Executives in every industry grapple with fragmented information streams that obscure the full picture of customers, competitors, vendors, and risks.

They want pictures of places with perils, proximity, price, sanctions, anti-corruption, regulation, crime, and compliance. Those map the playing field.

They need profiles of players on that field. Best, worst, and next customer, competitor, consortium, and criminals. Active, passive, and latent friction on the field of business and nature.

What are the risks they need to manage themselves? Which risks can be transferred with insurance? How do things change over time? When and why should they adopt new tactics?

Nobody builds or operates anything without risk management and insurance.

Some risks are well-enough understood that there are formulas on maps with data that explain:

• rate to risk – distance to coast, nearest fire line, closest body of water, feet to fire hydrant
• rules of risk – sovereign borders, zoning, taxation boundaries, legislative hellholes, politics
• range of risk – proximity to population, nearness of combustible materials, crime indexes

Some risks are still being grappled with:

• "invisible" risks – the internet is not a place, criminals lie, organized criminals lie better
• "known unknowns" risk – pandemic, war, supply chain, cyber, lawsuits, climate, tech
• "emerging" risks – aging infrastructure, connectivity, AI, casualty CATs, land use

Entity resolution and digital entity resolution are two key dimensions where massive progress is being made in transforming our understanding of the players on the field as well as navigating current and foreseeable changes in the field of play itself.

Adding entity data with geo-digital entity attributes is now a new avenue for putting risk on the map.

Are my neighbors my adversaries?

"See the battlefield, know your enemy" is a strategic imperative in conflict – some see that business competition, combating criminals, and complying with rules, regulations, and laws as necessary conflict, akin to war. Sun Tzu's "The Art of War" makes compelling sense in creating an awareness about your own situation and a diligence in understanding others with whom you interact or that are in your environment.

Tactically, assigning ultimate owner entity resolution to all the places and resources on your business battlefield makes a frustrating legacy of poor data a daily problem. Strategically, you can add more sustainability and resilience to your business by improving your knowledge of "brick and mortar, with click and order" data and clearly mapping your known trusted customers and partners as well as those you do not trust. Then everything else is open for business, with trust unknown, yet not unknowable.

Blending hyper-local with hyper-linkable on a map

The risks you can see and those you can only know as relatable can be illustrated visually on a map now.

Visible overlap of the world and the e-world can take a picture where two worlds interact - physical names and addresses and internet names and addresses pool into entities and relationships. Perils and problems in either or both can create business risk, but a peril on the internet might manifest at multiple physical locations. A duality of risk with asymmetric shapes.

You can play these visuals forensically and in a forward-looking fashion to understand risks, supply chains and single points of failure to improve your sustainability and resiliency. It's a new frontier of both understanding risk as well as a new entrée for Predict & Prevent initiatives.

Tracking relatedness on a map when water is rising, the wind is blowing, a freeze is coming, the earth is shaking, or when a fire is raging are traditional processes now. But today's risks now include more layers of risk that extend to digital assets and brand reputation as well as cyber exposures and regulatory and compliance requirements tied to knowing your business, your customers, your vendors, and your physical/digital/legal/cyber ecosystem.

When is a bunch of dots on a map really a single organization with legitimate purpose? If you don't tie them together appropriately, then you create aggregation and accumulation risk.

When are those dots nefarious sanctioned shells, all being operated in shadowy collusion? If you don't find these accurately, then you are dealing with the wrong customers.

Only entity resolution can help you sort it and keep it sorted.

A company in New York running on servers in North Korea owned by companies controlled by criminal cartels in sanctioned and unsanctioned countries is different than a legitimate NYC business entity. The same for Frankfurt, London, Quebec, Sao Paulo, Mexico City, or any hub.

Knowing what's behind the dots on a map matters.

Entity resolution and digital domain entity mapping emerge as pivotal technologies, bridging disparate data points to reveal actionable insights.

