IoT sensors are transforming insurance into a prevention service, but this transformation is not neutral. In Latin America, where insurance penetration is generally low compared to major advanced economies, the promise of fairer pricing masks a symmetrical risk: that of increased segmentation that further excludes those who cannot afford to connect.
The essentials
- IoT sensors enable insurers to anticipate claims in real time and adjust offers based on individual behavior rather than collective statistics, according to Capgemini (Top Trends 2025).
- This shift displaces insurance logic: from risk mutualization toward personalized pricing, which rewards good profiles and increases coverage for others.
- In Latin America, access to connected devices remains unequal: the digital divide compounds an insurance divide that expands the population effectively uninsurable.
- Without regulation of data access and pricing algorithms, predictive insurance risks consolidating a two-speed offering rather than broadening protection.
- Experiments in parametric insurance and connected micro-insurance show that another path is possible, provided there is adapted regulatory architecture.
Mutualization, the historical pillar that IoT calls into question
This shift toward individual pricing does not occur uniformly. Insurers can adopt personalization on segments where data is abundant and customers solvent. Mutualization can then persist for profiles that segmentation fails to valorize, within a more restricted scope. Pricing segmentation can reduce the share of implicit solidarity in the collective contract.
Insurance has rested for two centuries on a simple principle: those who do not suffer losses finance those who do. This mutualization mechanism enabled millions of households and businesses to access protection they could not have assumed alone. Premiums reflected aggregated risks, calculated over cohorts, not individual behaviors in real time.
IoT sensors modify this equation. A telematics box in a car records acceleration, braking, nighttime trips, average speed. A sensor in a warehouse monitors temperature and humidity variations. A connected device in an apartment detects water leaks before they become damage. Capgemini’s Top Trends 2025 report indicates that property and casualty insurers use advanced risk models and real-time data to respond to risk volatility and develop more personalized pricing.
This semantic shift from indemnification toward prevention is real, and it carries concrete advantages. A prudent driver pays less. A homeowner who installs smoke detectors sees their premium decrease. The insurer alerts their customer before the leak floods the basement. Viewed from this angle, personalization appears equitable: everyone pays according to their actual risk.
But this logic has a downside. Mutualization had precisely the virtue of absorbing risk differences in a collective premium. When pricing becomes individual, those whose risk profile is structurally high, because they live in exposed areas, drive older vehicles, or inhabit dilapidated housing, pay more, or simply become uninsurable.
Data, a strategic asset for insurers
The ongoing transformation is not limited to sensors. It engages a complete architecture: data collection, real-time processing, predictive models, client interfaces. Capgemini emphasizes that data quality and IT process compliance now constitute a major technological risk for insurers, on par with conventional operational risks.
Large groups that invest in this infrastructure gain a structural competitive advantage. They can offer more attractive premiums to certain profiles and capture customers considered less risky. This phenomenon, known in economics as cream-skimming, gradually erodes the broad mutualization model.
Advanced insurers thus build veritable feedback loops: data improves the model, the model refines pricing, pricing retains profitable customers, who produce new data. This virtuous circle for the insurer is not necessarily virtuous for overall population coverage.
The issue here is that of algorithmic governance. When an algorithm sets a premium without the insured understanding the criteria used or being able to contest them, the transparency of the insurance contract erodes. European regulators have begun to frame these practices, notably via the General Data Protection Regulation, which imposes safeguards on automated decisions. Regulatory frameworks relating to data, AI, and connected insurance are heterogeneous and still evolving across Latin American countries. This connects to the broader debate on technological sovereignty: regulating systems without mastering their architecture remains a fragile exercise.
In Latin America, the digital divide compounds the insurance divide
Latin America presents a particular context. Insurance penetration rates there are structurally low: according to the Inter-American Insurance Federation (FIDES), insurance premiums represent on average less than 3% of regional GDP. In some advanced economies, premiums can exceed 8% or 10% of GDP; the OECD average was 6.2% in 2024. A portion of the economically active population remains without homeowners insurance, without liability coverage, or without agricultural protection.
Connectivity aggravates this imbalance. In major metropolises—São Paulo, Mexico City, Bogotá—smartphones are widespread and connected insurance offerings are developing. Insurtechs multiply products there: telematics auto insurance, micro-insurance indexed to weather data, health coverage linked to physical activity tracking applications. Zurich Insurance, SURA, and several local startups like Chubb or Bemoov have launched products of this type in Brazil, Colombia, and Mexico.
