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All Journal Eksakta : Berkala Ilmiah Bidang MIPA International Journal of Business, Economics, and Social Development International Journal of Global Operations Research
Riaman
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Analysis of Health Insurance Claims Factors using The Stochastic Restricted Maximum Likelihood Estimation (SRMLE) Binary Logistic Regression Model: (Case Study: Health Insurance Claims at XYZ Company in 2023) Bagariang, Elizabeth Irene; Riaman; Gusriani, Nurul
International Journal of Global Operations Research Vol. 6 No. 3 (2025): International Journal of Global Operations Research (IJGOR), August 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i3.389

Abstract

The health insurance claim approval process is a crucial aspect for insurance companies. Inaccuracy in predicting claim status can pose financial risks to the company and reduce policyholder trust. This study aims to identify the factors that influence the approval or rejection of health insurance claims. In this type of data analysis, the problem of multicollinearity among predictor variables is often encountered, which can lead to unstable parameter estimates. To address this issue, this study utilizes a binary logistic regression model with the Stochastic Restricted Maximum Likelihood Estimation (SRMLE) method, which is better suited to handle such conditions. The data used in this research includes the variables of total claim amount, premium price, number of insured individuals, employee age, and the number of previous claims recorded at XYZ Company. The results of the factor analysis, through the developed logistic regression model, show that the variables of total claim amount, premium price, and the number of insured individuals are significant factors influencing the probability of claim approval.
Quota Share Reinsurance and Excess of Loss Reinsurance Calculations Using Ruin's Theory Dwi Susanti; Riaman; Badrulfallah; Dimas Apriliyanto
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 03 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss03/468

Abstract

Ruin theory is commonly used to predict the likelihood of bankruptcy for an insurance company and relates to the rate of surplus of the insurance company for the insurance policy portfolio. Considering the change in the insurance fund from time to time, the timing of the occurrence of a number of claims is highly taken into account. Ruin theory is necessary so that companies can anticipate and detect bankruptcy early. One way to help insurance companies minimize their bankruptcy chances is through reinsurance. In this paper, will discuss about application of ruin theory in computing two methods of reinsurance treaty, that is Quota Share Reinsurance and Excess of Loss Reinsurance to decide more effective method to minimize probability of ruin. Results show that Excess of Loss Reinsurance method more effective than Quota Share Reinsurance method to minimize ruin probability of insurance company.
Comparative Analysis of Endowment Life Insurance Premium Reserves Using the Canadian Method Based on the 2019 TMI and Gompertz’s Law Irene Araminta Febry; Riaman; Sukono
International Journal of Business, Economics, and Social Development Vol. 7 No. 2 (2026): International Journal of Business, Economics, and Social Development (IJBESD)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v7i2.1177

Abstract

Life uncertainty, particularly mortality risk, creates the need for financial protection through life insurance. Endowment life insurance is a product that provides both death benefits and survival benefits at the end of the coverage period; therefore, insurance companies must establish adequate premium reserves. This study aims to calculate premium reserves for endowment life insurance using the Canadian method and to compare the effects of different mortality models based on the 2019 Indonesian Mortality Table (TMI) and Gompertz’s law. The results show that premium reserves calculated using Gompertz’s law are higher than those calculated using the 2019 Indonesian Mortality Table (TMI). This difference indicates that the choice of mortality model affects the magnitude of the resulting premium reserves.
Analysis of Term Life Insurance Premium Reserves Using the Zillmer Method with Hull–White Interest Rates Fatahillah Akmal; Riaman; Gusriani, Nurul
International Journal of Business, Economics, and Social Development Vol. 7 No. 2 (2026): International Journal of Business, Economics, and Social Development (IJBESD)
Publisher : Rescollacom (Research Collaborations Community)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijbesd.v7i2.1172

Abstract

This study analyzes the valuation of premium reserves for term life insurance using the Zillmer method under a stochastic interest rate framework. In conventional actuarial practice, premium reserves are commonly calculated using deterministic interest rate assumptions, which may inadequately reflect the dynamic nature of interest rate movements over long-term policy horizons. To address this limitation, this study applies the one-factor Hull–White interest rate model to incorporate stochastic interest rate dynamics into the reserve calculation process. The Hull–White model parameters are estimated using historical interest rate data, and interest rate paths are generated through numerical simulation. These simulated interest rates are then employed to compute discount factors in the calculation of Zillmer premium reserves. The analysis focuses on illustrating how stochastic interest rate movements influence the development of premium reserves over the policy duration, rather than on probabilistic risk measurement or capital adequacy assessment. The results show that premium reserves calculated under the stochastic interest rate framework exhibit dynamic patterns over time, particularly during the early and middle policy periods. Compared to deterministic interest rate assumptions, the stochastic approach captures variations in reserve values arising from interest rate fluctuations. This finding highlights the sensitivity of Zillmer premium reserves to interest rate dynamics. Overall, this study demonstrates that integrating the Hull–White stochastic interest rate model with the Zillmer method provides a descriptive and flexible framework for analyzing premium reserves in term life insurance. The proposed approach may serve as a basis for further research on stochastic valuation methods in actuarial applications.
Co-Authors Badrulfallah Bagariang, Elizabeth Irene Dimas Apriliyanto Dwi Susanti Fatahillah Akmal Irene Araminta Febry Nurul Gusriani Sukono