A robust random coefficient regression representation of the chain-ladder method

Abstract

It is well known that the presence of outliers can mis-estimate (underestimate or overestimate) the overall reserve in the chain-ladder method, when we consider a linear regression model, based on the assumption that the coefficients are fixed and identical from one observation to another. By relaxing the usual regression assumptions and applying a regression with randomly varying coefficients, we have a similar phenomenon, i.e., mis-estimation of the overall reserves. The lack of robustness of loss reserving regression with random coefficients on incremental payment estimators leads to the development of this paper, aiming to apply robust statistical procedures to the loss reserving estimation when regression coefficients are random. Numerical results of the proposed method are illustrated and compared with the results that were obtained by linear regression with fixed coefficients.

Heat Equation as a Tool for Outliers Mitigation in Run-Off Triangles for Valuing the Technical Provisions in Non-Life Insurance Business

“…Current research on the removal of outliers in contaminated datasets for claims reserves is quite limited as the traditional methods are preferably applied. In the light of the most recent works published in this topic (Avanzi et al 2022b;Badounas et al 2022), our method takes on a different approach. First, outlying values are detected and smoothed by the heat equation application.…”

Copula-based Modeling for IBNR Claim Loss Reserving

“…, J}, and C n i,j is the estimated number of IBNR reserve for the accident year i and the development year j [32]. Recently, the CL method faced high interest in insurance applications such as [33][34][35][36].…”