From unmasking fraudsters and untrustworthy entities, we can now blend data and view them in maps and graph analytics like never before. These connected and resolved entities can show what is otherwise hidden – how "click&order" meets "brick&mortar" – and then relates these to maps and graphs that bring entity resolution data and GIS tools together as new ways for reshaping how businesses operate. In some regards, a GIS coordinate or polygon is the same as a street address in creating a unique identifying reference. In other regards it may be even better.

Classic geospatial information systems are mashing up with federated streams of disparate identities getting resolved with industrial grade entity resolution engines on names and addresses from the real world and modernized digital entity names and addresses from the e-world.

Unraveling Entities: The Foundation of Clarity

Entity resolution, at its core, is the art and science of identifying when different records refer to the same real-world entity, despite variations in naming, addresses, or other attributes. Think of it as a digital detective work: matching "Acme Corp." in one database with "Acme Industries" in another, accounting for typos, abbreviations, or mergers. This process relies on advanced algorithms, machine learning, and sometimes geospatial data to link entities across sources like customer databases, transaction logs, and public records.

In the business world, poor entity resolution leads to potentially costly blind spots—duplicate customer entries inflating marketing budgets or missed connections in supply chains. But when done right, it creates a unified view, often called a "Customer 360," enabling personalized experiences and efficient operations. Financial institutions, for instance, use it to consolidate profiles from multiple accounts, spotting patterns that standalone data might overlook.

Business leaders face a perennial challenge: How do you connect the dots in a sea of disconnected data? Consider a scenario where a financial institution spots unusual web traffic patterns on its site. Is it a legitimate corporate inquiry or a sophisticated fraud attempt? Or imagine a real estate firm assessing a commercial property—does the tenant's online activity signal stability or hidden vulnerabilities? These questions underscore the power of entity resolution and digital domain mapping, two opportunistically intertwining techniques that transform raw data into strategic advantage.

Mapping the Digital Footprint: From IP to Insight

Companies are complementing entity resolution with digital domain mapping, particularly in the practice of tracing web traffic back to specific companies through reverse IP lookups. When a visitor lands on your site, their IP address can be cross-referenced against databases of corporate networks, revealing not just location of the domain server, but also the operating organizational identity - using B2B signals to understand transactional behavior.

Tools like reverse IP tracking turn anonymous visits into named prospects, enriching CRM systems with firmographic data such as company size, industry, and revenue. When integrated with entity resolution, it resolves ambiguities—ensuring that traffic links correctly to the parent corporation, even if subsidiaries are involved.

Key Use Cases: Where Resolution Meets Reality

The true value shines in practical applications. Here are a few ways businesses are leveraging these technologies to drive decisions and mitigate risks.

Fraud Detection: Spotting the Anomalies

In fraud prevention, entity resolution and digital domain mapping form a dynamic duo. Banks analyze transaction data alongside web traffic to detect mismatches—say, a login from an IP tied to a known risky entity, or duplicate profiles attempting wire transfers. For example, if multiple accounts share an email but originate from disparate company IP addresses, it could flag account opening fraud. Anti-money laundering (AML) teams use this to uncover hidden networks, reducing false positives and accelerating investigations. Real-time resolution cuts fraud losses by identifying suspicious patterns across channels more accurately and faster than other means.

Property Due Diligence: Assessing Digital Vitality

For real estate investors and developers, due diligence extends beyond physical inspections. Entity resolution helps verify tenant identities by linking lease records to corporate filings, while digital domain mapping evaluates a company's web traffic footprint. High traffic from reputable IP addresses might indicate a thriving business, boosting property value; conversely, erratic patterns and patterns with "bad actors" could signal instability. In M&A contexts, this combo accelerates reviews, slashing due diligence time from weeks to hours by automating entity matches and traffic analysis. OSINT techniques further enhance this, pulling in public web data for comprehensive risk profiles.

Marketing and Lead Generation: Targeting with Precision

B2B marketers thrive on digital domain mapping to identify anonymous site visitors as potential leads. By resolving these entities, teams may personalize content—serving tailored ads or emails to decision-makers at visiting companies. Account-based marketing (ABM) benefits immensely as well, prioritizing high-value prospects based on traffic intent.

