scholarly journals Ethics, Insurance Pricing, Genetics, and Big Data

Author(s):  
Robert Klitzman

Insurers are rapidly gaining access to consumers’ genetic information. In the U.S., federal law bars using genetic information in health insurance, but not in life, disability, or long-term care insurance. Accordingly, insurers may fear adverse selection: individuals could undergo testing, learn they have risky genes, and purchase insurance without revealing test results. While other countries have established moratoria on insurers’ use of genetic information, there is no consensus in the U.S. regarding how to avoid ‘unfair discrimination.’ The chapter discusses alternative solutions, including government bans of insurers’ use of genetic information, or limiting insurer information to only high-risk genes.

2015 ◽  
Vol 20 (2) ◽  
pp. 348-365

This abstract relates to the following paper: AdamsC.Adverse selection in a start-up long-term care insurance market. British Actuarial Journal. doi:10.1017/S1357321714000270


2017 ◽  
Author(s):  
M. Martin Boyer ◽  
Philippe De Donder ◽  
Claude-Denys Fluet ◽  
Marie-Louise Leroux ◽  
Pierre-Carl Michaud

2019 ◽  
Vol 26 (2-3) ◽  
pp. 258-276 ◽  
Author(s):  
Irene Albarrán ◽  
Pablo J. Alonso-González ◽  
Aurea Grané

2010 ◽  
Vol 94 (11-12) ◽  
pp. 1041-1050 ◽  
Author(s):  
Emily Oster ◽  
Ira Shoulson ◽  
Kimberly Quaid ◽  
E. Ray Dorsey

2014 ◽  
Vol 20 (2) ◽  
pp. 298-347 ◽  
Author(s):  
C. Adams ◽  
C. Donnelly ◽  
A. Macdonald

AbstractCommon to all previous studies assessing the cost of adverse selection associated with genetics has been the assumption of an established market, i.e., the adverse selectors have been buying insurance at that rate for such a period that premiums have already absorbed it. Their analyses involve calculating the percentage difference between premiums in a market with adverse selection and one without adverse selection. They can shed no light on how the premiums would get to this stage over time and what losses might be incurred in the process. We take the modelling further by outlining a multiple state Markov model for a start-up market of long-term care insurance. With this model, we explicitly show the progression of adverse selection costs using the development of information that an insurer would gain from analysing the claims history of its existing business, to reprice premiums for new business. To overcome the complication of insurance benefit amounts, which depend on the value of previous benefit payments, we develop a simulation approach of estimating the expected present values of insurance benefits and premium payments. In applying our modelling to a UK setting, we find genetic testing of the apolipoprotein E gene (whose variants can cause a high risk of developing dementia) to be of a relatively small impact compared with our hypothetical state of intermediate dementia progression. Furthermore, we find that the government’s cap on care costs has little effect on adverse selection costs as it benefits only a small proportion of people.


2017 ◽  
Author(s):  
Martin Boyer ◽  
Philippe De Donder ◽  
Claude Fluet ◽  
Marie-Louise Leroux ◽  
Pierre-Carl Michaud

2017 ◽  
Author(s):  
M. Martin Boyer ◽  
Philippe De Donder ◽  
Claude-Denys Fluet ◽  
Marie-Louise Leroux ◽  
Pierre-Carl Michaud

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