From Chain-Ladder to Individual Claims Reserving
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 23 references that are in the map. First posted on arXiv 17 Feb 2026.
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6 of 23 references.
References · 6
- 2024The Credibility Transformercited by 5
- 2018DeepTriangle: A Deep Learning Approach to Loss Reservingcited by 4
- 2020Individual Claims Forecasting with Bayesian Mixture Density Networkscited by 4
- 2022Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Networkcited by 1
- 2023A machine learning approach based on survival analysis for IBNR frequencies in non-life reservingcited by 1
- 2026Reinforcement Learning for Micro-Level Claims Reservingcited by 1
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