A joint model of cost and churn for the insurance industry

Abstract In insurance markets, claim costs are highly variable, heavy‐tailed, and difficult to predict. At the same time, policyholder retention and lapse behavior (customer churn) are critical determinants of long‐term profitability and solvency. Most existing models in the literature treat claim costs and lapses as independent, overlooking potential latent associations that arise from adverse selection and unobserved heterogeneity. In this article, we introduce a joint modeling framework that simultaneously captures individual‐level claim costs and churn behavior, using multivariate Tweedie regression with shared random effects. This framework integrates claim cost dynamics with lapse risk, allowing insurers to more accurately predict costs, classify policyholder profitability, and design retention or pricing strategies. Applying our approach to data from the Wisconsin Local Government Property Insurance Fund, we demonstrate that accounting for dependence between claim risk and lapse risk improves out‐of‐sample prediction and yields actionable insights for customer valuation and management.