Tag Archives: insurance

数学・数理科学グローバル特別講義6

My upcoming lectures at Kyoto University as part of the Global Special Lecture Series in Mathematics and Mathematical Sciences (No. 6) is now on the agenda: November 4, 5, 10, 11, and 13, 2025, from 9:30 till 11:30, at the Graduate School of Science, Kyoto University.

It will be one “Fairness and Distribution in Insurance – An Actuarial Perspective”. We will explore how fairness, risk sharing, and distributional concerns intersect with actuarial science and insurance, providing both theoretical insights and practical implications.

I am truly honored to contribute to this international program and look forward to engaging with students and colleagues in Kyoto.

Personalized Insurance Premiums Cheaper Thanks to AI? Here’s Why It’s a Slippery Slope

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.

In the end, who will bear the cost of insurance?

This article was written with Laurence Barry, co-holder of the PARI Chair (Research Program for the Understanding of Risks and Uncertainties), initially in French.

Storms that repeat, premiums that soar, insurers pulling out: in the era of climate change, one question arises: who will ultimately foot the bill?

From the workers’ mutuals of the 19th century, created to cushion the blows of industrial development, to the shareholder logics of today’s multinationals, insurance has always reflected the great risks of its era.

Now, under the pressure of climatic events with unprecedented frequency and severity, the sector faces a new equation: how to remain solvent and socially legitimate when claims (amounts paid out by an insurance company for losses) grow faster than premiums (collected)? Between soaring rates, exclusions of coverage, and the threat of entire territories becoming uninsurable, how must insurance solidarity reinvent itself?

Each for all, and all for each

Since 1990, insured losses linked to natural disasters have grown by 5% to 7% per year – 137 billion U.S. dollars in 2024, and a trend toward 145 billion in 2025. Lucian Coman/Shutterstock

Before becoming a financial industry worth billions of euros, insurance began as a simple common pot: members contributed, the victims drew from it, and the surplus (if any) returned to the policyholders. From solidarity and mutual aid organizations created under the Hanse (the Hanseatic League, network of merchant cities in Northern Europe between the 13th and 17th centuries) to medieval guilds, associations of people practicing the same trade or activity, the logic was already that of a risk-pooling, a zero-sum sharing of risk. Each pays for all, and all for each.

In the guilds of the Middle Ages, in Europe, each master craftsman paid an annual fee that financed the rebuilding of a workshop destroyed by fire or support for the widow in case of death. For economic historian Patrick Wallis (eprints.lse.ac.uk), this was the first structured relief fund. The Danish charters of 1256, which imposed compulsory “fire aid” (brandstød) after a loss, provide a perfect example, as shown by social policy researcher Bernard Harris.

The principle runs through the centuries. In the 19th century, mutual aid societies introduced rebates when claims were milder than expected. Even today, nearly one in two policyholders in fire, accident, and miscellaneous risks (P&C) belongs to a mutual where he or she is a statutory co-owner.

The financial equation remains fragile: when the climate transforms chance into near-certainty, the premium is no longer a simple “cake-sharing” but an ever-larger advance on future expenses. The Swiss Re group has calculated (swissre.com) that, since 1990, insured losses linked to natural disasters have grown by 5% to 7% per year – 137 billion U.S. dollars in 2024, with a trend toward 145 billion U.S. dollars in 2025 (respectively 118 billion and 125 billion euros).

The mutualist model (erudit.org), founded on the relative rarity of claims and geographic diversification, is forced to reinvent its solidarity if frequency doubles and severity explodes… at the risk of tipping toward segmentation as fine as that of shareholder-driven insurers.

Solidarity pricing and shareholder optimization

Starting in the 1990s, financialization injected a new imperative: the premium must cover claims, finance marketing, remunerate equity, and occasionally serve as an adjustment variable for quarterly targets. Pricing optimization, popularized under the term price optimization, dissects thousands of behavioral variables (number of clicks before signing, banking inertia, connection times) in order to estimate the individual reservation price, that is the minimum price a seller is willing to accept, or a buyer is willing to pay, in a transaction.

