Which model is the most suitable for capturing different levels of risk among policyholders submitting claims?

The latent class model is particularly suited for capturing different levels of risk among policyholders submitting claims because it allows for the identification of subgroups within a population that exhibit distinct characteristics or risk profiles. By categorizing policyholders into latent (unobserved) classes based on their claim behavior, this model can effectively capture the underlying heterogeneity in risk levels across different groups.

This capability is critical in insurance contexts where not all policyholders are the same; some might have a higher propensity for filing claims due to various factors like demographic differences or previous claims history. The latent class model enables actuaries and risk managers to tailor their strategies and pricing based on the differences in risk presented by these various classes.

Other models, while useful in specific contexts, may not possess the same level of granularity when differentiating risk among diverse policyholder groups. For instance, a zero-inflated model is typically designed to handle count data with excess zeros but may not reveal the distinct risk profiles of policyholders effectively. Similarly, although heterogeneity models address aspects of variability in risk, they may not explicitly define subgroups in the same way that latent class models do. Logistic regression can model binary outcomes but does not inherently account for differences in risk among unobserved segments,