 Addressing unanticipated interactions in risk equalization: A machine learning approach to modeling medical expenditure riskI. Ismail, P.J.A. Stam, F.R.M. Portrait, A. van Witteloostuijn and X. Koolman Economic Modelling, 2024, vol. 130, issue C Abstract: Adverse selection harms market efficiency and access to essential services, particularly for disadvantaged groups. Risk equalization policies attempt to mitigate this by compensating agents for risk disparities, but often fall short of addressing interactions between risk factors. Using health insurance data from the Netherlands, we present a machine learning approach to capture unanticipated interactions that impact medical expenditure risk. We compare our novel approach to a state-of-the-art statistical model. We find that our approach explains an additional 1.5% of medical expenditure, equivalent to 571 million euros over all individuals in the Dutch market. In particular, this translates into better compensation for low- and high-cost groups that are especially vulnerable to adverse selection. These findings confirm the significance of risk factor interactions in explaining medical expenditure risk, and support the adoption of machine learning alongside statistical models to further mitigate selection incentives in risk equalization policies. Keywords: Risk equalization; Machine learning; Risk selection; Model fitting; Health insurance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecmode:v:130:y:2024:i:c:s0264999323003760 DOI: 10.1016/j.econmod.2023.106564 Access Statistics for this article Economic Modelling is currently edited by S. Hall and P. Pauly More articles in Economic Modelling from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.