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Application of Logistic Regression to Insurance Policy Renewal Prediction

Abstract

The study examines the application of logistic regression to insurance policy renewal prediction, with emphasis on the use of statistical modelling to estimate the likelihood that policyholders will renew their insurance policies. Policy renewal is important to insurance companies because consistent renewals contribute to premium income, customer retention, portfolio stability, and long-term profitability. Accurate prediction of renewal behaviour can therefore assist insurers in understanding policyholder patterns and making informed decisions about portfolio management. The study will investigate the application of logistic regression in predicting whether an insurance policyholder will renew or discontinue a policy at the end of its term. Particular attention will be given to the ability of logistic regression to model the probability of renewal using selected policyholder and policy characteristics. The study will assess whether the model can effectively distinguish between policyholders who are likely to renew and those who are likely not to renew. The study will further examine the influence of factors such as policy duration, premium amount, payment frequency, claims history, policy type, policyholder characteristics, previous renewal behaviour, and payment patterns on insurance policy renewal. These factors will be incorporated into the logistic regression model to determine their relationship with renewal outcomes. The study will also assess the predictive performance of the model in identifying policyholders with different levels of renewal likelihood. A quantitative research approach will be adopted for the study. Historical insurance policy data containing information on renewal status and relevant policyholder and policy characteristics will be collected and analysed using logistic regression techniques. Descriptive statistics will first be used to examine the characteristics of the dataset, after which a logistic regression model will be developed to estimate the probability of policy renewal. Model performance may be evaluated using classification accuracy, sensitivity, specificity, and other appropriate statistical measures. The study is expected to reveal that logistic regression can provide useful predictions of insurance policy renewal behaviour. It is expected that factors such as previous renewal history, payment patterns, policy duration, premium level, and claims experience may have meaningful relationships with renewal outcomes. The findings may also indicate that policyholders with favourable policy and payment characteristics have different renewal probabilities from those with less favourable characteristics. The study is further expected to establish that logistic regression can provide insurers with a practical framework for identifying policyholders who are more or less likely to renew their policies. Reliable renewal predictions may assist insurers in forecasting future premium income, improving customer retention strategies, managing policy portfolios, and allocating resources more effectively. The study may also demonstrate that the quality and completeness of historical policy data are important for achieving reliable prediction results. The study concludes that logistic regression is a useful statistical technique for predicting insurance policy renewal and supporting evidence-based insurance decision-making. It is therefore recommended that insurance companies strengthen their policy data collection and management systems, regularly analyse renewal patterns, apply logistic regression and other appropriate predictive techniques, and use reliable renewal predictions to improve customer retention, premium forecasting, and overall portfolio management.

Keywords: Logistic Regression, Insurance Policy Renewal, Renewal Prediction, Insurance Policies, Policyholder Behaviour, Policy Retention, Customer Retention, Predictive Modelling, Insurance Analytics, Renewal Probability, Policy Duration, Premium Payment, Claims History, Statistical Modelling, Insurance Portfolio Management.