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Application of Survival Models in Estimating Policyholder Persistence

Abstract

The study examines the application of survival models in estimating policyholder persistence, focusing on the use of statistical techniques to analyse the length of time policyholders remain active with an insurance company. Policyholder persistence is an important consideration in insurance management because lapses, cancellations, and non-renewals can affect premium income, customer relationships, and the long-term stability of insurance portfolios. Survival models provide a suitable framework for analysing the duration of policyholder relationships and estimating the likelihood of policy continuation or termination over time. The study focuses on the application of survival analysis techniques to identify factors associated with policyholder persistence. Variables such as policy duration, premium payment patterns, claims experience, policy type, customer characteristics, and renewal history can provide useful information for understanding the probability of continued participation. Survival models can account for differences in the length of observation among policyholders and provide estimates of the probability that a policy remains active over specific periods. The study further examines the relevance of survival models to policy lapse and renewal prediction. Insurance companies need to understand when and why policyholders discontinue their policies in order to develop effective retention strategies. By estimating survival probabilities and identifying factors associated with policy termination, insurers can recognise customer groups with higher lapse risks and develop appropriate interventions to encourage continued policy participation. The study also considers the application of survival models to insurance planning and customer relationship management. Accurate estimates of policyholder persistence can assist insurers in forecasting future premium income, planning customer retention activities, and managing portfolio stability. Information generated from survival analysis may also support product development and service improvement by revealing patterns in the duration of policyholder relationships. The study is expected to demonstrate that survival models can provide useful and reliable estimates of policyholder persistence. Their ability to account for time-to-event outcomes and censored observations is anticipated to provide insurers with a more comprehensive understanding of policy continuation and termination patterns. However, the effectiveness of these models is expected to depend on the quality and completeness of policyholder data, the suitability of the selected survival model, the definition of the termination event, and the appropriateness of the variables included in the analysis. The study concludes that survival models provide an important statistical framework for analysing policyholder persistence and supporting insurance management decisions. Their application can strengthen lapse prediction, renewal planning, customer retention, premium forecasting, and portfolio management. The study therefore recommends increased use of appropriate survival analysis techniques, improved collection and management of longitudinal policyholder data, and continuous development of statistical and actuarial competencies to enhance the accuracy of policyholder persistence estimation.

Keywords: Survival Models, Policyholder Persistence, Survival Analysis, Policy Lapse, Policy Renewal, Insurance Risk, Customer Retention, Policy Duration, Insurance Policies, Lapse Prediction, Premium Forecasting, Risk Assessment, Statistical Modelling, Actuarial Science, Insurance Management