In other words, it is no longer only a matter of estimating the actuarially most “fair” premium (the actuary being the expert in risk management), in the sense given by Kenneth Arrow in 1963 (aeaweb.org), but also the highest premium the policyholder is willing to pay. The fair premium being the expected average cost of claims, the amount the insurer expects to pay next year for similar risks.

The Institute of Australian Actuaries denounces, in its report The Price of Loyalty actuaries.asn.au), a systematic penalization of loyal clients, likened to a tax on trust. In the United Kingdom, the Financial Conduct Authority (FCA, ca.org.uk) struck hard. Since January 1, 2022, renewal pricing must be identical to that for a new client at equal risk; the authority estimates the savings for households at £4.2 billion over ten years.

This regulatory battle goes well beyond price. By relegating the logic of pooling to the background, behavioral optimization strengthens indirect socio-economic indicators—such as age, the digital divide, or residential stability—which end up weighing more than pure technical risk in determining the premium.

Now the insurer has access to private data lakes (raw data), where the policyholder is unaware of what makes their premium more expensive. By nature, contracts remain resistant to any simplified comparison. One shows a €2,000 deductible, another a lower compensation ceiling or exclusions hidden in tiny clauses, so that it takes an almost legal review to really align the offers, as a European Commission report pointed out (commission.europa.eu).

One file out of two refused

The rise of extreme events brutally illustrates these drifts. In Australia, three climatic phenomena in the first half of 2025, including Cyclone Alfred, generated 1.8 billion Australian dollars (AUD), or 1 billion euros, in compensation claims. The Insurance Council warns that home insurance premiums will see double-digit increases, and some contracts could reach 30,000 AUD per year (or €16,600 per year) in the most exposed areas (abc.net.au).

In the United States, California is seeing cancellations and refusals of coverage. A report cited by the Los Angeles Times shows that three major insurers declined nearly one file out of two in 2023 (latimes.com). A class action accuses 25 insurance companies of collusion with the aim of pushing victims toward the FAIR Plan, a pool of last-resort insurers with reduced coverage (cbsnews.com).


Toward systemic “uninsurability”

The phenomenon is not marginal. Insurers are reducing their exposure. U.S. insurers State Farm and Allstate stopped issuing new policies in California as early as 2023 (reuters.com). In Florida, because it intervenes when no private insurer accepts to cover a home at a reasonable price, the public last-resort insurer Citizens saw its portfolio grow to about 1.4 million policies at the peak of the crisis, then fall back below one million at the end of 2024 thanks to transfers (takeouts) to private players—a real improvement, but one that still reveals a fragile market (insurancejournal.com).

At the global level, Swiss Re counts 181 billion U.S. dollars of 2024 losses left to victims or states, or 57% of the total (swissre.com).  Faced with these growing protection gaps, insurers are reducing their exposure. This contraction of supply rebounds onto real estate finance: economist Bill Green reminds, in a letter to the Financial Times, that the slightest insurance default leads, in a few weeks, to the cancellation of mortgages supposed to secure the American middle class (ft.com).

When insurers withdraw or when the premium becomes unaffordable, property values collapse, and with them, the stability of an entire segment of the local banking system.

Refounding the social contract of risk

Nevertheless, avenues are emerging. The Center for American Progress proposes the creation of resilience funds co-financed by premiums and by the federal state, in order to finance levees, reinforced roofs, and relocations in very high-risk areas (americanprogress.org).

In Europe, France maintains a CatNat scheme based on a uniform mandatory surcharge – 20% in 2025 – for a risk reinsured by the Caisse centrale de réassurance (CCR). This mechanism guarantees unlimited compensation while pooling disasters across the entire national territory. Combined with incentive pricing (deductibles adjusted according to prevention measures), these mechanisms can preserve insurability without exploding individual premiums.

What remains is to address the upstream side: limiting exposure by freezing building permits in unbuildable areas, conditioning bank financing on climate compatibility, and sustaining, at the national level, a progressive climate prevention surtax that would finance structural adaptations while smoothing rate shocks (ecologie.gouv.fr, strategie-plan.gouv.fr)

At this price, insurance would once again become a common good: neither a pure financial product nor a simple common pot, but an essential infrastructure where society, and no longer only the insurer, consciously chooses the share of the climate bill it accepts to bear.

