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Claim reserving is primarily accomplished using macro-level models, with theChain-Ladder method being the most widely adopted method. These methods areusually constructed heuristically and rely on oversimplified data assumptions,neglecting the heterogeneity of policyholders, and frequently leading to modestreserve predictions. In contrast, micro-level reserving leverages on stochasticmodeling with granular information for improved predictions, but usually comesat the cost of more complex models that are unattractive to practitioners. Inthis paper, we introduce a simple macro-level type approach that canincorporate granular information from the individual level. To do so, we implya novel framework in which we view the claim reserving problem as a populationsampling problem and propose a reserve estimator based on inverse probabilityweighting techniques, with weights driven by policyholders' attributes. Theframework provides a statistically sound method for aggregate claim reservingin a frequency and severity distribution-free fashion, while also incorporatingthe capability to utilize granular information via a regression-type framework.The resulting reserve estimator has the attractiveness of resembling theChain-Ladder claim development principle, but applied at the individual claimlevel, so it is easy to interpret and more appealing to practitioners.

Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method