LIFE INSURANCE PREMIUM AND BENEFIT CALCULATION USING THE MARKOV CHAIN MODEL (CASE STUDY: COVID-19 CONFIRMED DATA IN BANDUNG CITY) - Qurrotun Nadwah, Hamidah
Coronavirus Disease (COVID-19) is an infectious disease that was discovered in China. Thus far, the disease has spread throughout the world including Indonesia. This implies the emergence of diverse risks that impact various sectors, especially in economic sector. This risk can be mitigated by transferring the risk to insurer, in this context, life insurance. This research focuses on calculating life insurance premiums and benefit using Markov chain model. Markov chain is one of methods used to see the dependency of foregoing data. In this discussion, stationary distribution Markov chain model is used to calculate long-term predictions of the spread of COVID-19 by analyzing daily cases. The probability of increasing and decreasing COVID-19 cases over a long period of time are calculated in 30 sub-districts in Bandung city. Furthermore, results of the long-term predictive value are used to estimate the premiums as well as life insurance benefits. The research methodology used begins with determining the state space of data. State space is divided into three; when COVID-19 cases fell from previous day (-1), remained unchanged from previous day (0), and increased from previous day (1). The second step is to count the frequency of three state spaces along with the calculation of the transition frequency of each state space to another state space. The third stage, stationarity of the data will be tested with a discrete-time Markov chain. Stationarity of the distribution means Markov chains which are aperiodic, have positive recurrent, together with irreducible. Data is stationary if it fulfills these three properties. The fourth stage is to calculate long-term probability of COVID-19 cases. Calculation results are used to estimate premiums and life insurance benefits. According to the results, three districts with highest probability of increasing COVID-19 cases were obtained: Coblong (40%), Arcamanik (39%), Antapani (38%). Meanwhile, three districts with the lowest probability of increasing COVID-19 cases: Bandung Kulon (15%), Cibiru (15%), Bandung Wetan (22%). Based on the probability, the result is the higher the probability of an increase in COVID-19 cases, the greater the premium paid. Additionally, the higher the probability of an increase in COVID-19 cases, the greater benefits needed.