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Contact Name
Imam Mukhlash
Contact Email
imamm@matematika.its.ac.id
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+6285648721814
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ijcsam.matematika@its.ac.id
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Departemen Matematika, Gedung F Lantai II, Kampus ITS, Keputih, Sukolilo-Surabaya 60111 Jawa Timur, Indonesia Phone: +62 31-5943354 Email:ijcsam.matematika@its.ac.id
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Kota surabaya,
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INDONESIA
International Journal of Computing Science and Applied Mathematics-IJCSAM
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : -     EISSN : 24775401     DOI : -
Core Subject : Education,
Mathematics
IJCSAM (International Journal of Computing Science and Applied Mathematics) is an open access journal publishing advanced results in the fields of computations, science and applied mathematics, as mentioned explicitly in the scope of the journal. The journal is geared towards dissemination of original research and practical contributions by both scientists and engineers, from both academia and industry. IJCSAM (International Journal of Computing Science and Applied Mathematics) is a journal published by Pusat Publikasi Ilmiah LPPM, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Arjuna Subject : Matematika - Matematika Terapan
Articles 151 Documents
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A Predictive Framework for Transition ProbabilityShifts Using Environmental Data Via Lee-CarterModel Extension Kenneth Gunawan; Nicholas Andrew Tanubrata; Rex Aaron Holindo; Ferry Vincenttius Ferdinand; Helena Margaretha
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.10834

Abstract

This study addresses the challenge of incorporatingenvironmental data, particularly air pollution, into practicalactuarial models. This study extends the Lee-Carter modelusing environmental variables and employs LASSO regressionto address potential multicollinearity among pollution indicators.The results are closed-form approximations for estimating theexpected changes in transition probabilities from the previoustransition probabilities forecast within both multiple-state andmultiple-decrement models, which are calculated using currentenvironmental data. These approximations enable actuaries toquantify the expected difference in transition probabilities fromthe initial forecast by utilizing the newest environmental data thatwas previously unavailable. The framework is empirically testedusing a case study on a dementia-related multiple-state modelin the UK. Results show significant improvements in predictiveaccuracy, measured by reductions in Mean Absolute PercentageError (MAPE), reducing MAPE by over 40% in several agegroups. These results emphasize the value of environmentalintegration in actuarial modeling, particularly in the context ofhealth-related insurance products

Page 16 of 16 | Total Record : 151

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