Mazzi, C.; Damone, A.; Vandelli, A.; Ciuti, G.; Vainieri, M. Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data. Risks2024, 12, 24.
Mazzi, C.; Damone, A.; Vandelli, A.; Ciuti, G.; Vainieri, M. Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data. Risks 2024, 12, 24.
Mazzi, C.; Damone, A.; Vandelli, A.; Ciuti, G.; Vainieri, M. Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data. Risks2024, 12, 24.
Mazzi, C.; Damone, A.; Vandelli, A.; Ciuti, G.; Vainieri, M. Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data. Risks 2024, 12, 24.
Abstract
One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. The state-of-the-art envisages various methods for estimating the claims reserve but without being applied to systems that do not present huge variability and heterogeneity, such as the healthcare sector. The methodology is based on generalized linear models using the Overdispersed Poisson distribution as shown in the state-of-the-art. 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. The proposed methodology has been entirely developed using MATLAB and has been applied to estimate the claims reserve in the healthcare system of the Tuscany Region in Italy, as case study. The results obtained were validated by comparing them with the state-of-the-art by measuring the confidence intervals of the Overdispersed Poisson distribution parameters. Therefore local healthcare authorities could use the proposed and improved methodology to allocate the resources dedicated to healthcare and global management.
Keywords
healthcare; claims reserving; generalized linear models; medical malpractice; error estimation
Subject
Business, Economics and Management, Econometrics and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