Charting the Future: Integration and Innovation

As data volumes explode, entity resolution and domain mapping will evolve with AI, incorporating real-time geospatial layers for even richer insights—think mapping traffic to physical locations for everything from trusting a transaction to supply chain optimization. Executives invest in resolving uncertainties while positioning their organizations for what's next – the unknown to the knowable.

The global insurance market is used to discussing war risks in terms of coverage, limits, and pricing.

In Ukraine, war risk is no longer a theoretical construct or a niche extension of property insurance. It is a daily operational reality.

Over the past few years, Ukrainian insurers have gone through a learning curve that most markets only explore through stress tests or academic scenarios. This experience is not about heroism or communication. It is about how claims are actually handled when war becomes a physical risk environment.

When PVI stops being theoretical

In stable jurisdictions, political violence insurance is typically perceived as:

• an add-on to property coverage,
• a tool for large infrastructure or cross-border projects,
• a low-frequency, high-severity product.

In Ukraine, this logic no longer holds.

War risks here:

• materialize with high frequency
• take multiple forms — from direct hits to secondary damage,
• overlap with active production, energy, and logistics processes.

As a result, the key question is no longer whether war risks can be insured, but whether insurers are operationally capable of settling such claims in a controlled and professional manner.

Claims ≠ payment

One of the most common misconceptions outside Ukraine is the idea that war risk claims follow a linear process:

incident → report → payment.

In reality, war-related losses are rarely simple.

Assets affected by attacks — power plants, manufacturing facilities, logistics hubs — have multi-layered technical structures, including:

• core equipment,
• auxiliary systems,
• cable networks,
• control and monitoring systems,
• infrastructure elements with indirect or secondary damage.

Each component requires separate technical assessment, and standard claims-handling templates are largely ineffective.

In practice, war risk claims become engineering-driven analytical projects, not administrative exercises.

The limits issue: why "EUR 250,000" or "UAH 10 million" is not underinsurance

A frequent question from international partners is why war risk limits in Ukraine often appear modest.

The answer lies in reinsurance availability and affordability.

After every major attack, insurers receive a surge of requests from corporate clients. International reinsurers — including the Lloyd's market — are formally willing to quote. In practice:

• quotes are valid for hours or days,
• pricing can reach 10–15%,
• terms fluctuate significantly depending on the phase of the conflict.

Under such conditions, full risk transfer frequently becomes economically unviable for insureds.

As a result, Ukrainian insurers have developed an alternative model — providing war risk coverage backed by their own capital, within limits that are financially sustainable.

This is not a compromise.

It is pragmatic capital risk management.

Speed versus accuracy

Another underestimated dimension is claims settlement timing.

War risk claims require a delicate balance:

• excessive speed increases the risk of technical or legal errors,
• excessive delay jeopardizes business continuity for insureds.

In the Ukrainian context, 30–40 days from incident to payment is not slow. It reflects:

• comprehensive documentation,
• multi-level technical expertise,
• decision-making under non-standard operational conditions.

This balance is difficult to model theoretically but emerges through practice.

The human dimension of claims handling

An often-overlooked element of war risk claims is the human factor.

Claims teams operate:

• on physically damaged sites,
• in constant interaction with clients facing business disruption or loss of critical infrastructure,
• under intense responsibility for accuracy, timing, and capital impact.

In such conditions, policy wording alone is insufficient.

Effective claims handling requires the ability to combine technical expertise, expectation management, and professional restraint.

This dimension is largely absent from traditional claims-handling frameworks in peaceful markets.

What global markets still underestimate

The core lesson from Ukraine is uncomfortable but clear:

War risk is not a standalone insurance line.

It is a systemic stress test for underwriting, capital adequacy, claims handling, and human management.

Ukrainian insurers are currently accumulating experience that:

• cannot be fully replicated through simulations,
• is not captured in standard methodologies,
• will, unfortunately, become relevant for other markets sooner or later.

Ideally, such experience would never be needed.

But since it exists, it deserves to be discussed professionally and without illusion.

