Nos derniers articles

The paper presents a framework for individual claims reserving based on the projection-to-ultimate (PtU) method as an alternative to the traditional chain-ladder approach. It describes how reserving can shift from aggregate loss triangles to claim-level modeling by directly estimating ultimate claim costs. The approach is presented as compatible with classical actuarial structures while enabling the use of stochastic covariates and machine learning models, including neural networks and transformers. The authors emphasize decomposing reserves into Reported But Not Settled (RBNS) and Incurred But Not Reported (IBNR) components to maintain consistent claim cohorts. Case studies suggest that linear regression can perform robustly in individual-claim settings.

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.

This study analyzes insurance claim processing delays due to limited capacity and backlogs. It proposes optimal capacity selection to minimize costs by accounting for delay-adjusted and fixed settlement costs, supported by theoretical insights and a large-scale numerical study to demonstrate practical application.