The Negative Binomial Chain-Ladder: A Full Likelihood Model for Claim Count Reserving

TL;DR

This paper introduces a probabilistic Negative Binomial Chain-Ladder model for claims reserving, providing a full likelihood framework that interprets dispersion as accident-year heterogeneity, unifying existing methods.

Contribution

It develops a full likelihood-based NB-CL model with a structural interpretation of dispersion, extending the classical CL method within a probabilistic hierarchy.

Findings

Simulation studies show near-nominal coverage with bias correction.

The model unifies Poisson and negative binomial frameworks.

Empirical data illustrate the structural interpretation of dispersion.

Abstract

The Chain-Ladder (CL) method remains the dominant macro-level technique for claims reserving in non-life insurance, yet its classical formulation lacks a coherent probabilistic foundation. Existing stochastic extensions-including the Mack model and the Over-Dispersed Poisson (ODP) framework-provide measures of uncertainty but rely on second-moment assumptions or quasi-likelihood variance structures without clear generative interpretations. This paper develops a Negative Binomial Chain-Ladder (NB-CL) model that embeds the CL method within a full likelihood-based framework. The key contribution is a micro-level derivation showing that the negative binomial distribution arises naturally from a Poisson-Gamma construction: claims arrive according to a Poisson process with Gamma-distributed accident-year heterogeneity, and aggregation yields negative binomial incremental counts. This derivation gives the dispersion parameter $\kappa$ a structural interpretation as accident-year heterogeneity, rather than an ad-hoc overdispersion adjustment. The NB-CL model generalises the Poisson Chain-Ladder model in the limit $\kappa \to \infty$, shares the point estimates of the ODP model while differing in its variance function (quadratic vs. linear), and unifies the Chain-Ladder family within a single probabilistic hierarchy. A parametric bootstrap procedure is developed to incorporate both process and parameter uncertainty. Simulation studies confirm near-nominal coverage under correct specification once the dispersion parameter is bias-corrected, and a controlled degradation under model misspecification. Empirical illustrations on claim count data (Australian motor bodily injury) and paid amounts (Taylor-Ashe) document both the structural reading of $\kappa$ and the working-approximation status of the model in the amounts case.