With the new year in full swing, property and casualty insurers are navigating a period of considerable uncertainty. Customer expectations are evolving, risk patterns are becoming more volatile, and human-AI collaboration is reshaping decision-making. In 2026, the distance between strategic intent and operational execution will separate those gaining ground from those losing it.

While insurance executives recognize that modern core systems and advanced technology are essential for transformation, the industry still lags and faces mounting pressure to meet broker and policyholder expectations. Too often, limited technological maturity holds progress back. Personalization won't scale without stronger data foundations. AI will create more technical debt unless workflows evolve at the same time. And climate risk modeling will fall short without dynamic underwriting. 

Here are four trends every insurer should watch – and act on – in the year ahead.

1. From static to dynamic: how evolving consumer expectations are redefining insurance

Today's consumer will not accept an inferior experience. Across insurance, our research shows that two in three (63%) policyholders are willing to share data to receive personalized policy recommendations and premium discounts. Insurers recognize this: while 87% of industry executives acknowledge that their customers expect personalized experiences, only 54% report readiness to deliver at scale. The gap isn't customer willingness or awareness; it comes down to execution.

Insurers that design products around usage‑based coverage, dynamic pricing, telematics, and embedded distribution – rather than static, one‑size‑fits‑all models – are increasingly capturing outsized growth and staying ahead.

2. The data foundation: why personalization stalls without infrastructure

Personalization requires AI-ready data infrastructure, not just integration. 70% of insurers say data fragmentation and quality challenges limit their ability to derive actionable insights. The data sources relied on can be siloed and disconnected, with different practices for underwriting or claims, and quality standards attached. This creates inconsistent results and conflicting interpretations. Legacy systems become a bottleneck holding back progress. Without this foundation, personalization remains more promise than practice.

When done right – prioritizing data quality, integration and democratization of data with real-time capabilities – the payoff is real. Carriers report improved retention rates when they integrate customer service insights across functions. Forward-looking insurers are already translating data-driven strategies into real-world solutions, setting new benchmarks for personalization across the industry.

As an example, Nationwide introduced a discount scheme for its policyholders, linking safe driving behavior in its usage-based insurance program to unlock personalized savings on homeowners' insurance.

3. Scaling AI: translating enterprise intelligence into customer outcomes

With data infrastructure in place, AI amplifies personalization at scale. But here's the paradox: insurers want to deploy AI agents across operations and increase their generative AI investments, yet most pilots stay pilots. Across large enterprises, AI gets stuck in individual functions, creating technical debt and inconsistent operational models rather than transformation. 2026 will start to reveal which insurers moved from proof-of-concept to proof-of-impact. This means ensuring AI delivers measurable outcomes, builds trust, and enables collaboration at scale.

The harder half isn't technology deployment – it's workflow redesign. You can't bolt AI onto legacy processes and expect enterprise-wide transformation. While 96% of financial services leaders cite regulatory and compliance concerns, that caution often masks a deeper challenge: redesigning how work actually happens.

Real impact emerges when AI is embedded across processes, systems, and operating models – turning data infrastructure into tangible customer outcomes. The insurers reporting improved underwriting outcomes through advanced capabilities didn't just invest in technology; they used these capabilities to address critical underwriting gaps while optimizing exposure management.

4. From reactive to resilient: why climate risk will demand a new insurance playbook

As personalization and AI reshape customer engagement, climate risk is exposing the limits of historical data models. They struggle to capture the rising severity of secondary perils and the growing exposure from population shifts into high-risk zones. According to the Gallagher Re 2024 Natural Catastrophe and Climate Report, severe convective storms accounted for 41% of global insured catastrophe losses. Capgemini data also reveals 70% of the global population is expected to reside in urban centers by 2050, amplifying exposure in vulnerable regions. Regulatory mandates are equally tightening with scenario analysis, climate risk disclosures, and capital adequacy norms now becoming table stakes.

These paradigm shifts demand a move from reactive to resilient risk modelling. By consolidating high-resolution hazard mapping, real-time climate data, and predictive analytics into underwriting platforms, insurers can improve pricing accuracy and strengthen capital adequacy against secondary peril losses.

