Differential Pricing in Insurance: Opportunity, Backlash, and the Future of Selective Underwriting

Differential pricing in insurance, the practice of charging different customers different premiums for equivalent coverage, is not new. For decades, insurers have relied on underwriting, geography, and demographics to segment risk. What has changed is the precision and speed enabled by telematics, embedded insurance, and AI-driven underwriting. These tools allow newcomers and incumbents alike to price risk with unprecedented granularity.

The commercial potential is obvious: selectively targeting low-risk, high-margin customers can rapidly improve profitability. But the societal and strategic consequences are more complex. Left unchecked, differential pricing weakens the solidarity mechanisms that make insurance sustainable and politically viable in the long term.

A Brief History of Differential Pricing

Although today’s debates focus on data-driven underwriting, differential pricing has long been part of the industry. Early examples include the use of postcodes and occupations in motor insurance, which allowed insurers to distinguish between groups of customers based on proxies for underlying risk. In some markets, carriers also experimented with “price optimisation”, adjusting renewal premiums based not on claims history but on a customer’s likelihood of switching.

International practice has varied. In the United States and Australia, regulators have traditionally placed fewer limits on underwriting, enabling sharp premium differences between customers with otherwise similar profiles. By contrast, European markets, shaped by post-war welfare traditions, leaned more heavily on solidarity, regulating to prevent excessive segmentation.

This divergence is crucial. In Europe, the rise of advanced differential pricing feels like a direct challenge to long-standing assumptions that insurance is both an economic and a social good.

The Mechanics of Differential Pricing

Modern differential pricing is made possible by three reinforcing forces. First, alternative data sources such as telematics, wearables, transaction data, and even digital footprints provide granular insights that go beyond traditional actuarial models. Second, new distribution channels (price comparison websites), digital brokers, and embedded partners, allow insurers to selectively target profitable segments at scale. Finally, AI and advanced modelling techniques process thousands of variables in real time, enabling underwriting precision that would have been unthinkable a decade ago.

Together, these forces create a market environment where lean players (digital MGAs or neo-insurers) can compete without taking on the burden of cross-subsidised risk pools.

Opportunities for Insurers

For insurers, differential pricing can unlock tangible advantages. The most immediate is profit uplift, as premiums are brought closer to actual loss cost for historically overpriced segments. It also provides competitive differentiation, particularly in commoditised markets such as motor, pet, or travel insurance, where customers often make purchasing decisions on price alone. A further benefit lies in the learning effects: data gathered from selective portfolios can inform product design, claims prevention measures, and long-term underwriting strategies. Some incumbents are already experimenting through flanker brands and digital-only propositions, which allow them to compete tactically while protecting their core brand position.

Risks and Criticism

The drawbacks of differential pricing are equally significant. Regulatory challenges are mounting, with the UK’s Financial Conduct Authority banning “price walking” in 2022 after finding that long-tenured customers were being charged systematically more than new ones. Fairness is another flashpoint: while the EU’s Test-Achats ruling removed gender as a pricing factor, newer debates now focus on the ethical use of socio-economic profiling, credit scoring, and health-related data. Perhaps the most worrying consequence is systemic destabilisation. As new entrants skim the profitable risks, incumbents are left with higher-risk pools, which in turn forces premiums up, drives customer churn, and creates a vicious cycle of declining affordability and profitability.

In practice, these risks are inseparable from the broader issue of solidarity in insurance. The erosion of cross-subsidies often begins with differential pricing and selective underwriting, where new entrants target low-risk segments and leave incumbents with a weakened pool. We explore this mechanism in more depth in our article on [insurance cross-subsidies], including case examples of how solidarity breaks down across European markets.

Market Case Studies

UK Motor – Telematics AdoptionEarly telematics insurers in the UK targeted safe, low-mileage drivers who had long overpaid relative to their risk. Their migration destabilised traditional portfolios, forcing price hikes on the remaining book and triggering a profitability spiral. The lesson: selective adoption can destabilise entire pools if incumbents fail to respond with defensive pricing or product innovation.

