Modeling Term Life Insurance Premiums Using Monte Carlo Simulations with Stochastic Interest Rates Based on The Indonesia Mortality Table IV - Lubis, Mery Christyn; Tampubolon, Bungaria; Tarigan, Febry Vista Kristen (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Pamulang, Indonesia, 2026)
This study addresses the limitation of deterministic interest rate assumptions in actuarial premium calculations, which may lead to inaccurate pricing, particularly for long-term life insurance products. In practice, interest rates are influenced by economic conditions and exhibit stochastic behavior, making fixed-rate assumptions less realistic. Therefore, incorporating interest rate uncertainty is essential to obtain more accurate and reliable premium estimates. The objective of this study is to determine the net premium of term life insurance by integrating stochastic interest rates using the Vasicek model and Monte Carlo simulation. A quantitative approach is employed using actuarial modeling based on the Indonesian Mortality Table IV (TMI IV) and the equivalence principle. Interest rates are modeled as a mean-reverting stochastic process, and Monte Carlo simulation with 10,000 iterations is applied to estimate the distribution of premiums under uncertainty. The results show that premiums increase significantly with higher entry age and longer coverage periods, reflecting increased mortality risk and longer exposure to uncertainty. Male premiums are consistently higher than female premiums due to higher mortality probabilities. The premium distribution is approximately normal, with increasing variability observed for longer-term policies. Sensitivity analysis indicates that the long-term mean interest rate parameter has the strongest influence on premium values, while volatility mainly affects the dispersion of premiums rather than their expected value. Overall, the stochastic approach provides a more realistic and comprehensive framework compared to deterministic methods, as it captures both expected values and uncertainty. The findings of this study can support more accurate pricing, improved risk management, and better decision-making in the life insurance industry, particularly in the Indonesian context.