Montserrat; Valdez, Emiliano A.

doi:10.1016/j.eswa.2021.115552

Cited by 6 publications

(1 citation statement)

References 34 publications

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“…We could allow dependence by incorporating unobservable policy-specific random effects or by using copula modeling. For example, Shi et al (2016Shi et al ( , 2022 and Frees et al (2021) accommodate the dependence of the multilevel structure of claims using copula modeling, and Okine et al (2022) use random effects to capture the association between the size of claims and time to settlement. However, we note that Frees et al (2016) verify that dependence modeling has little influence on the claim scores under the frequency-severity model approach.…”

Section: Out-of-sample Performancementioning

confidence: 99%

Ratemaking in a changing environment

Okine

2023

ASTIN Bull.

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In pricing insurance contracts based on the individual policyholder’s aggregate losses for non-life insurers, the literature has mainly focused on using detailed information from policies and closed claims. However, the information on open claims can reflect shifts in the distribution of the expected claim payments better than closed claims. Such shifts may be needed to be reflected in the ratemaking process earlier rather than later, especially when insurers are experiencing environmental changes. In practice, actuaries use ad hoc techniques to adjust data to current levels to determine premiums. This paper presents an intuitive ratemaking model, employing a marked Poisson process framework, which ensures that the multivariate risk analysis is done more routinely using all reported claims and makes an adjustment for Incurred But Not Reported claims. Utilizing data from the Wisconsin Local Government Property Insurance Fund, we find that by determining rates based on current data, the proposed ratemaking model leads to better alignment of premiums and provides insurers with a more financially sound portfolio.

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Section: Out-of-sample Performancementioning

confidence: 99%

Ratemaking in a changing environment

Okine

2023

ASTIN Bull.

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Case study data for joint modeling of insurance claims and lapsation

et al. 2021

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The dataset tracks 40,284 insurance clients over five years, between 2010 and 2015, who subscribed to both automobile and homeowners insurance. We have combined information on these customers. First, the characteristics including age, gender or driving experience, among others and dates of renewal for the two types of policies considered here. Note that we have only considered clients corresponding to persons and not commercial firms that can also underwrite home and motor insurance policies. Second, the policy data file for motor vehicle insurance consists of all vehicle insurance coverage including power, driving area or whether there is a second driver that drives the car occasionally. Third, the policy data file for homeowners insurance has information on the property such as value of the building (essentially the value of the home without any furniture, apparel and personal items), location and type of dwelling. Besides these three sources, we have access to data containing information on the number of claims and total cost of those claims per year and per policy type. So, for all policies that are in force, we finally have up to a five year record of the yearly cost of claims in the motor insurance and in the home coverage. If the customer does not renew one of those two policies or both, we do not have more information after this lapse occurs. After summarizing the data, we provide the usual marginal analysis, where we fit regression models using Tweedie distributions for claims and a logistic model for lapse. Data can be used for joint analysis of insurance policyholders with more than one product.

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