# From Chain-Ladder to Individual Claims Reserving

arXiv: https://arxiv.org/abs/2602.15385 · HTML: https://mlsanity.com/paper/2602.15385/

## 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.

## Builds on (most cited first, 6 of 6)

- [The Credibility Transformer](https://mlsanity.com/paper/2409.16653/) (2024)
- [DeepTriangle: A Deep Learning Approach to Loss Reserving](https://mlsanity.com/paper/1804.09253/) (2018)
- [Individual Claims Forecasting with Bayesian Mixture Density Networks](https://mlsanity.com/paper/2003.02453/) (2020)
- [Micro-level Reserving for General Insurance Claims using a Long Short-Term Memory Network](https://mlsanity.com/paper/2201.13267/) (2022)
- [A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving](https://mlsanity.com/paper/2312.14549/) (2023)
- [Reinforcement Learning for Micro-Level Claims Reserving](https://mlsanity.com/paper/2601.07637/) (2026)

## 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.