Publication
Title
Addressing unanticipated interactions in risk equalization : a machine learning approach to modeling medical expenditure risk
Author
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.
Language
English
Source (journal)
Economic modelling. - Guilford
Publication
Guilford : 2024
ISSN
0264-9993
DOI
10.1016/J.ECONMOD.2023.106564
Volume/pages
130 (2024) , p. 1-10
Article Reference
106564
ISI
001106256100001
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 09.01.2024
Last edited 10.01.2024
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