Implementation of Gaussian Process Regression in Estimating Motor Vehicle Insurance Claims Reserves

Suwandani, Ria Novita; Purwono, Yogo

doi:10.47616/jamrems.v2i1.77

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Aligned with such conceptual appeal, there have been several BNP frameworks studied and applied in actuarial practice recently, such as the Gaussian process, Dirichlet process, and Pitman-Yor process. (e.g., Suwandani and Purwono 2021;Hong and Martin 2018;Shams Esfand Abadi 2022). Focusing on the Dirichlet process prior, Hong and Martin (2018) recently developed the Dirichlet process mixture (DPM) model as a BNP approach that maximizes the fitting flexibility of the loss distribution with the presence of unknown risk classes.…”

Section: Introductionmentioning

confidence: 99%

Dirichlet Process Log Skew-Normal Mixture with a Missing-at-Random-Covariate in Insurance Claim Analysis

Kim,

Lindberg,

Crane

et al. 2023

Econometrics

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In actuarial practice, the modeling of total losses tied to a certain policy is a nontrivial task due to complex distributional features. In the recent literature, the application of the Dirichlet process mixture for insurance loss has been proposed to eliminate the risk of model misspecification biases. However, the effect of covariates as well as missing covariates in the modeling framework is rarely studied. In this article, we propose novel connections among a covariate-dependent Dirichlet process mixture, log-normal convolution, and missing covariate imputation. As a generative approach, our framework models the joint of outcome and covariates, which allows us to impute missing covariates under the assumption of missingness at random. The performance is assessed by applying our model to several insurance datasets of varying size and data missingness from the literature, and the empirical results demonstrate the benefit of our model compared with the existing actuarial models, such as the Tweedie-based generalized linear model, generalized additive model, or multivariate adaptive regression spline.

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Section: Introductionmentioning

confidence: 99%

Dirichlet Process Log Skew-Normal Mixture with a Missing-at-Random-Covariate in Insurance Claim Analysis

Kim,

Lindberg,

Crane

et al. 2023

Econometrics

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Development of the Black–Scholes Model for Determining Insurance Premiums to Mitigate the Risk of Disaster Losses Using the Principles of Mutual Cooperation and Regional Economic Growth

Purwandari,

Hidayat,

Sukono

et al. 2024

Risks

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The frequency and economic damage of natural disasters have increased globally over the last two decades due to climate change. This increase has an impact on the disaster insurance field, particularly in the calculation of premiums. Many regions have a shortcoming in employing insurance because the premium is too high compared with their budget allocation. As one of the solutions, the premium calculation can be developed by applying the cross-subsidies mechanism based on economic growth. Therefore, this research aims to develop premium models of natural disaster insurance that uniquely involve two new variables of an insured region: cross-subsidies and the economic growth rate. Another novelty is the development of the Black–Scholes model, considering the two new variables, and it is used to formulate the premium model. Following the modeling process, this study uses the model to estimate the premiums for natural disaster insurance in each province of Indonesia. The estimation results show that all new variables involved in the model novelties significantly affect the premiums. This research can be used by insurance companies to determine the premium of natural disaster insurance, which involves cross-subsidies and economic growth.

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Implementation of Gaussian Process Regression in Estimating Motor Vehicle Insurance Claims Reserves

Cited by 2 publications

References 15 publications

Dirichlet Process Log Skew-Normal Mixture with a Missing-at-Random-Covariate in Insurance Claim Analysis

Dirichlet Process Log Skew-Normal Mixture with a Missing-at-Random-Covariate in Insurance Claim Analysis

Development of the Black–Scholes Model for Determining Insurance Premiums to Mitigate the Risk of Disaster Losses Using the Principles of Mutual Cooperation and Regional Economic Growth

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