Artificial Intelligence and Personalization of Insurance: Failure or Delayed Ignition?

Our joint paper, Artificial Intelligence and Personalization of Insurance: Failure or Delayed Ignition?, with Xavier Vamparys, has been published in Big Data & Society.

In insurance, there is still a significant gap between the anticipated disruption, due to big data and machine learning algorithms, and the actual implementation of behaviour-based personalization, as described by Meyers (2018). Here, we identify eight key factors that serve as fundamental obstacles to the radical transformation of insurance guarantees, aiming to closely align them with the risk profile of each policyholder. These obstacles include the collective nature of insurance, the entrenched beliefs of some insurance companies, challenges related to data collection and use for personalized pricing, limited interest from insurers in adopting new models as well as policyholders’ reluctance towards embracing connected devices. Additionally, the hurdles of explainability, insurer inertia and ethical or societal considerations further complicate the path toward achieving highly individualized insurance pricing.

The Insurance Market in the Era of Digital Transitions

A few months ago, we spent time with Raphaël Suire to write a short article on the insurance market, or more specifically, “The Insurance Market in the Era of Digital Transitions: Relationships Between Insurers, Big Tech, and Insurtechs“. The report is now available, on the webiste of the Society of Actuaries.

The digital revolution has profoundly transformed market dynamics, particularly within the insurance sector. This transformation encompasses the infrastructure and technologies that facilitate information exchange, the emergence of new business practices, a deluge of data, and the rise of innovative players capitalizing on these changes to deliver unique value propositions to customers. Traditional insurance companies face significant challenges and opportunities as they navigate competition from established Big Tech firms and agile insurtech startups. This study examines the disruptive nature of digital advancements, compelling historical players to confront the innovator’s dilemma (Christensen, 1997): whether to adapt and develop established practices or invest in new strategies to leverage digital opportunities. In doing so, they also come up against smaller, more agile start-ups. We highlight the necessity for insurance actors to rethink their roles in light of new market entrants and the evolving landscape shaped by Big Tech’s data monetization strategies. To analyze these dynamics, we propose an original framework in the form of a triangle of possibilities, which positions various market players and elucidates their strategic movements, innovations, and possible partnerships. This framework also aids in identifying competitive advantages and development trajectories, ultimately offering scenarios for the evolution of traditional insurance players in a digital and data-driven era.

The role of government versus private sector provision of insurance

A short paper The role of government versus private sector provision of insurance has just been published in the Journal of Risk and Insurance.

Insurance markets are important for managing risk and promoting economic stability, since they play a key role in mitigating financial losses from unpredictable events such as natural disasters, cyberattacks, and health crises. However, these markets often face challenges, including market failures, information asymmetries, and correlated risks that can destabilize private insurers. In response, governments frequently intervene in insurance markets, either by providing insurance directly or by acting as a reinsurer of last resort. The interaction between government and private sector provision of insurance raises interesting and important questions about the appropriate role of each player in ensuring market efficiency and protecting individuals and businesses from catastrophic risks.

Selection bias in insurance: why portfolio-specific fairness fails to extend market-wide

With Marie-Pier Côté and Olivier Côté, we recently upload a short note, selection bias in insurance: why portfolio-specific fairness fails to extend market-wide, now available on SSRN,

Fairness centres on people. In insurance, the scope of fairness should be the entire insured population, not solely an insurer’s clients. However, each insurance company’s portfolio represents a possibly skewed subsample. Models fit to these selection-biased data do not generalise well for the broader population of insureds. Two biases stem from portfolio composition: representation bias, when large prediction errors are made on individuals from subpopulations infrequently observed, and selection bias, when underwriting and marketing skew the portfolio away from the insured population. We examine how portfolio composition affects fair premium methodologies for mitigating direct and indirect discrimination on a protected attribute. We illustrate how unfairness mitigation based on a selection-biased portfolio does not yield a fair market from the perspective of insureds. Relying on causal inference and a portfolio composition indicator, we describe the selection mechanism and determine conditions under which each bias affects various fairness-adjusted premiums. We propose a method to recover the population-wide fairness-adjusted premiums from selection-biased data, by using a (third-party provided) unbiased estimate of the prohibited attribute distribution. We show that this approach effectively mitigates selection bias but leads to overall premiums that are not balanced. In a limiting case, we show that portfolio-specific fairness-aware premiums can lead to a market-wide unawareness strategy: portfolio composition opens the back door to proxy discrimination.

