This article was initially written in French.
Insurance is based on a principle of solidarity that is undermined by the algorithms tasked with creating our profiles. As algorithms become more precise, the bill becomes more personalized. Various “at risk” profiles may thus find themselves excluded from insurance schemes, as the costs are so high. Personalization has an obvious legitimacy. But it must be reconciled with equitable access to insurance.
It must first be understood that insurance is marked by a fundamental paradox. On the one hand, its very principles assume a collective mechanism in which everyone contributes according to their capacity and benefits from solidarity in the event of a loss. On the other hand, technological advances, ever-larger datasets, and increasingly precise actuarial methods push toward ever greater individualization of premiums.
It is nothing less than reconciling actuarial refinement with the values of redistribution and solidarity on which the profession of insurer was founded.
To this tension is added an increasingly demanding legal framework, which prohibits any form of discrimination based on sensitive data, sometimes correlated with risk factors that are nevertheless relevant.
Pricing segmentation
Insurance companies have long used classification as the pillar of their economic model: age, sex, profession, geographic area, claims history…
In 1662, English statistician John Graunt published the Bills of Mortality, a first statistical analysis of London’s death registers. In 1693, English astronomer Edmund Halley developed the first quantified mortality table, allowing calculation of life expectancy at each age. These works laid the foundations for differentiated pricing by age and sex, which long remained the two main criteria of segmentation in life-death insurance.
At the same time, after the Great Fire of London in 1666, the first fire insurance contracts appeared: companies collected data on the type of construction materials and urban density. In the 18th–19th centuries, premiums were segmented according to the proximity of neighboring buildings and the presence of fire services, giving rise to the first “high-risk zones” and “low-risk zones.”
With the rise of the automobile in the 1910s–1920s, American insurers began systematically recording the number of claims, and the age and sex of drivers. As early as the 1920s, several pricing “classes” were distinguished: young drivers, women drivers, experienced drivers, making it possible to set variable premiums depending on the profile.
Today, actuaries have sophisticated algorithms, machine learning tools, and a flood of data: onboard telematics, connected objects, geolocation, driving or lifestyle behavior… For the insurer, refining segmentation makes it possible to charge each policyholder “their true risk level,” reducing the cross-subsidization effect from good risks to bad ones, while improving overall profitability.
But overly fine pricing reduces pooling; it can make insurance very expensive, even inaccessible for certain high-risk segments. Hence today, actuaries seek a subtle balance, aiming to capture the right information to differentiate profiles, while preserving the viability of the insured community.
Policyholders and the illusion of winning personalization
In Europe, the legislative proposal FIDA (Financial Data Access Framework) would open regulated access for insurers to individuals’ financial data. Its purpose is to refine understanding of spending and repayment behaviors. In this context, the promise of ultra-personalized pricing arouses both hopes of lower premiums and fears of excessive profiling and significant exclusions.
Faced with this new influx of data, many clients perceive personalization as a win-win approach: if I manage my budget better, I will benefit from a discount; if my saving and repayment habits are judged virtuous, my health premium will decrease; if my financial profile improves, my home insurance will become lighter.
This “pay-as-you-live” or “pay-how-you-drive” logic appeals: individuals believe themselves masters of their insurance cost through their lifestyle choices.
Yet several points deserve to be highlighted.
The principle of pooling is not neutralized: those who cannot adopt the most virtuous behaviors remain dependent on the solidarity of others. Indeed, even if higher-risk individuals pay more individually, those who are less at risk nevertheless continue to bear part of the costs thanks to the principle of pooling.
The asymmetry of information is reinforced, as the insurer knows statistics better than the client. The offer of personalization is often based on correlations, sometimes tenuous, whose scope the client does not understand.
Very fine personalization can force the most at-risk to over-insure, or on the contrary to give up insurance, weakening the pool.
Thus, even strengthened by access to financial data, “personalization” is not necessarily synonymous with “empowerment” for the consumer.
The legal framework: when the fight against discrimination is required
The development of big data in insurance raises important ethical and legal questions: how far can sensitive variables be exploited to predict risk?
In France and in the European Union, legislation explicitly prohibits discrimination based on protected criteria: ethnic origin, gender, sexual orientation, disability, religious beliefs, etc. The Solvency II Directive (EU) requires insurers to use “transparent” and non-discriminatory risk models.
Unlike the European Union—which bans differentiated pricing based on protected criteria (gender, origin, disability)—the Quebec model offers a more permissive framework. While the Charter of Human Rights and Freedoms of Quebec also prohibits discrimination, it provides exemptions specific to insurers: they can, when a factor is statistically relevant, base pricing on age, sex, or marital status.
This usage, authorized solely on the basis of a correlation, raises questions.
Ethics and social responsibility of insurers
Beyond mere legal compliance, insurers are increasingly judged on their ethical practices and social responsibility by consumer associations and the media, which relay incidents of algorithmic discrimination and exert reputational pressure.
In recent years, insurers must therefore ask themselves collectively how to guarantee equitable access to their products for vulnerable populations, without sacrificing the financial viability of their portfolios. Some innovative models propose “solidarity” formulas or capped premiums to avoid exclusion.
Insurers are being required to show more and more transparency. They must clearly explain pricing criteria, make calculation keys accessible to avoid a feeling of arbitrariness. Finally, they must integrate data protection and privacy from the design stage of offers (“privacy by design”), in order to preserve trust.
Insurers that manage to reconcile personalization, fairness and inclusion will become reference players for clients concerned with ethics.
Reconciling solidarity and data: a crucial challenge
The challenge, as we see, is considerable.
It is nothing less than reconciling actuarial precision with the values of redistribution and solidarity that founded the insurance profession.
It is in resolving this tension that the future of insurance will be decided: neither pure price discrimination nor simple illusory personalization, but rather a balance allowing each to contribute according to their risk and to benefit in fair measure from the pooling of life’s uncertainties.