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Ahmad Fuad Zainuddin
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ahmadfuadzain@gmail.com
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+6285256677506
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editorial.iaj@aktuaris.or.id
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Setiabudi Atrium 7th Floor, Room 703, Jalan H.R. Rasuna Said Kav. 62, Kuningan, South Jakarta 12920
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INDONESIA
Indonesian Actuarial Journal
Published by Persatuan Aktuaris Indonesia
ISSN : -     EISSN : 31106463     DOI : -
Core Subject : Economy, Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Physics
The Indonesian Actuarial Journal (IAJ) is an international peer-reviewed electronic journal published by the Society of Actuaries of Indonesia (Persatuan Aktuaris Indonesia). The journal is published twice a year and may also feature special issues addressing specific themes of interest in actuarial science and related fields. IAJ focuses on advancing theoretical and applied research in actuarial science and its interdisciplinary domains. The journal welcomes high-quality manuscripts that contribute to the development of actuarial theory, methodology, and practice, as well as studies with implications for policy, industry, and education. The scope of IAJ encompasses a wide range of topics, including but not limited to: life and non-life insurance mathematics, pension and social security systems, risk theory, health insurance, financial and investment modeling, applied probability and statistics, stochastic processes, and emerging areas in data analytics and actuarial applications. IAJ serves as a platform for researchers, practitioners, academics, policymakers, and students to exchange knowledge and insights that advance the actuarial profession both in Indonesia and globally.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 24 Documents
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Forecasting and Financial Risk Analysis Using ARIMA Intervention Model: A Case Study on JMAS Tbk Rina Yuliantika; Indah Gumala Andirasdini
Indonesian Actuarial Journal Vol. 2 No. 1 (2026): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol2no1pp013-023

Abstract

The capital market is characterized by high volatility, making accurate forecasting and risk measurement essential for investment decision-making. This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) intervention model and the Value at Risk (VaR) approach for stock prices forecasting and finansial risk analysis.The data consist of monthly stock prices of PT Asuransi Jiwa Syariah Jasa Mitra Abadi Tbk (JMAS) from January 2018 to December 2025. The ARIMA intervention model is applied to identify and quantify structural changes caused by external shocks, particularly the COVID-19 pandemic. The results indicate that the ARIMA (1,2,0) intervention model is the most appropriate model, with a step intervention function reflecting a sudden and permanent impact on stock price movements. The model satisfies diagnostic assumptions and demonstrates good forecasting accuracy, with a Mean Absolute Percentage Error (MAPE) of 13.01%. Forecasting results for January to March 2026 show that stock prices are expected to remain relatively low, indicating a slow post-pandemic recovery. Financial risk is measured using the Cornish-Fisher Value at Risk (VaR) approach at a 95% confidence level. The estimated VaR is -0.4245 indicating a maximum potential loss of 42.45% over a one-month period. This high level of risk reflects extreme market conditions during the COVID-19 period, which significantly increased volatility. This study contributes to financial analysis by integrating intervention-based time series modeling with risk measurement in a unified framework.
Robust E-Bayesian Estimation and Prediction of Esscher Premium for Car Insurance Claim Frequencies Under a Negative Binomial Model Eli Zulkatri; Muhammad Azka; Ahmad Fuad Zainuddin
Indonesian Actuarial Journal Vol. 2 No. 1 (2026): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol2no1pp069-080