(to be continued…)

Talk at the Financial Conduct Authority, UK

This morning (Montréal time), I will give a talk for the Financial Conduct Authority in London, in the UK, on “Demystify fairness and discrimination in insurance, and avoid some pitfalls“.

What’s unique about insurance is that even statistical discrimination, which by definition is devoid of malicious intent, poses significant challenges. Because, on the one hand, policymakers would like insurers to treat their policyholders equally, without discrimination based on race, gender, age or other characteristics, even if it could make (statistical) sense to (indirectly) discriminate. On the other hand, at the core of actuaries’ activities lies discrimination, between risky and non-risky policyholders. And this risk is often statistically correlated with sensitive characteristics that regulation would like to prohibit insurers from taking into account. The analysis of possible discrimination in decision rules, whether human or algorithmic, is an old subject. Most of the concepts date back at least to the 50s, but recent developments in artificial intelligence have brought these issues back into the spotlight. Massive data facilitate statistical or proxy discrimination, and black-box algorithms do not facilitate understanding. Not to mention the various regulations that make it difficult to collect sensitive information, and ultimately test whether decisions can be discriminated against, especially indirectly.

Insurance, Biases, Discrimination and Fairness

Insurance, Biases, Discrimination and Fairness was published a few weeks ago. I still plan to spend some time this summer on the R package, including data and some functions…

This book offers an introduction to the technical foundations of discrimination and equity issues in insurance models, catering to undergraduates, postgraduates, and practitioners. It is a self-contained resource, accessible to those with a basic understanding of probability and statistics. Designed as both a reference guide and a means to develop fairer models, the book acknowledges the complexity and ambiguity surrounding the question of discrimination in insurance. In insurance, proposing differentiated premiums that accurately reflect policyholders’ true risk—termed “actuarial fairness” or “legitimate discrimination”—is economically and ethically motivated. However, such segmentation can appear discriminatory from a legal perspective. By intertwining real-life examples with academic models, the book incorporates diverse perspectives from philosophy, social sciences, economics, mathematics, and computer science. Although discrimination has long been a subject of inquiry in economics and philosophy, it has gained renewed prominence in the context of “big data,” with an abundance of proxy variables capturing sensitive attributes, and “artificial intelligence” or specifically “machine learning” techniques, which often involve less interpretable black box algorithms.

The book distinguishes between models and data to enhance our comprehension of why a model may appear unfair. It reminds us that while a model may not be inherently good or bad, it is never neutral and often represents a formalization of a world seen through potentially biased data. Furthermore, the book equips actuaries with technical tools to quantify and mitigate potential discrimination, featuring dedicated chapters that investigate into these methods.

Talk in Stockholm, Sweden, at the Insurance Data Science Conference

This week, I will attend the Insurance Data Science conference in Sweeden. It has been a while… I was a keynote speaker at the one in London, ten years ago (to give a talk I still have feedbacks about – Getting into Bayesian Wizardry… (with the eyes of a muggle actuary) – by that time, the conference was “R in Insurance”), and then, we organized the one in Paris, back in 2017. Then we had the online events, but it was… different.

This time, I will get back to our recent paper A Sequentially Fair Mechanism for Multiple Sensitive Attributes, with François Hu and Philipp Ratz, and the equipy package, wrote with Agathe Fernandes-Machado and Suzie Grondin. The slides are available online.

Trip in (Northern) Europe

The next two weeks, in will be in (Northern) Europe, with a first stop in Brussels (to visit colleagues), then in Leuven (I will give a talk on Monday at KU Leuven), then in København (I will give a talk on Friday at Københavns Universitet), and finally in Stockholm (at Stockholm University, for the Insurance Data Science conference).

In the Fall, I will be in Europe, with Lisbon (European Actuarial Journal conference), in France (Cerisy Colloques) and in Warsaw in Poland. In Poland, I will give a two day cours on Insurance, Biases, Discrimination and Fairness

More to come soon…