From Chain-Ladder to Individual Claims Reserving

TL;DR

This paper proposes a new approach to claims reserving that restructures data usage in the chain-ladder method, enabling the application of machine learning techniques like neural networks for individual claims prediction.

Contribution

It introduces a novel data restructuring method for the chain-ladder technique, facilitating the integration of machine learning into claims reserving.

Findings

Successful application of neural networks to individual claims reserving

Enhanced accuracy in reserve estimation through the new approach

Potential for broader machine learning integration in insurance reserving

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.