  Claim reserving primarily relies on macro-level models, with the Chain-Ladder
method being the most widely adopted. These methods were heuristically
developed without minimal statistical foundations, relying on oversimplified
data assumptions and neglecting policyholder heterogeneity, often resulting in
conservative reserve predictions. Micro-level reserving, utilizing stochastic
modeling with granular information, can improve predictions but tends to
involve less attractive and complex models for practitioners. This paper aims
to strike a practical balance between aggregate and individual models by
introducing a methodology that enables the Chain-Ladder method to incorporate
individual information. We achieve this by proposing a novel framework,
formulating the claim reserving problem within a population sampling context.
We introduce a reserve estimator in a frequency and severity distribution-free
manner that utilizes inverse probability weights (IPW) driven by individual
information, akin to propensity scores. We demonstrate that the Chain-Ladder
method emerges as a particular case of such an IPW estimator, thereby
inheriting a statistically sound foundation based on population sampling theory
that enables the use of granular information, and other extensions.
