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.

翻译：索赔准备金评估主要采用宏观层面模型，其中链梯法是最广泛使用的方法。这些方法通常基于启发式构造，依赖过度简化的数据假设，忽略了保单持有人的异质性，常导致预测精度有限。相比之下，微观层面准备金评估利用随机建模与细粒度信息来改进预测，但通常以模型复杂性增加为代价，这使其从业者吸引力不足。本文提出了一种简单的宏观层面方法，能够整合个体层面的细粒度信息。为此，我们引入了一个新框架，将索赔准备金评估问题视为总体抽样问题，并基于逆概率加权技术提出了准备金估计量，其权重由保单持有人属性决定。该框架在频率与严重程度分布自由的条件下，提供了具有统计严谨性的聚合索赔准备金评估方法，同时通过回归式框架具备利用细粒度信息的能力。最终得到的准备金估计量兼具链梯法索赔发展原理的直观性，并应用于个体索赔层面，因此易于解释且对从业者更具吸引力。