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All Journal Kubik KUBIK: Jurnal Publikasi Ilmiah Matematika
Manurung, Chintya Carissa
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Computer Science & IT Economics, Econometrics & Finance Education Mathematics

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ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE KALMAN FILTER DENGAN STATE SPACE MODEL SCALAR PADA PRODUK ASURANSI UMUM Manurung, Chintya Carissa; Yulita, Tiara; Listiani, Amalia
KUBIK Vol 9, No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

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Abstract

Usually, there is a delay in reporting claims from the time of the incident which results in the insurance company having a responsibility or debt. Therefore, insurance companies need to prepare funds to cover these debts, namely with claims reserves. There are two types of claim reserves, namely Incurred But Not Reported (IBNR) and Reported But Not Settled (RBNS). This research focuses on determining the estimation of aggregate claim reserves using the Kalman Filter method with scalar State Space Models (SSMs) which is a model resulting from the development of the Chain Ladder (CL) method. The Kalman Filter method with SSMs is a stochastic method that takes into account the time series model so that it can predict the temporal dynamics of a system more accurately. The results of forecasting claim reserves using the Kalman Filter method with SSMs will be compared with the CL method. Variational Of Coefficient (VOC) is an error predictor to determine the best method. The calculation results using the Kalman Filter method with SSMs produce a smaller VOC value than the CL method, proving that the Kalman Filter method with SSMs is better than CL.
ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE KALMAN FILTER DENGAN STATE SPACE MODEL SCALAR PADA PRODUK ASURANSI UMUM Manurung, Chintya Carissa; Yulita, Tiara; Listiani, Amalia
KUBIK Vol 9, No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Usually, there is a delay in reporting claims from the time of the incident which results in the insurance company having a responsibility or debt. Therefore, insurance companies need to prepare funds to cover these debts, namely with claims reserves. There are two types of claim reserves, namely Incurred But Not Reported (IBNR) and Reported But Not Settled (RBNS). This research focuses on determining the estimation of aggregate claim reserves using the Kalman Filter method with scalar State Space Models (SSMs) which is a model resulting from the development of the Chain Ladder (CL) method. The Kalman Filter method with SSMs is a stochastic method that takes into account the time series model so that it can predict the temporal dynamics of a system more accurately. The results of forecasting claim reserves using the Kalman Filter method with SSMs will be compared with the CL method. Variational Of Coefficient (VOC) is an error predictor to determine the best method. The calculation results using the Kalman Filter method with SSMs produce a smaller VOC value than the CL method, proving that the Kalman Filter method with SSMs is better than CL.
ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE KALMAN FILTER DENGAN STATE SPACE MODEL SCALAR PADA PRODUK ASURANSI UMUM Manurung, Chintya Carissa; Yulita, Tiara; Listiani, Amalia
KUBIK Vol 9 No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Usually, there is a delay in reporting claims from the time of the incident which results in the insurance company having a responsibility or debt. Therefore, insurance companies need to prepare funds to cover these debts, namely with claims reserves. There are two types of claim reserves, namely Incurred But Not Reported (IBNR) and Reported But Not Settled (RBNS). This research focuses on determining the estimation of aggregate claim reserves using the Kalman Filter method with scalar State Space Models (SSMs) which is a model resulting from the development of the Chain Ladder (CL) method. The Kalman Filter method with SSMs is a stochastic method that takes into account the time series model so that it can predict the temporal dynamics of a system more accurately. The results of forecasting claim reserves using the Kalman Filter method with SSMs will be compared with the CL method. Variational Of Coefficient (VOC) is an error predictor to determine the best method. The calculation results using the Kalman Filter method with SSMs produce a smaller VOC value than the CL method, proving that the Kalman Filter method with SSMs is better than CL.
ESTIMASI CADANGAN KLAIM MENGGUNAKAN METODE KALMAN FILTER DENGAN STATE SPACE MODEL SCALAR PADA PRODUK ASURANSI UMUM Manurung, Chintya Carissa; Yulita, Tiara; Listiani, Amalia
KUBIK Vol 9 No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Usually, there is a delay in reporting claims from the time of the incident which results in the insurance company having a responsibility or debt. Therefore, insurance companies need to prepare funds to cover these debts, namely with claims reserves. There are two types of claim reserves, namely Incurred But Not Reported (IBNR) and Reported But Not Settled (RBNS). This research focuses on determining the estimation of aggregate claim reserves using the Kalman Filter method with scalar State Space Models (SSMs) which is a model resulting from the development of the Chain Ladder (CL) method. The Kalman Filter method with SSMs is a stochastic method that takes into account the time series model so that it can predict the temporal dynamics of a system more accurately. The results of forecasting claim reserves using the Kalman Filter method with SSMs will be compared with the CL method. Variational Of Coefficient (VOC) is an error predictor to determine the best method. The calculation results using the Kalman Filter method with SSMs produce a smaller VOC value than the CL method, proving that the Kalman Filter method with SSMs is better than CL.
Co-Authors Listiani, Amalia Yulita, Tiara