Predicting BPJS health insurance premiums using SIR-like participant models and frequency–severity model.

This work will give a novel approach in predicting premium of Indonesian's health insurance for the next 30 years using application of dynamical systems. First, we fit Indonesian population dataset from 1960–2018 and the number of policy holders to estimate the coefficients of our dynamical systems using Markov Chain Monte Carlo Process. We used models fit to predict the number of participant for the next 30 years. We then implemented the frequency-severity model to evaluate the premium of each segment in the insurance policy. The projected premium for the next 30 years will be calculated using the same frequency-severity model but with new parameters obtained from the numerical solution of the dynamical system. We assumed a constant proportion between population data to the number of participant in the sample data and a gradually decreasing proportion between the number of zero claim and the number of sample data. Premium in all segments overestimated costs regulated by the government with one exception in one segment. [ABSTRACT FROM AUTHOR]