Supply Chain Strategy for Managing Risk for Health Insurance: An Application of Bayesian Model

Abstract

Abstract- One important matter that should be investigated is the estimation of the risk distribution model of claims for each age group of the insured in health insurance because it is beneficial to prevent the occurrence of losses for insurance companies in the future. Supply chain strategy can be used in health insurance industry to manage the risks. In this paper, the research done is about the risk distribution model estimation on health insurance claims using Bayesian. The objective is to derive a health insurance risk model and determine the amount of net premium for each insured age group in health insurance. The sample of this study is the participant of health insurance in the Bandung area, Indonesia, especially for the insured who live in flood-prone areas. The estimation of the Poisson and Gamma distribution parameter is performed using the Bayesian method, which OpenBUGS involves Markov Chain Monte Carlo (MCMC) the simulation technique. The estimation results show that the frequency of claims significantly follows the Poisson distribution, whereas the amount of claims substantially follows the Gamma distribution. With the result of the analysis, the estimated frequency distribution of claims and the amount of claims, a health insurance risk model may be established. Thus the net premium of health insurance for every age group, for the insured who live in the area prone to floods can be determined.