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All Journal International Journal of Global Operations Research International Journal of Quantitative Research and Modeling
Mayaningtyas, Chibi Adinda
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Mathematics Physics

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Accelerated Pension Fund Calculations Using the Individual Level Premium Method and the Projected Unit Credit Method Case Study: PT. Dirgantara Indonesia Rohman, Aletta Divna Valensia; Mayaningtyas, Chibi Adinda
International Journal of Global Operations Research Vol. 5 No. 3 (2024): International Journal of Global Operations Research (IJGOR), August 2024
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v5i3.325

Abstract

This paper examines the calculation of accelerated pension funds using two actuarial methods: the Individual Level Premium (ILP) method and the Projected Unit Credit (PUC) method. The case study focuses on PT. Dirgantara Indonesia. We compare the methods' impact on normal contribution amounts, actuarial liabilities, and retirement benefits. The research highlights the advantages and disadvantages of each approach, considering factors like participant age and contribution period. The findings demonstrate that the PUC method generally leads to lower normal contributions but may result in lower final retirement benefits compared to the ILP method. This study provides valuable insights for companies and employees in PT. Dirgantara Indonesia to choose the most suitable method for their accelerated pension plan, considering their financial goals and risk tolerance.
Risk Prediction and Estimation of Corporate Product Claim Reserve Funds in Insurance Companies Using the Extreme Value Theory Maelowati, Indah Dewi; Mayaningtyas, Chibi Adinda
International Journal of Quantitative Research and Modeling Vol 5, No 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.820

Abstract

Every human action involves risk, and in the insurance industry, customer claims are the biggest risk that companies face. This risk must be managed effectively through claim prediction, especially for corporate products. This research analyzes the risk of claims at insurance companies using the Extreme Value Theory (EVT) method, which can estimate extreme risks. Identification of extreme values in claims data is done through the EVT approach, namely Block-Maxima (BM). Generalized Extreme Value (GEV) distribution parameter estimation is performed, followed by prediction of claim risk using Value at Risk (VaR) and estimation of claim reserve funds. The results show that the GEV approach with a 95% confidence level is most suitable for predicting claim risk. Based on these results, the company requires a claim reserve fund of IDR 100,798,248,000 to deal with potential losses due to extreme claims.
Risk Prediction and Estimation of Corporate Product Claim Reserve Funds in Insurance Companies Using the Extreme Value Theory Maelowati, Indah Dewi; Mayaningtyas, Chibi Adinda
International Journal of Quantitative Research and Modeling Vol. 5 No. 4 (2024)
Publisher : Research Collaboration Community (RCC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46336/ijqrm.v5i4.820

Abstract

Every human action involves risk, and in the insurance industry, customer claims are the biggest risk that companies face. This risk must be managed effectively through claim prediction, especially for corporate products. This research analyzes the risk of claims at insurance companies using the Extreme Value Theory (EVT) method, which can estimate extreme risks. Identification of extreme values in claims data is done through the EVT approach, namely Block-Maxima (BM). Generalized Extreme Value (GEV) distribution parameter estimation is performed, followed by prediction of claim risk using Value at Risk (VaR) and estimation of claim reserve funds. The results show that the GEV approach with a 95% confidence level is most suitable for predicting claim risk. Based on these results, the company requires a claim reserve fund of IDR 100,798,248,000 to deal with potential losses due to extreme claims.
Co-Authors Maelowati, Indah Dewi Rohman, Aletta Divna Valensia