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MULTI-STATE MODEL FOR CALCULATION OF LONG-TERM CARE INSURANCE PRODUCT PREMIUM IN INDONESIA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 16 No. 4 (2022) · pp. 1293–1302

DOI
10.30598/barekengvol16iss4pp1293-1302
ISSN
2615-3017, 1978-7227
Published
2022-12-15
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Long Term Care (LTC) insurance is a type of health insurance. One of the LTC products is Annuity as A Rider Benefit. This insurance provides benefits for medical care costs during the term and death benefits if the insured dies. This insurance product can be modeled with a multi-state model. The multi-state model is a stochastic process in which the subject can switch states at a specified number of states. This paper discusses the calculation of LTC insurance premiums with the Annuity as A Rider Benefit product using a multi-state model for critically ill patients in Indonesia. The state used consisted of eight states, namely healthy, cancer, heart disease, stroke, died from the illness from each disease, and died from others. The premium calculation also utilized Markov chain transition probabilities. The data used were data on Indonesia's population in 2018, data on the prevalence of cancer, heart disease, stroke, and Indonesia's 2019 mortality table. The stages of this study were calculating the net single premium value, benefit annuity value, and insurance premium value. The case study was conducted on a 25 years old male in good health following LTC insurance with a coverage period of 5 years. It was known that the compensation value for someone who dies was IDR 100,000,000 and the interest rate used was 5%. The calculation results obtained an annual premium of IDR 5,308,915 which was then varied based on gender and varied interest. Insurance premiums for men were more expensive than for women since men had a greater chance of dying. Then, the higher the interest rate taken; the lower premium paid. This was because the interest rate is a discount variable.

Collaboration network of the authors

40 authors · 90 co-authorship links · 2 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Tanjungpura University
  • Badan Pusat Statistik
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Evy SulistianingsihTanjungpura University10
Siti AprizkiyandariTanjungpura University6
Naomi Nessyana DebatarajaTanjungpura University5
Wirda AndaniTanjungpura University4
Hani Dwi RetnaniTanjungpura University3
Inggriani Millennia TaralyTanjungpura University3
Dinda TiaraTanjungpura University3
Shantika MarthaTanjungpura University2
Steven Jansen SinagaTanjungpura University2
Widad ImtiyazTanjungpura University2
Fritzgerald Muhammad ArsyiTanjungpura University2
Muhammad Ilham FauzanTanjungpura University2
Nabila Izza HarnantaTanjungpura University2
Nurhanifa NurhanifaTanjungpura University2
Yundari YundariTanjungpura University1
Setyo Wira RizkiTanjungpura University1
Shindy UtariTanjungpura University1
Geby MarolaTanjungpura University1
Rita ApriliyantiTanjungpura University1
Tiffany ArdhithaTanjungpura University1
Indah OktavianiTanjungpura University1
Maria Welita DwinandaTanjungpura University1
Lisa Listri LestariTanjungpura University1
Aditya Nugraha PratamaTanjungpura University1
Nurfitri Imro’ahTanjungpura University1
Herina MarlisaTanjungpura University1
Rifkah Alfiyyah SaryTanjungpura University1
Cristy Framedia CahyaniTanjungpura University1
Ainun ZakiahTanjungpura University1
Noerul HaninTanjungpura University1
David Jordy DhandioTanjungpura University1
M. Arthur SimanjuntakTanjungpura University1
HAIRIL AL-HAMTanjungpura University1
Preatin PreatinBadan Pusat Statistik1
Louis Putra JayaTanjungpura University1
Hendri KurniawanTanjungpura University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">MULTI-STATE MODEL FOR CALCULATION OF LONG-TERM CARE INSURANCE PRODUCT PREMIUM IN INDONESIA</dc:title>
	<dc:creator>Perdana, Hendra</dc:creator>
	<dc:creator>Satyahadewi, Neva</dc:creator>
	<dc:creator>Kusnandar, Dadan</dc:creator>
	<dc:creator>Tamtama, Ray</dc:creator>
	<dc:description xml:lang="en-US">Long Term Care (LTC) insurance is a type of health insurance. One of the LTC products is Annuity as A Rider Benefit. This insurance provides benefits for medical care costs during the term and death benefits if the insured dies. This insurance product can be modeled with a multi-state model. The multi-state model is a stochastic process in which the subject can switch states at a specified number of states. This paper discusses the calculation of LTC insurance premiums with the Annuity as A Rider Benefit product using a multi-state model for critically ill patients in Indonesia. The state used consisted of eight states, namely healthy, cancer, heart disease, stroke, died from the illness from each disease, and died from others. The premium calculation also utilized Markov chain transition probabilities. The data used were data on Indonesia's population in 2018, data on the prevalence of cancer, heart disease, stroke, and Indonesia's 2019 mortality table. The stages of this study were calculating the net single premium value, benefit annuity value, and insurance premium value. The case study was conducted on a 25 years old male in good health following LTC insurance with a coverage period of 5 years. It was known that the compensation value for someone who dies was IDR 100,000,000 and the interest rate used was 5%. The calculation results obtained an annual premium of IDR 5,308,915 which was then varied based on gender and varied interest. Insurance premiums for men were more expensive than for women since men had a greater chance of dying. Then, the higher the interest rate taken; the lower premium paid. This was because the interest rate is a discount variable.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2022-12-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6263</dc:identifier>
	<dc:identifier>10.30598/barekengvol16iss4pp1293-1302</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications; 1293-1302</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol16iss4year2022</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/6263/5145</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Hendra Perdana, Neva Satyahadewi, Dadan Kusnandar, Ray Tamtama</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>