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（CL）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流程中数据利用方式根本性重构的新颖方法，用于计算链梯法准备金。我们不再通过估计的链梯因子滚动预测累计索赔额，而是直接利用最新观测数据估计多期因子以预测最终索赔额。这种链梯法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，我们通过小规模真实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。