Dutch Health – Risk Equalisation ChallengesThe Netherlands’ health system compensates insurers that attract higher-risk customers through a risk equalisation mechanism. Even so, some insurers selectively appealed to healthier groups, such as students or sports clubs. This required repeated formula adjustments and drew political scrutiny, underscoring how difficult it is to contain selective underwriting once it gains momentum.

AI and Emerging Technologies in Pricing

If history shaped the foundations of differential pricing, technology is now driving it forward. Artificial intelligence, machine learning, and alternative data streams are enabling segmentation with a level of granularity that even the most sophisticated actuarial models could not previously achieve.

Dynamic repricing is one clear example. AI systems can adjust premiums in near real time based on driving behaviour, payment patterns, or purchasing activity. While this creates commercial opportunity, it also raises new risks: opaque algorithms, potential bias, and challenges in customer communication. Regulators are already responding. The EU's new landmark AI Act legislation imposes stringent new obligations on "high-risk" AI systems, a category that critically includes AI used for risk assessment and pricing in life and health insurance. For leadership, this creates a strategic imperative to ensure all relevant AI applications meet rigorous new standards for fairness, transparency, and human oversight to mitigate risk and maintain market access within the EU.

The rise of wearable devices and IoT data feeds adds another layer. From fitness trackers to smart homes, insurers can now access continuous behavioural data. While this can sharpen pricing, it risks fragmenting risk pools so extensively that solidarity mechanisms, such as state-backed reinsurance schemes, become the only way to protect vulnerable groups.

For incumbents, the challenge is to embrace AI-driven pricing while maintaining trust and transparency. Those who invest early in governance and explainability will be best positioned to influence the rules of engagement.

Regulatory and Public Responses

Across Europe, regulators are balancing innovation with fairness. In the UK, the FCA has acted decisively by banning price walking in general insurance. At the EU level, non-discrimination principles have been reinforced, while newer debates focus on fairness in algorithmic underwriting and the ethical use of personal data. Meanwhile, markets such as Germany and France rely on hybrid frameworks from basic tariffs in health insurance to state-backed agricultural reinsurance. These help preserve solidarity while leaving room for competition at higher levels of coverage.

Possible Futures for Differential Pricing

The trajectory of differential pricing will depend on how insurers, regulators, and customers respond over the coming decade. Three broad scenarios can help leaders frame their strategic options:

1. Race to the BottomIn this scenario, competition intensifies through PCWs, embedded channels, and AI-powered repricing. Profit margins compress as insurers aggressively segment and chase low-risk customers. Regulators intervene only reactively, allowing the market to tip toward commoditisation before stepping in. The outcome is a landscape of thinner margins, higher volatility, and frequent new entrants.

2. Hybrid EquilibriumHere, segmentation is allowed but carefully balanced by solidarity mechanisms. Regulators set guardrails to ensure access and affordability, using tools such as state-backed reinsurance, basic tariffs, or risk equalisation pools. Insurers compete on innovation and customer experience in less sensitive lines, while politically exposed segments remain more regulated. This path creates a sustainable balance between competition and stability.

3. Data Arms RaceIn the most disruptive scenario, AI-driven underwriting dominates. Hyper-segmentation fragments risk pools to the point where solidarity mechanisms collapse outside of government-backed pools. Access to affordable cover becomes more dependent on public intervention, while insurers compete mainly on analytics, data partnerships, and speed of repricing. For incumbents, survival hinges on technological scale and governance credibility.

Looking Ahead: Segmentation Meets Solidarity

AI will intensify the ability to segment risks with precision. At the same time, ESG pressures and political scrutiny may push regulators to reinforce solidarity principles. The future will likely be hybrid, where segmentation and solidarity coexist under carefully designed rules. Flood Re in the UK and France’s agricultural reinsurance schemes are evidence that this balance is possible, but not automatic.

For incumbents, the challenge is not whether to embrace differential pricing but how to do so without eroding the legitimacy of insurance itself. Those who monitor market shifts, build flexible pricing capabilities, and make their philosophical stance clear will be better placed to set the rules of engagement, rather than simply reacting to them.

For a deeper dive into insurance solidarity and cross subsidies read our companion article “The End of Insurance Solidarity? How Incumbents Can Respond to the Erosion of Cross-Subsidies".