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All Journal Pythagoras: Jurnal Matematika dan Pendidikan Matematika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Gaussian Indonesian Journal of Applied Statistics Desimal: Jurnal Matematika Syntax Literate: Jurnal Ilmiah Indonesia Journal of Data Analysis InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Statistika dan Aplikasinya Premium: Insurance Business Journal Jurnal Riset Statistika Pattimura Proceeding : Conference of Science and Technology Bandung Conference Series: Statistics
Aceng Komarudin Mutaqin
Program Studi Statistika Universitas Islam Bandung
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Journal : JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI

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Modeling Claim Frequency in Indonesia Auto Insurance Using Generalized Poisson-Lindley Linear Model Mardianto Karim; Aceng Komarudin Mutaqin
Jurnal Matematika, Statistika dan Komputasi Vol. 16 No. 3 (2020): JMSK, MAY, 2020
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (633.014 KB) | DOI: 10.20956/jmsk.v16i3.9315

Abstract

This paper will discuss the modeling of claim frequency from Indonesian auto insurance using the generalized Poisson-Lindley linear model. This modeling method assumes that the data of claim frequency are from populations that follow generalized Poisson-Lindley distribution. Generalized Poisson-Lindley linear model is an alternative to modeling count data that contains overdispersion. The parameters in the generalized Poisson-Lindley linear model can be estimated using the maximum likelihood estimation method through Newton Raphson's iteration numerical method. The data are the secondary data took from XYZ Company for the 2013 policy which is overdispersed. The data contains policyholder partial loss claims for comprehensive motor vehicle insurance products. From the research conducted it was concluded that the data is suitable to be modeled with generalized Poisson-Lindley linear models and produce better models than ordinary Poisson linear modeling because of produced the smaller AIC value. Of the 3 predictor variables that are modeled on the frequency of claims, 2 variables influenced they are the use variable and vehicle brand variable.
Ukuran-Ukuran Aktuaria untuk Data Besar Klaim Berdistribusi Inverse Gaussian Fauziah Rahmayanti; Aceng Komarudin Mutaqin
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.30067

Abstract

An insurance company must be able to manage risks in the form of claims submitted by policyholders. There are several risk measures or actuarial measures that can be used to predict future risks and help companies prepare reserves. These actuarial measures are Value at Risk (VaR), Tail Value at Risk (TVaR), Tail Variance (TV), and Tail Variance Premium (TVP). In this article, we will discuss these actuarial measures for inverse Gaussian distributed claim severity. The Kolmogorov-Smirnov test is used to test the fit of the inverse Gaussian distribution. The maximum likelihood estimator is used as a method to estimate the parameters of the inverse Gaussian distribution. The data used in this article is data on partial loss claims for motor vehicle insurance insurance company PT. ABC in 2019 Category 1 in all regions. However, after testing the goodness of fit of the distribution using the Kolmogorov-Smirnov test for region 3, it did not come from a population with an inverse Gaussian distribution. So the data used to proceed to the actuarial measures estimation stage is only region 1 and region 2. Based on the results of calculating the actuarial measures for inverse Gaussian distributed claim severity, it can be concluded that the value of losses expected by a company can be calculated by taking into account the actuarial measures for claim severity on motor vehicle insurance in Indonesia.
Co-Authors Abdul Kudus Agung Muhammad Fikri Agus Yodi Gunawan Ainani Tajriyan Muntaharridwan Ajeng Mega Pratiwi Alfan Siam Nuri Andi Setia Nugraha Anggis Sagitarisman Anneke Iswani A. Anneke Iswani Ahmad Asep Syaifulhadi Aunuddin Aunuddin Avin Rahmadian Budi Susetyo Danang Setiawan Disa Fauzana Dumaria R. Tampubolon Dwi Sekar Kania Fachry Abda El Rahman Fakhriel Muhammad Hafizh Fauziah Rahmayanti Fauziah Rahmayanti Fera Shintarini Harmonia Kamil Hendrik Wijayanto Ikbar Farid Maulana Indah Permatasari Indah Permatasari Inne Marliani Khalimatus Sa'diah Khalis Syahril Suryana Kiki Yogaswara Komarudin Komarudin Lia Rahayu Linda Maliani Lisnur Wachidah Lukman Nul Hakim M Dziqri Nur Rohiim M Dziqri Nur Rohiim M. Wildan Bin H.M. Yahya Mardianto Karim Mardianto Karim Maulana Ilham Pratama Maya Dian Wati Mira Zakiah Rahmah Mohammand Sobri Muhammad Iqbal Fauzi Muhammad Rifqi Hendriawan Muhammad Rizq Nafisyah Alam Muhammad Rizq Nafisyah Alam Naufal Fajar Nina Fitriyati Nur Rofiq Azijah NURTITI SUNUSI Nusar Hajarisman Puput Aryanti Raisha Shahelia Nastiti Resyi Pandini Safitri Riki Kurniawan Rillifa Iris Adisti Roosdiana N.R. Sa'diah, Khalimatus Sabda Iman Dani Safira Pratiwi Sheli Andriani Sinta Asanah Siti Magfiratul Hasanah Sutawanir Darwis Suwanda Suwanda Syahla Anisah Tanti Susanti Wulan Jati Nuray Yayat Karyana Yhupi Praga Adri Yusuf Fahrizal