Multi-State Markov Net Premium Valuation for Chronic Illness Riders with Care Benefits in Indonesia - Irma Fauziah; Hamida Hamida; Suma Inna (UIN Sumatera Utara, 2026)
This study develops a net premium valuation model for chronic illness riders with care benefits in Indonesia using a discrete-time multi-state Markov framework. Indonesian 2023 prevalence, mortality, and population data are used as inputs. Age-group prevalence of stroke, chronic kidney disease, and hypertension is interpolated to single ages and used to derive model-implied transition probabilities under Markov assumptions. These probabilities are combined with a population-weighted unisex mortality basis to calculate net premiums under the actuarial equivalence principle. Results show that the probability of remaining healthy decreases with age, while illness and death probabilities increase. Hypertension gives the largest contribution to illness transitions. For entry ages 35-65, the annual net premium rises from IDR 1.62 million to a peak of IDR 3.25 million at age 61, then declines slightly. The model provides a population-level basis for diagnosis-based chronic illness rider pricing in settings without longitudinal incidence data or claims histories.