A semi-analytic prediction of reserve under pandemic risk: covid-19 in Indonesia as a case study

This study develops an integrated actuarial–epidemiological framework to improve premium and reserve estimates under pandemic conditions. The model combines forecasted epidemiological indicators complete with their upper and lower confidence bounds with varying proportions of severity levels to generate financial projections for two insurance benefit schemes. By directly linking disease dynamics to liability valuation, the framework seeks to enhance the reliability of actuarial assessments when health risks are rapidly evolving. A comparison of predicted and observed outcomes shows that, while the model produces credible estimates, it consistently understates the premium and reserve levels required in several periods. To correct for this systematic shortfall, an additional loading factor is introduced. Specifically, to accurately correct the premium values, a loading mechanism is applied wherein the loading factor is calculated utilizing the median model predictions and the upper bound of the confidence interval. Sensitivity results further reveal that shifts in the distribution of infection severity have a substantial impact on projected premiums and reserves. This finding underscores the importance of incorporating dynamic severity parameters into pandemic-oriented insurance modeling, as doing so enables the framework to capture a broader range of plausible epidemiological scenarios and thereby improve the robustness of its projections.