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Anti-selection & Genetic Testing in Insurance: An Interdisciplinary Perspective
Preprint   Open access

Anti-selection & Genetic Testing in Insurance: An Interdisciplinary Perspective

Dexter R. Golinghorst, Aisling de Paor, Yann Joly, Angus Smith Macdonald, Margaret Otlowski, Richard Peter and Anya E.R. Prince
SSRN
01/01/2021
DOI: 10.2139/ssrn.3863417
url
https://doi.org/10.1017/jme.2022.18View
Published (Version of record)This article has now been published in a journal and has been peer-reviewed by subject experts. This version may differ significantly from the preprint version. Access restricted to faculty, staff and students
url
https://doi.org/10.2139/ssrn.3863417View
Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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Abstract

Anti-selection occurs when information asymmetry exists between an insurer and an applicant. When an applicant knows that they are at high risk of loss, but the insurer does not, the applicant may try to exploit this knowledge differential to secure insurance at a lower premium that does not match risk. Predictive genetic testing could lead to anti-selection if individuals, but not insurers, learn of genetic risk. Yet, to address fear of discrimination, several countries have, or are considering, limitations on insurers’ use of predictive genetic test results. In this paper, we discuss anti-selection theory and modeling and illustrate how regulation regarding insurer use of predictive genetic test results could impact anti-selection in insurance markets. The extent of this impact turns on how much individuals alter their insurance purchasing behavior following predictive genetic testing. At first blush it may seem likely that those who learn that they are at high-risk of a genetic condition would attempt to gain greater coverage. However, we highlight several domains of on-the-ground realities that challenge this baseline assumption. These real-world considerations should be incorporated into modeling of anti-selection to truly assess the potential impacts of regulation limiting insurer use of predictive genetic testing.
 Business Purchasing Actuarial science Baseline (configuration management) Exploit Genetic testing Information asymmetry Risk of loss Selection (genetic algorithm) Test (assessment)

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