Implementasi Metode Bayesian untuk Menghitung Premi Produk Asuransi Kendaran Bermotor dengan Pendekatan Monte Carlo Markov Chain

Boy Nathanael Situmorang; Kevina Alal A’la; Aurellia Arvianti; Feby Indriana Yusuf; Endang Wahyu Handamari

doi:10.37905/euler.v13i2.32930

Implementasi Metode Bayesian untuk Menghitung Premi Produk Asuransi Kendaran Bermotor dengan Pendekatan Monte Carlo Markov Chain

Boy Nathanael Situmorang, Kevina Alal A’la, Aurellia Arvianti, Feby Indriana Yusuf, Endang Wahyu Handamari

Abstract

Accurate premium determination is a fundamental aspect of risk management in motor vehicle insurance. This study implements the Bayesian method using a Markov Chain Monte Carlo (MCMC) approach to calculate the net premium. The aggregate claim model is constructed from a claim frequency distribution (Poisson) and a claim severity distribution (Generalized Extreme Value (GEV)), with the GEV distribution specifically chosen to model extreme claim risk. The analysis utilizes generated data for the period 2018–2024, with parameters derived from the historical data of PT Asuransi Jasa Indonesia Purwokerto (2013–2017). Parameter estimation, performed via OpenBUGS software, was validated to have achieved good convergence (MC-error ). Based on the estimated parameters, a premium of IDR 397.502.000 was obtained, calculated using the net premium principle from the expected value of aggregate claims. These results demonstrate that the Bayesian MCMC approach is effective for producing a robust premium estimation, contributing a pricing framework that explicitly accounts for extreme value claims.

Keywords

Bayesian Method; Markov Chain Monte Carlo; Poisson Distribution; GEV Distribution; Insurance Premium; Aggregate Claims

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DOI: https://doi.org/10.37905/euler.v13i2.32930