CALCULATION OF TERM LIFE INSURANCE PREMIUM FROM COX-INGERSOLL-ROSS (CIR) INTEREST RATE MODELS BASED ON 2011 AND 2019 INDONESIAN LIFE TABLES

• Main Author: Kevin Chandra, Marcellius
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/70891
• Institution: Institut Teknologi Bandung

In the life insurance industry, the calculation of life insurance premiums is one of the most important things to do in any insurance companies to sell insurance products that can provide benefits to the company. Generally, the calculation of life insurance premiums intended for a policyholder is determined based on a predetermined interest rate and the risks held by a policyholder based on the probability of death of the individual at that age. Currently, there are many types of life insurance products offered by various companies, including term life insurance which provides protection to policyholders for a specified period of time. In general, the calculation of premiums on life insurance is done using a fixed interest rate. However, under certain economic conditions, the value of interest rates in the market may change. Therefore, this study aims to estimate the effect of changes in interest rates on the calculation of premiums from a term life insurance and to develop a method of calculating premiums that considers the non-constant interest rates. This study uses the Cox-Ingersoll-Ross (CIR) interest rate model. The CIR model is used to predict future interest rate movements as long as the term life insurance is valid. In addition, this study also uses the Weibull survival model based on the 2011 and 2019 Indonesian Life Tables. The results of this study indicate that the interest rate is not constant and changes in survival probabilities affect the calculation of premiums. The results of this study also produce a premium calculation model that can follow a movement in interest rates from an interest rate simulation result with a combination of various CIR interest rate models with different parameters and uses a survival model based on data from the mortality table. From these results, the authors suggest that the model can be developed to produce a better premium calculation model.