Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method

32 Pages Posted: 4 Jul 2023 Last revised: 22 Jul 2023

See all articles by Sebastian Calcetero Vanegas

Sebastian Calcetero Vanegas

University of Toronto

Andrei Badescu

University of Toronto - Department of Statistics

X. Sheldon Lin

Department of Statistical Sciences, University of Toronto

Date Written: July 3, 2023

Abstract

Claim reserving is primarily accomplished using macro-level models, with the Chain-Ladder method being the most widely adopted method. These methods are usually constructed heuristically and rely on oversimplified data assumptions, neglecting the heterogeneity of policyholders, and frequently leading to modest reserve predictions. In contrast, micro-level reserving leverages on stochastic modeling with granular information for improved predictions, but usually comes at the cost of more complex models that are unattractive to practitioners. In this paper, we introduce a simple macro-level type approach that can incorporate granular information from the individual level. To do so, we imply a novel framework in which we view the claim reserving problem as a population sampling problem and propose a reserve estimator based on inverse probability weighting techniques, with weights driven by policyholders' attributes. The framework provides a statistically sound method for aggregate claim reserving in a frequency and severity distribution-free fashion, while also incorporating the capability to utilize granular information via a regression-type framework. The resulting reserve estimator has the attractiveness of resembling the Chain-Ladder claim development principle, but applied at the individual claim level, so it is easy to interpret and more appealing to practitioners.

Keywords: Claim reserving, Survey Sampling, Inverse Probability Weighting, Chain Ladder, Survival modeling

Suggested Citation

Calcetero-Vanegas, Sebastian and Badescu, Andrei and Lin, Xiaodong Sheldon, Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method (July 3, 2023). Available at SSRN: https://ssrn.com/abstract=4499355 or http://dx.doi.org/10.2139/ssrn.4499355

Sebastian Calcetero-Vanegas (Contact Author)

University of Toronto ( email )

105 St George Street
Toronto, Ontario M5S 3G8
Canada

Andrei Badescu

University of Toronto - Department of Statistics ( email )

100 St. George St.
Toronto, Ontario M5S 3G3
Canada

Xiaodong Sheldon Lin

Department of Statistical Sciences, University of Toronto ( email )

Department of Statistical Sciences
100 St George Street
Toronto, Ontario M5S 3G3
Canada

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