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Modelling Policyholder Duration Using Survival Analysis

Abstract

Policyholder duration is an important consideration in life insurance because the length of time that policyholders maintain their policies can influence premium income, policy persistency, profitability, and the valuation of future insurance obligations. Understanding how long policyholders are likely to remain insured provides useful information for actuarial modelling and insurance portfolio management. Survival analysis offers statistical techniques for examining the time until an event occurs, making it suitable for analysing policyholder duration and the timing of policy termination. The study will model policyholder duration using survival analysis techniques to examine the length of time policyholders remain active before experiencing policy termination or lapse. It will analyse the distribution of policyholder survival times and estimate the probability that a policy remains active over different periods. The study will also examine differences in policy duration patterns across relevant categories of life insurance policies. The analysis will consider survival functions, hazard functions, Kaplan–Meier estimation, and other appropriate survival modelling techniques. These methods will be used to estimate policyholder survival probabilities and identify periods during which the likelihood of policy termination may increase or decrease. Where appropriate, regression-based survival models may also be applied to assess the contribution of selected policy characteristics to variations in policyholder duration. A quantitative research approach will be adopted for the study. Historical policyholder data containing information on policy commencement dates, termination or lapse dates, policy status, and relevant policy characteristics will be collected and analysed. Survival analysis techniques will be used to estimate survival probabilities and hazard rates, while appropriate statistical procedures will be applied to evaluate the performance and suitability of the selected models. Censored observations will also be considered where policies remain active at the end of the observation period. The study is expected to demonstrate that survival analysis can effectively model the duration of life insurance policies and provide estimates of policyholder retention over time. The findings may reveal variations in survival probabilities and hazard rates across different stages of policy duration. Certain periods may exhibit higher risks of policy termination, while other periods may demonstrate stronger policy persistency. The findings will be useful to actuaries, life insurance companies, underwriters, and insurance managers in improving policy persistency analysis and actuarial forecasting. Accurate estimates of policyholder duration may support premium and reserve calculations, cash-flow projections, product evaluation, and portfolio management. The study may also provide insurers with a stronger statistical basis for understanding policy termination patterns and improving long-term financial planning. The study concludes that survival analysis provides an appropriate framework for modelling policyholder duration and evaluating the timing of policy termination in life insurance portfolios. It is therefore recommended that life insurance companies incorporate survival analysis into policy persistency and actuarial modelling practices where suitable data are available. Regular updating of survival models with new policyholder experience will help insurers improve the accuracy of duration estimates and strengthen insurance portfolio management.

Keywords: Policyholder Duration, Survival Analysis, Life Insurance, Policyholder Persistency, Survival Function, Hazard Function, Kaplan–Meier Estimation, Policy Lapse, Policy Termination, Actuarial Modelling, Insurance Portfolio, Policyholder Retention, Survival Probability, Hazard Rate, Life Insurance Valuation.