One global insurer centralized its property data from multiple sources to enable dynamic risk scoring and portfolio-level exposure analysis. As a result, they identified concentration risk across portfolios by specific perils, uncovering multimillion-dollar missed limits. That's resilience in action: insight that changes pricing, accumulation, and capital decisions before the next event.

The road ahead

Taken together, these four trends signal a defining imperative for the property and casualty insurance industry in 2026. This is no longer about incremental improvements. It's about embedding intelligence, agility, and foresight into every layer of the enterprise that turns disruption into opportunity.

You can't scan a business headline, listen to an earnings call, or sit in a leadership meeting today without hearing the term: "elastic staffing."

Once considered a niche workforce strategy, elastic staffing has quickly become a defining operating model for organizations navigating sustained uncertainty. At its core, the concept is straightforward: maintain a strong internal team, then flex capacity up or down by tapping pre-qualified external talent as business needs change.

What's driving its rise isn't novelty—it's pressure. Persistent talent shortages, accelerating retirements, cost volatility, rapid digital transformation, and unpredictable demand have exposed the limitations of rigid, fixed-headcount staffing models across nearly every industry. Nowhere is this shift more apparent than in insurance.

Why Traditional Staffing No Longer Fits Insurance

The insurance workforce model was built for a very different era—one defined by predictable workloads, long employee tenures, and incremental change. For decades, insurance organizations could rely on steady volumes and institutional knowledge accumulated over entire careers. That reality no longer exists.

Today's carriers, MGAs, MGUs, and brokerages are operating in an environment marked by volatile claims activity, rapid technology adoption, tightening margins, heightened regulatory scrutiny, and shrinking talent pipelines. Demand now comes in peaks and valleys rather than steady, forecastable patterns. At the same time, the industry is grappling with a wave of retirements that is steadily draining deep institutional expertise.

Fixed staffing forces leadership into a constant balancing act. Overstaff to prepare for demand surges and organizations absorb unnecessary cost when volumes normalize. Understaff and they risk backlogs, service breakdowns, compliance exposure, and employee burnout when activity spikes. Neither option is sustainable, and both create long-term organizational drag.

As Carrier Management has observed, insurers need workforce models that can "expand, contract, reorganize and modernize quickly without compromising culture or compliance, or creating a disjointed customer experience." That requirement alone challenges the viability of traditional, static staffing approaches.

Elastic staffing addresses this gap by turning labor into a variable resource rather than a fixed constraint. It allows organizations to scale specialized expertise—claims, underwriting, accounting, compliance, account management—without committing to permanent headcount that may not align with future demand. Just as importantly, it provides a practical way to adapt as automation and AI reshape roles, without relying on layoffs as the primary lever.

But flexibility alone is not the answer.

Why Elastic Staffing Can Fail in Practice

Many insurance organizations struggle with elastic staffing because they approach it as a short-term fix or a substitute for temporary labor. When elasticity is treated as a transactional solution, it often becomes reactive rather than strategic—and can introduce new forms of risk.

To succeed at scale, elastic staffing must rest on three critical foundations:

• Access to experienced, insurance-specific talent
• Operational consistency and accountability
• Alignment with how insurance work is actually performed

Without these, organizations may gain short-term capacity but sacrifice quality, continuity, and control.

Insurance is not an industry where generalist labor can be dropped in without consequence. Regulatory requirements, system complexity, and client expectations demand professionals who understand the nuances of the work. Elastic staffing models that overlook this reality often struggle with long ramp-up times, rework, and execution errors.

The Role of Specialized Workforce Models

Elastic staffing works best when it is built around talent pools that are purpose-built for insurance, rather than broad labor marketplaces.

The most effective models share several defining characteristics.

