Modelling dependence within and across run-off triangles for claims reserving

TL;DR

This paper introduces a Bayesian stochastic model for claims reserving that captures dependence within and across run-off triangles using autoregressive latent variables and hierarchical priors, improving inference across multiple lines of business.

Contribution

It presents a novel hierarchical Bayesian model that captures dependence within and across run-off triangles for claims reserving, enabling improved inference and borrowing strength across different datasets.

Findings

Effective modeling of dependence within run-off triangles.

Hierarchical priors facilitate sharing information across multiple triangles.

Bayesian inference provides robust parameter estimation.

Abstract

We propose a stochastic model for claims reserving that captures dependence along development years within a single triangle. This dependence is of autoregressive form of order $p$ and is achieved through the use of latent variables. We carry out bayesian inference on model parameters and borrow strength across several triangles, coming from different lines of businesses or companies, through the use of hierarchical priors.