THE ROLE OF GENETIC ALGORITHM IN THE DETERMINATION OF TERM LIFE INSURANCE PREMIUMS THROUGH STACKELBERG'S GAME THEORY

• Main Author: Angelina, Anastasia
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/65398
• Institution: Institut Teknologi Bandung

In the insurance industry, especially life insurance, determining the amount of premium paid by the insured to the insurer is very crucial. If the calculation is wrong, the insurance company will suffer a loss. Stackelberg's game theory is used to describe the reality that occurs in the insurance industry. The amount of premium is obtained by optimizing the insurer's profit for each insurance product and each insured's choice of strategy with a certain premium value limit. The optimization problem will be solved by using a genetic algorithm. The insurer offers 4 different insurance products. Meanwhile, the insured has 2 strategies, namely accepting one of the offered insurance products or rejecting all offered insurance products. In the analysis, the Indonesia Mortality Table IV data published in 2019 by the Indonesian Life Insurance Association as a guideline is used to obtain the parameters of the Makeham and Gompertz distribution functions, as well as some required constant. Generated premiums for male and female insureds from the age of 0 - 65 years when buying every insurance product offered, benefits for the insurer and the insured, as well as the expectation of the number of insureds who will buy each insurance product according to the modeled function. The result is that the older the insured, the greater the premium that needs to be paid and the fewer people who buy one product, so the lower the profit for the insurer and the insured. In addition, the third insurance product which using Makeham distribution and offering 2 types of benefits is preferred by the insured and insurer because it provides the largest profit.