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

Claudio Mazzi, Angelo Damone (), Andrea Vandelli, Gastone Ciuti and Milena Vainieri
Additional contact information
Claudio Mazzi: Department of Management, School of Advanced Studies Sant’Anna, 56127 Pisa, Italy
Angelo Damone: The BioRobotics Institute, School of Advanced Studies Sant’Anna, 56127 Pisa, Italy
Andrea Vandelli: Department of Management, School of Advanced Studies Sant’Anna, 56127 Pisa, Italy
Gastone Ciuti: The BioRobotics Institute, School of Advanced Studies Sant’Anna, 56127 Pisa, Italy
Milena Vainieri: Department of Management, School of Advanced Studies Sant’Anna, 56127 Pisa, Italy

Risks, 2024, vol. 12, issue 2, 1-29

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.

Keywords: healthcare; claims reserving; generalized linear models; medical malpractice; error estimation (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-9091/12/2/24/pdf (application/pdf)
https://www.mdpi.com/2227-9091/12/2/24/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:12:y:2024:i:2:p:24-:d:1329099

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().