1 research outputs found
Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method
Claim reserving is primarily accomplished using macro-level models, with the
Chain-Ladder method being the most widely adopted method. These methods are
usually constructed heuristically and rely on oversimplified data assumptions,
neglecting the heterogeneity of policyholders, and frequently leading to modest
reserve predictions. In contrast, micro-level reserving leverages on stochastic
modeling with granular information for improved predictions, but usually comes
at the cost of more complex models that are unattractive to practitioners. In
this paper, we introduce a simple macro-level type approach that can
incorporate granular information from the individual level. To do so, we imply
a novel framework in which we view the claim reserving problem as a population
sampling problem and propose a reserve estimator based on inverse probability
weighting techniques, with weights driven by policyholders' attributes. The
framework provides a statistically sound method for aggregate claim reserving
in a frequency and severity distribution-free fashion, while also incorporating
the capability to utilize granular information via a regression-type framework.
The resulting reserve estimator has the attractiveness of resembling the
Chain-Ladder claim development principle, but applied at the individual claim
level, so it is easy to interpret and more appealing to practitioners