Abstract

Car insurance claim frequencies often exhibit overdispersion, making the Poisson model too restrictive for premium estimation. This study develops a Negative Binomial-based Bayesian and E-Bayesian framework for estimating and predicting Esscher premiums using aggregated car insurance claim-frequency data. The novelty of this study lies in combining a closed-form Esscher premium under the Negative Binomial model with Bayesian conjugate updating and E-Bayesian hyperparameter averaging to reduce sensitivity to prior specification. A simulated aggregated claim-frequency table with 1,200 exposure units was used to illustrate the proposed framework. The data contained 642 total claims, with an empirical mean of 0.5350 and a sample variance of 0.8378. The variance-to-mean ratio of 1.5660 confirmed overdispersion and supported the use of the Negative Binomial model over the Poisson model. The method-of-moments estimates were  for the dispersion parameter and  for the success probability. At the representative tilt parameter , the Esscher premiums obtained from the method-of-moments, Bayesian, and E-Bayesian approaches ranged from 0.9004869 to 0.9059240, indicating stable premium estimates across alternative estimation methods. The sensitivity analysis showed that the Esscher premium increases nonlinearly with the tilt parameter, confirming its role as a risk-loading control. The prequential illustration further showed that the framework can support one-step-ahead prediction for aggregated claim totals. Overall, the proposed approach provides a robust and analytically tractable premium estimation framework for overdispersed car insurance claim-frequency data when prior information is uncertain and only aggregated data are available.
Government Employees with Employment Agreements Pension Fund Valuation Across 17 Salary Grades Using Entry Age Normal and Projected Unit Credit Khalil Rafif; Illuminata Wynnie
Indonesian Actuarial Journal Vol. 2 No. 1 (2026): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol2no1pp052-068

Abstract

Accurate calculation of pension funds for Government Employees with Employment Agreements (PPPK) is crucial for the sustainability of the program. Variations in the age and age of entry used can result in significantly different pension cost projections, so a comprehensive analysis is needed to determine the right methods and assumptions. This study aims to analyze the differences in the calculation of present value future benefits, normal contributions, actuarial obligations, and lumpsum benefits by taking into account based on class and age of entry. This quantitative research uses actuarial simulations using the Entry Age Normal (EAN) and Projected Unit Credit (PUC) methods. The simulation was carried out at the age of 20 to 48 years and groups I to XVII. This calculation follows the 100% one-time pension benefit for a service period of 10 to 15 years and 20% at once for a service period of more than 15 years, with a normal retirement age of 58 years. The results of the study show that the EAN method produces constant normal contributions, while the PUC method produces contributions that increase every year, the normal monthly contributions increase monotonically based on the group and age of entry into work until retirement. In addition, the EAN method produces greater lumpsum benefits than the PUC method. The 17 PPPK groups take into account the projected pension fund present value future benefits, normal contributions, actuarial liabilities, and lumpsum benefits that are more realistic and sustainable.
Multiple Decrement Modeling of BPJS Health Transitions Under Air Pollution Exposure in DKI Jakarta Bernadeth Saskia Laudya Cintya; Leyton Raynaldo Ozzora; Helena Margaretha; Ferry Vincenttius Ferdinand
Indonesian Actuarial Journal Vol. 2 No. 1 (2026): Indonesian Actuarial Journal
Publisher : Persatuan Aktuaris Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65689/iajvol2no1pp024-039

Abstract

Air pollution represents a significant environmental health burden in Indonesia. Despite this, the quantitative relationship between ambient pollutant concentrations and population-level health state transitions within Indonesia’s national health insurance system remains uncharacterized. This study estimates cause-specific forces of decrement for health state transitions within the BPJS Kesehatan administrative claims data from DKI Jakarta spanning 2022 to 2024, and examines the association between ambient air pollutant concentrations and estimated transition intensities through exploratory LASSO regression. The analytical dataset comprises 454,001 FKTP and 147,379 FKRTL episodes, stratified by five-year age bands and calendar quarter. Multiple decrements across both care settings are modeled, with the FKTP-to-FKRTL referral (rujuk lanjut) decrement serving as the primary focus. NO2 emerged as a candidate predictor in exploratory screening, appearing with a positive coefficient in 6 of 17 age bands, with model-implied elasticities ranging from 0.063 to 0.544. Scenario analysis showed that a hypothetical 20% increase in ambient NO2 corresponded to fitted referral intensity increases of between 1.3 and 10.9 percent across retained age bands. 

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