Paper: arXiv 2602.15385
Authors: Ronald Richman, Mario V. Wüthrich
Abstract
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.
Complexity vs Empirical Score
- Math Complexity: 5.5/10
- Empirical Rigor: 5.0/10
- Quadrant: Holy Grail — high math complexity, high empirical rigor
Why this score: The paper presents a clever structural reframing of the classical Chain-Ladder method into direct multi-period projection, bridging traditional actuarial methods with machine learning applications for individual claims. While the core mathematical identity was previously known (as acknowledged by the authors in the addendum), the conceptual translation into an ML framework and the real-data proof-of-concept provide solid practical and methodological value.
Research Flowchart
flowchart TD
A["Research Goal: Modernize Chain-Ladder (CL)<br>for Individual Claims Reserving"] --> B["Data Inputs: Historical Claims Data<br>& Individual Claim Features"]
B --> C["Restructure Data Strategy:<br>Shift from 1-Period Roll-Forward<br>to Direct Multi-Period Factors"]
C --> D["Apply Machine Learning:<br>Train Neural Networks for<br>Individual Claim Factor Estimation"]
D --> E["Proof of Concept:<br>Test on Real-World<br>Non-Life Insurance Data"]
E --> F["Key Outcome: Unified Framework<br>Bridging Traditional CL Reserving<br>and ML-based Individual Reserving"]