The chain-ladder (CL) method is the most widely used claims reserving technique in non-life insurance. This manuscript introduces a novel approach to computing the CL reserves based on a fundamental restructuring of the data utilization for the CL prediction procedure. Instead of rolling forward the cumulative claims with estimated CL factors, we estimate multi-period factors that project the latest observations directly to the ultimate claims. This alternative perspective on CL reserving creates a natural pathway for the application of machine learning techniques to individual claims reserving. As a proof of concept, we present a small-scale real data application employing neural networks for individual claims reserving.

翻译：链梯法是非寿险领域应用最广泛的索赔准备金评估技术。本文提出了一种基于链梯法预测过程中数据利用方式根本性重构的新型链梯准备金计算方法。与使用估计的链梯因子滚动预测累计索赔的传统方式不同，我们通过估计多周期因子将最新观测值直接映射至最终索赔额。这种链梯准备金评估的替代视角为机器学习技术在个体索赔准备金评估中的应用开辟了自然路径。作为概念验证，我们通过小规模实际数据应用展示了神经网络在个体索赔准备金评估中的实践。