Abstract

Policy data granularity refers to the level of detail at which information relating to life insurance policies is recorded, organised, and analysed for actuarial purposes. Detailed policy data may contain information on individual policy characteristics, policyholder attributes, premium payments, benefit amounts, policy duration, and other relevant factors. The level of detail available in policy data may therefore influence the accuracy and consistency of life insurance premium calculations. This study will examine the effect of policy data granularity on life insurance premium calculations. It will assess how different levels of detail in policy data influence the calculation of life insurance premiums and the resulting estimates of expected policy liabilities. The study will also compare premium calculations based on detailed and aggregated policy data to determine how data granularity affects actuarial pricing outcomes. The study will focus on policy data granularity, life insurance premium calculations, policyholder characteristics, policy duration, premium payments, benefit amounts, mortality experience, actuarial assumptions, policy data aggregation, and actuarial pricing. Different levels of policy data detail will be examined to determine their influence on premium estimates. Actuarial and statistical techniques will be used to evaluate variations in calculated premiums under alternative data structures. A quantitative research approach will be adopted for the study. Historical life insurance policy, premium, mortality, benefit, and exposure data will be organised according to different levels of granularity and analysed using descriptive statistics, mortality analysis, actuarial present value calculations, premium valuation techniques, comparative analysis, and sensitivity analysis. Premium estimates generated from detailed and aggregated policy data will be compared to determine the effect of data granularity on life insurance premium calculations. The study is expected to reveal that policy data granularity may have a significant effect on life insurance premium calculations. More detailed policy data may allow actuarial models to capture differences in policyholder characteristics and contract features more effectively, while aggregated data may simplify calculations but conceal important variations in risk. The magnitude of the effect may depend on data quality, portfolio composition, mortality differences, policy characteristics, aggregation levels, and the actuarial valuation method applied. The study will be useful to actuaries, life insurance companies, pricing analysts, underwriters, valuation specialists, risk managers, regulators, and insurance researchers. It may provide useful information for improving premium calculations, strengthening actuarial data management, enhancing policy risk assessment, and supporting more reliable life insurance pricing. The findings may also assist insurers in determining appropriate levels of policy data detail for actuarial applications. The study concludes that policy data granularity is an important consideration in life insurance premium calculations because the level of detail contained in policy information can influence the measurement of policyholder risk and the resulting premium estimates. It is therefore recommended that insurers maintain sufficiently detailed policy data, evaluate appropriate aggregation levels, and apply comparative and sensitivity analyses to assess the effect of data granularity on life insurance premium calculations.

Keywords: Policy data granularity, life insurance premium calculations, actuarial pricing, policyholder data, life insurance policies, premium estimation, mortality experience, policy duration, benefit amounts, premium payments, actuarial assumptions, data aggregation, actuarial valuation, insurance risk, sensitivity analysis.