Insurance Premium Determination Model and Innovation for Economic Recovery Due to Natural Disasters in Indonesia

Abstract

Climate change that occurs causes the risk of natural disasters to continue to increase throughout the world. Economic losses are unavoidable, leading to the need for continuous innovation in post-disaster economic recovery efforts. Insurance is one of the offers in providing funding for the economic recovery that occurs. This study aimed to develop innovations and models for determining natural disaster insurance premiums with a subsidy and tax system. In addition, the developed model considers the disaster risk index in the form of the level of risk distribution, the frequency of events, and economic losses. In this study, the data used were the frequency of events and economic losses obtained from the Indonesian National Disaster Management Agency. The data used were 20 database periods from 2000 to 2019. This study used the collective risk method from the index of natural disaster risk parameters. From the results of the analysis, it was found that the level of distribution of disaster risk affected the determination of insurance premiums. The amount of insurance premiums is increasing along with the increase in the magnitude of the spread of disaster risk. In addition, if taxes and subsidies are reduced, then for high-risk areas, there will be a decrease in the burden of insurance premiums, and for low-risk areas, there will be an increase in the premium burden that must be paid. On the basis of the results of the analysis on the insurance model, it was found that the insurance premiums in each province varied. The results of this study are expected to be a reference for the government and private companies in implementing disaster insurance in Indonesia. In addition, the results of this study can be a means of developing innovations for disaster risk management that occurs.

Determination of Mount Eruption Insurance Premiums in Indonesia Based on Collective Risk and Level of Risk Spread

“…An example of a counting process is the number of volcanic eruptions in Indonesia in the time span . In determining the mean and variance of the Poisson process on the frequency of volcanic eruptions, it can be formulated as follows (Kalfin et al, 2021b):…”

“…Collective risk models are determined based on large data of economic losses and the number of events, which can be used to measure the risk of an insurance company as a whole. Determination of collective risk values is based on expectations and variance from volcanic eruption data which are formulated in equations ( 5) and ( 6) (Kalfin et al, 2020;Sukono et al, 2022;Kalfin et al, 2022b).…”

“…From disaster risk analysis, the expected loss value can be obtained, namely the average loss that is expected to occur in a certain time period. This expected loss value can be used as a basis for calculating pure disaster insurance premiums (Kalfin et al, 2022a;Kalfin et al, 2022b;Kalfin et al, 2023).…”

Earthquake Bond Pricing Model Involving the Inconstant Event Intensity and Maximum Strength

“…An analysis of data from the International Disaster Database (accessed on 28 October 2022 from https://www.emdat.be) shows that, on average, a single extreme earthquake results in economic damage of USD 1,266,348,909, with the highest magnitude compared to other extreme disasters, as illustrated in Figure 1. This high loss burdens the budgets of earthquakeprone countries (countries whose territories lie atop tectonic plate lines) [4][5][6][7], so several of them used traditional insurance mechanisms to obtain contingency funds for mitigation from 1992 to 2000. However, this mechanism generally has shortcomings, whereby the provided contingency costs pale in comparison to the actual losses incurred [8][9][10].…”

How to Price Catastrophe Bonds for Sustainable Earthquake Funding? A Systematic Review of the Pricing Framework

“…Significant earthquake losses place a burden on a country's budget [4][5][6][7]. As a result, between 1992 and 2000, many countries used traditional insurance mechanisms to obtain contingency costs for their earthquake responses.…”