Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data

Mazzi, Claudio and Damone, Angelo and Vandelli, Andrea and Ciuti, Gastone and Vainieri, Milena (2024) Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data. Risks, 12 (2). p. 24. ISSN 2227-9091

[thumbnail of risks-12-00024-v2.pdf] Text
risks-12-00024-v2.pdf - Published Version

Download (624kB)

Abstract

One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss–Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss–Newton method, where the choice of the initial guess is fundamental. This methodology is applied as a case study to the healthcare system of the Tuscany region. The results were validated by comparing them with state-of-the-art measurement of the confidence intervals of the Overdispersed Poisson distribution parameters with better outcomes. Hence, local healthcare authorities could use the proposed and improved methodology to allocate resources dedicated to healthcare and global management.

Item Type: Article
Subjects: Science Global Plos > Multidisciplinary
Depositing User: Unnamed user with email support@science.globalplos.com
Date Deposited: 30 Jan 2024 09:21
Last Modified: 30 Jan 2024 09:21
URI: http://ebooks.manu2sent.com/id/eprint/2459

Actions (login required)

View Item
View Item