• Andini Setyo Anggraeni Institut Teknologi Batam
Keywords: Sickness Insurance, Medical Reimburstment, Premium, Daily Benefit


The purpose of this study is to calculate premiums for sickness insurance using Helligman Pollard's law of mortality. This research will focus on Medical Reimburstment Premium and Daily Benefit Premiums. Data on the size of claims, z (frequency of claims per individual) and d (duration of claims) where z has a Poisson distribution (0.8) and claims size is lognormally distributed (1,5,1). The premium calculation will be aggregated for 5 years and starting from the age of 15 years to 70 years. The premium for daily benefits is higher than the premium for medical reimbursement for all ages. The lowest premium is premium for 25-30 years old, which is 8.518657957 for medical reimbursement premium and 41.79084811 for daily benefit premium, while the highest premium is for 65-70 years old, which is 114.2254996 for medical reimbursement premium and 258.6346296 for daily benefit premiums.

Keywords       : Sickness Insurance, Premium, Medical Reimburstment, Daily Benefit


Bartleson, E. L. 1968. Health Insurance. USA : The Society of Actuaries, Illinois.

Benjamin, B., Pollard, J. H. 1993. The Analysis of Mortality and Other Actuarial Statistics. Oxford: The Institute of Actuaries.

Bowers, Newton L. 1997. Actuarial Mathematics Second Edition. USA: The Society of Actuaries.

D. J. Sharrow. 2012. HPbayes: Heligman Pollard mortality model parameter estimation using Bayesian Melding with Incremental Mixture Importance Sampling. R package version 0.1.

Effendie, A. R. 2015. Matematika Aktuaria dengan Software R.Yogyakarta: Gadjah Mada University Press.

Emilidha, Wella Pasca., Danardono. 2017. Modelling hospital mortality data using the Helligman-Pollard model with R HPBayes. AIP Conference Proceedings 1827, 020025 (2017);

M. A. Heligman and J. H. Pollard. 1980. The age pattern of mortality. Journal of the Institute of Actuaries; 107: 49-80

Pitacco E. 1997. Health Insurance Basic Actuarial Models: Switzerland : European Actuarial Academy, Springer International Publishing

R Core Team. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna, Austria. URL

K.L. Kahn, R.H. Brook, D. Draper, E.B Keeler, L.V. Rubenstein, W.H. Rogers and J. Kosecoff. Interpreting hospital mortality data: How can we proceed?. JAMA 1988;260(24):3625-3628.