But in rural areas, peripheral neighborhoods, low-income communities, connectivity remains insufficient and connected devices are beyond financial reach. Poor and vulnerable populations, including in certain precarious urban neighborhoods and rural territories, may be more exposed to climate shocks and have weaker resilience capacity; risks must be analyzed locally.
Algorithmic segmentation thus creates a scissor effect. Those who have the tools to prove their low risk obtain low premiums. Those who cannot produce this data are either priced on unfavorable statistical bases or simply excluded from the offering. Personalization, sold as more equitable, can reinforce the inequalities it claims to correct.
Insurtechs adopting a different approach
The picture would be incomplete without experiments showing a different trajectory. Several actors in Latin America are precisely using connected data to reach populations previously excluded from formal insurance.
Parametric insurance is the most convincing example. The principle: automatically trigger a payment when an objective parameter—rainfall level, wind speed, seismic index—exceeds a threshold, without the need to file a claim or appoint an adjuster. Aon documented in 2025 a parametric program for coffee producers in the Nariño region, Colombia. Elements relating to Swiss Re, Bolivia, and the previous lack of insurance for farmers are not confirmed.
Satellite data plays a central role here: it allows measurement of weather conditions over specific plots without the farmer needing a smartphone or on-site sensor. IoT, in this case, is not a barrier for the insured to cross: it is external infrastructure that the insurer exploits directly.
Other actors work on micro-insurance distributed via mobile payment platforms. In Brazil, partnerships between insurers and fintech operators can offer minimal coverage, such as accidental death, hospitalization, or job loss, integrated into money transfer applications. This approach aligns with what we observe in other sectors: digital tools can expand access when designed for inclusion rather than segmentation.
These experiments still represent only a marginal fraction of the market. They nonetheless prove that connected insurance does not have a single trajectory.
Action levers for regulators, still underutilized
Framing pricing algorithms also poses a structural difficulty: regulators evaluate models whose complexity often exceeds their internal technical capacities. Without their own algorithmic expertise, they find themselves validating black boxes on the basis of statements produced by the actors they supervise. This information asymmetry weakens the scope of transparency obligations, even when they formally exist.
Regulation is the lever that will tip this trajectory one way or another. Several tools exist. The obligation to offer a basic non-connected offering, with mutualized pricing, for any insurer offering a connected offering. Algorithmic transparency rules that require insurers to explain the criteria used in pricing and offer a right to contest. Public investments or public-private partnerships to expand access to connected devices in under-equipped areas.
Several Latin American countries have undertaken reforms in insurtech supervision. Brazil, via SUSEP (Superintendência de Seguros Privados), has established a regulatory sandbox that allows insurance startups to test innovative products within a controlled framework. Colombia and Mexico have similar initiatives. These mechanisms are useful for innovation, but they do not address the equity question: a sandbox promotes experimentation, it does not guarantee that experiences emerging from it cover the most exposed populations.
The challenge by 2030 is whether predictive insurance will remain a service reserved for the most solvent and best-equipped profiles, or whether regulatory and commercial architectures succeed in extending its benefits. The answer depends less on technology than on the political choices that frame it, which moreover applies to most innovations with strong discrimination potential, as debates on measuring AI productivity gains underline.
Insurers betting on inclusion also have commercial reasons to do so
One final dimension deserves attention: the enlightened self-interest of insurers themselves. Extreme segmentation produces narrow markets. An insurer that skims the least risky profiles cuts itself off from an immense, underinsured population that represents a considerable potential market if one knows how to offer adapted products to it.
Insurers who have understood this work on reducing the cost of distribution and claims management through technology, to make low premiums economically viable on mass markets. Subscription and settlement automation, simple mobile interfaces, partnerships with nontraditional distribution networks—groceries, gas stations, telecommunications operators—are part of these strategies.
The question is therefore not one of choosing between technology and equity. It is one of constructing the conditions under which technology serves both. This requires regulators capable of anticipating the effects of algorithmic segmentation, insurers ready to think about their market long-term rather than optimize their portfolios short-term, and governments that invest in digital infrastructure without which connected insurance remains, by construction, reserved for those who need it most.
Sources
- Capgemini (2025). Top Trends 2025 – Property and Casualty Insurance. https://www.capgemini.com/fr-fr/perspectives/publications/top-trends-2025-assurance-iard/
- France Assureurs (2025). 2025 Prospective Mapping. France Assureurs (no verified link available).
- Inter-American Insurance Federation (FIDES). Insurance Penetration Statistics in Latin America. FIDES (no verified link available).
- SUSEP – Superintendência de Seguros Privados (Brazil). Insurance Regulatory Sandbox. SUSEP (no verified link available).