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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.

A new model for disability insurance tackles delays in claims by evolving in real-time. Unlike traditional methods, it adjusts reserves based on immediate information. By proposing modified reserves and estimators, it addresses delays effectively, demonstrated with real data, offering practical solutions for disability insurance schemes.