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Modelling Survival Probabilities for Life Insurance Valuation

Abstract

The study examines the modelling of survival probabilities for life insurance valuation, focusing on the application of actuarial and statistical techniques to estimate the likelihood that policyholders will survive to specified future ages or periods. Survival probabilities are fundamental to life insurance valuation because they influence the timing and probability of future benefit payments and consequently affect the value of insurance liabilities. Accurate survival modelling is therefore important for appropriate valuation, premium determination, and financial planning. The study will investigate the application of survival probability models in the valuation of life insurance contracts. It will examine how estimated survival probabilities can be incorporated into actuarial calculations to determine the expected present value of future insurance benefits. The study will also assess variations in survival estimates under different mortality assumptions and their implications for life insurance valuation. The analysis will focus on survival probabilities, mortality rates, age at entry, policy duration, life expectancy, benefit amounts, discount rates, and present values. Actuarial life tables and appropriate statistical survival models will be applied to estimate survival probabilities over different periods. The resulting estimates will be incorporated into actuarial valuation techniques to determine the expected value of future life insurance benefits and liabilities. A quantitative research approach will be adopted for the study. Relevant mortality, survival, and life insurance data will be obtained from appropriate secondary sources. Life-table methods, survival probability estimation, mortality modelling, present value calculations, and comparative analysis will be employed to develop and evaluate survival probability models. Sensitivity analysis will also be used to examine how changes in mortality and survival assumptions affect estimated insurance values. The study is expected to reveal that survival probability estimates have a measurable influence on life insurance valuation. Higher estimated survival probabilities may affect the timing and likelihood of future benefit payments, while differences in mortality assumptions may produce variations in the present value of insurance obligations. The findings may also demonstrate that the accuracy of survival probability models depends on the quality of mortality data and the assumptions used in the modelling process. The findings are expected to provide useful information for life insurers, actuaries, underwriters, pension professionals, and insurance analysts. Effective survival probability modelling may support more accurate estimation of future benefits, insurance liabilities, premium requirements, and long-term financial obligations. The study may also assist insurance companies in evaluating the suitability of different survival assumptions and modelling techniques for life insurance valuation. The study concludes that survival probability modelling provides an important actuarial basis for valuing life insurance benefits and estimating future insurance liabilities. It is therefore recommended that life insurance companies regularly update survival probability models using reliable mortality data and apply appropriate actuarial techniques to improve valuation accuracy, premium assessment, and long-term liability management.

Keywords: Survival probabilities, life insurance valuation, survival modelling, mortality rates, actuarial models, life tables, insurance benefits, insurance liabilities, life expectancy, mortality assumptions, policy duration, age at entry, present value, actuarial valuation, insurance pricing.