Genetic Life Insurance Policies: Artificial Intelligence and the Price of Our DNA
The encounter between algorithmic predictive models and DNA analysis risks destroying the principle of mutuality in the insurance market. "Genetic life insuranc
Predictive artificial intelligence applied to genomics is silently undermining one of the historical and moral pillars of private welfare: the concept of risk mutualization. The encounter between advanced algorithmic models and low-cost DNA sequencing does not merely refine the traditional actuarial calculation of insurance companies, but threatens to transform a mere genetic probability into a ruthless condition for access to economic protection. The fundamental ethical dilemma of our time is not the reductive "use or not use the data," but rather to establish politically and legally which intimate information may be legitimately used to decide who can afford to be insured, under what financial conditions, and with what mandatory safeguards. When a neural network reads our genetic code to price a life insurance policy, it is not evaluating an ongoing pathology, but is monetizing a probabilistic hypothesis about our biological future.
The Illusion of Certainty and Polygenic Scores
The technological heart of this revolution bears the name polygenic risk scores. A seminal 2026 study published in Health Economics, Policy and Law analyzed the emerging use of these scores in the underwriting (risk assessment) of life insurance policies in the United Kingdom [2827]. Artificial intelligence excels at processing millions of genetic variants to calculate the cumulative impact on the risk of developing complex diseases over the course of a lifetime, such as severe cardiovascular pathologies, diabetes, or neurodegenerative conditions.
However, academic research draws a clear dividing line, too often ignored by insurance marketing, between diagnostic tests (which unequivocally certify the presence of a real condition) and predictive tests (which indicate only a statistical susceptibility). The authors of the British study warn that the highly stochastic nature of genetic causality makes the use of these scores extremely slippery [2829]. Predicting future healthcare costs, mortality rates, and related phenotypes based solely on a genetic abstraction risks generating a profoundly distorted actuarial effect. For this reason, the international scientific community urgently calls for the establishment of new deliberative processes and forms of independent oversight. A high genetic risk is not and must not be considered an irrevocable clinical condemnation, nor an individual fault that justifies the social and economic exclusion of the individual.
The Regulatory Void: The GINA Paradox and Real Cases
The practical consequences of a genetic analysis market devoid of robust ethical barriers are already dramatically visible in the United States, where the regulatory void creates paradoxical short circuits to the detriment of citizens. As emphasized in a detailed briefing by the National Human Genome Research Institute (2024), the federal law known as GINA (Genetic Information Nondiscrimination Act) effectively and historically protects citizens from genetic discrimination in the exclusive sphere of basic health insurance and the workplace. Yet this same legislation leaves a glaring gap, omitting coverage of crucial sectors of family financial life: life insurance, disability insurance, and long-term care insurance [2821].
An illuminating, raw, and all-too-real case study, analyzed in depth by the European Journal of Human Genetics (2020), concerns pathogenic variants of the BRCA1 and BRCA2 genes, mutations strongly correlated with a high risk of breast and ovarian cancer [2824]. People who test positive for these genetic tests — tests they often courageously decide to undergo precisely to initiate life-saving prevention pathways — suddenly find themselves facing prohibitive insurance premium increases or outright refusals of access to a normal life insurance policy to protect their children.
Legislative fragmentation irreparably worsens the overall picture: an academic survey conducted among state insurance commissioners (published on PMC in 2019) revealed a disastrous regulatory variability among individual American states, combined with poor regulatory awareness among officials and the absolute absence of transparency and independent audits on actual DNA-based underwriting practices [2825].
The "Chilling" Effect: When Fear Hampers Research and Care
The social harm generated by this cynical approach to risk management does not, however, end within the contractual perimeter of a single denied policy. The most devastating collateral impact occurs upstream, directly in the field of public health and preventive medicine. A crucial retrospective study published on PMC (2021), conducted on a representative sample of over 1,600 U.S. citizens, empirically and unequivocally demonstrated that the creeping fear of insurance discrimination is directly proportional to patients' reluctance to undergo clinical genetic testing [2820, 2826].
The more people develop the conviction — which the current market framework tragically makes well-founded — that their DNA results could in the future negatively affect their insurability, the more they are inclined to forgo genetic screenings that could literally save their lives. This sociological phenomenon, known in scientific and legal literature as the chilling effect, significantly slows the progress of collective genomic research and sabotages at its foundation the imposing state preventive medicine programs, effectively transforming life insurance from an instrument of family protection into a powerful psychological deterrent to personal care.
The European Approach: The AI Act and the Defense of Biometric Rights
Faced with this dangerous speculative drift, the European Union has chosen to adopt a decidedly proactive regulatory approach, strictly based on the objective classification of risk to human rights. This legal effort finds its highest expression in Annex III of the newly born AI Act. The European regulation has the merit of explicitly classifying as "high-risk systems" all those artificial intelligence architectures specifically intended for risk assessment and pricing for natural persons operating in the life and health insurance sector [2864]. The legislator's rationale is unequivocal: when an algorithm holds the asymmetric power to determine insurance premiums or to block a vulnerable citizen's access to coverage, it instantly ceases to be a simple and harmless commercial calculation tool and rises to critical infrastructure for the safeguarding of civil rights.
As the European Commission incessantly reminds us in its privacy directives, DNA falls fully within the category of special biometric data: it represents the most intimately identifying and irremediably non-revocable information (one cannot "change" one's DNA as one changes a compromised password) that a human being can share with third parties [2823].
In parallel, the European Insurance and Occupational Pensions Authority (EIOPA), in its recent technical annex on the exclusion of generalized linear models (GLM and GAM), draws a clear and technically impeccable line between the legitimate use of traditional actuarial statistical models and the massive employment of complex high-risk systems trained on genetic data [2828]. EIOPA formally confirms that predictive profiling in the life and health sector must be mandatorily subject to safeguard controls proportionate to the impact on the insured's life: impeccable training data quality, the absolute absence of discriminatory bias (fairness), exhaustive algorithmic documentation, and continuous human monitoring are required. The use of the algorithm is not prohibited a priori in Europe, but the decision-making opacity of "computer says no" is no longer legally tolerated.
Conclusions: Uncertainty as a Shared Value
The contemporary debate must not sterily stall on the false dichotomy between inevitable technological progress and the archaic defense of privacy. The central question that policymakers and business leaders must ask themselves is not whether we can technically know our genetic risk, but rather: who will legally own that exclusive knowledge, who will be tacitly authorized to use it against our financial interests, and what will become of all those who cannot afford, for purely economic reasons, to ignore that probabilistic diagnosis?
A polygenic score generated by artificial intelligence does not establish with mathematical certainty when a person will actually fall ill, nor does it in any way define how they will live, fight, or manage their health condition. If left to operate in the servers of multinational corporations without severe regulatory and ethical limits, the use of predictive AI in insurance risks dramatically transforming a merely statistical vulnerability into a formidable, insurmountable economic and social barrier [2827, 2829, 2824].
The true frontier of innovation and corporate social responsibility in the insurance sector does not consist in identifying, through algorithmic sieves, the genetically perfect customers for the sole purpose of cynically zeroing out portfolio risk. It consists instead in using the immense analytical capacity of data to expand inclusion, to personalize prevention pathways, and to break down barriers to entry for the most vulnerable. Ultimately, a life insurance policy was born and should continue to exist to protect us and our families from the ineluctable uncertainty of the future, and not to punish us preventively simply because our DNA strand contains, hidden among its helices, a calculable hypothesis about that future [2820, 2827, 2864].
Bibliographic References and Sources
- [Genomics and insurance in the United Kingdom — Health Economics, Policy and Law, 2026] [2827]
- [Genetic testing and insurance implications — PMC, 2021] [2820, 2826]
- [Privacy in Genomics — National Human Genome Research Institute, 2024] [2821]
- [Beyond the Genetic Information Nondiscrimination Act — PMC, 2017] [2829]
- [Long-awaited progress in addressing genetic discrimination in the United States — European Journal of Human Genetics, 2020] [2824]
- [A Survey of United States State Insurance Commissioners — PMC, 2019] [2825]
- [Annex III: High-Risk AI Systems — EU AI Act] [2864]
- [Biometrics — European Commission] [2823]
- [Technical Annex on the exclusion of GLMs and GAMs — EIOPA] [2828]
Article by the Editorial Staff of La Bussola dell'IA