• Immediate access to experienced professionals: Elastic staffing only delivers value if talent can contribute quickly. In insurance, experience is not optional—particularly in high-stakes functions such as underwriting, claims, finance, compliance, and client service. Deep domain knowledge shortens ramp-up time, reduces errors, and minimizes operational risk.
• Flexibility without constant turnover: Elastic does not have to mean short-lived engagements. In fact, longer-term, embedded professionals often deliver the greatest value. This approach allows organizations to flex capacity for peak workloads, remediation efforts, or transformation initiatives while maintaining continuity, institutional knowledge, and service quality.
• Reduced execution risk: Moving from a fixed headcount model to an elastic one introduces new challenges around onboarding, security, performance management, and accountability. Workforce models that already address these operational realities allow organizations to adopt flexibility without redesigning internal processes or overburdening HR and operations teams.
• Smarter cost control: Elastic staffing converts labor from a fixed expense into a variable one while preserving access to senior-level expertise. When recruiting costs, benefits, turnover, and downtime are considered, this approach often proves more cost-effective than traditional hiring—particularly for roles tied to fluctuating demand or specialized projects.
• Support for long-term workforce resilience: As retirements accelerate and talent pipelines thin, insurance organizations need solutions that balance immediate capacity needs with long-term continuity. Elastic staffing provides that balance, stabilizing operations today while creating space to rethink workforce design for the future.

Elasticity as a Strategic Operating Model

Organizations that succeed with elastic staffing do not treat it as a stopgap. They treat it as an operating model.

That means planning for flexibility, building repeatable processes, and aligning workforce strategy with business volatility and technological change. It also means recognizing that elasticity is not about replacing internal teams, but about augmenting them in ways that preserve culture, compliance, and customer experience.

In an industry where regulatory exposure and trust are always at stake, execution matters as much as intent. When implemented thoughtfully, elastic staffing enhances resilience. It enables organizations to absorb demand shocks, manage transformation initiatives, and adapt to AI-driven change without destabilizing their workforce or compromising service quality.

The industry is already moving in this direction. As uncertainty becomes the norm rather than the exception, workforce agility is no longer a competitive advantage—it is a requirement.

The elastic wave is here. How insurance organizations ride it will determine who keeps pace—and who falls behind.

For many in the insurance industry, 2025 was the year of the "AI Reality Check." After a whirlwind of excitement surrounding generative models, many carriers found themselves navigating a landscape cluttered with broken promises and stalled pilots. As we look toward meaningful innovation in 2026, the path forward requires us to address the "key myth" of AI: the seductive, yet ultimately destructive, belief in the end-to-end magic pill.

Believing that AI can or should replace human judgment at scale is disconnected from the reality of what the technology is. It's far more nuanced and, ultimately, more valuable. AI excels at specific, well-defined tasks: parsing documents, extracting structured data, identifying patterns in large datasets. Humans excel at everything else: understanding context, applying judgment, managing relationships, and making decisions that balance competing priorities.

AI in insurance isn't about doing it all at once. It's about deploying AI module by module, connecting thoughtfully, and staying grounded in what the technology can and cannot do today. That's how AI moves from hype to durable business value.

This distinction matters enormously, especially in insurance, an industry that has been swept up in the promise of AI-powered transformation. Over the past few years, insurance companies have invested heavily in "end-to-end AI systems," ambitious platforms that promise to automate entire workflows, from document intake through underwriting decisions to claims processing. The pitch is compelling: let AI handle the complexity so your teams can focus on strategy. The reality, however, tells a very different story.

The Gap Between Hype and Production

The most significant barrier to durable business value has been the industry's obsession with "end-to-end" solutions. We have seen insurers attempt to buy "AI underwriters" with the expectation that the model will handle everything from initial intake and actuarial analysis to final premium pricing.

There's significant noise around concepts like "AGI" (artificial general intelligence) which creates unrealistic expectations about what AI can accomplish today. This prevailing narrative obscures a critical truth: we're nowhere near the kind of AI that can independently manage the nuanced, multifaceted work that insurance professionals do every day.

An AI cannot replicate 20 years of an underwriter's experience or possess the nuanced context of a specific account. When these "do-it-all" systems attempt to underwrite a complex entity like a national car rental fleet, they often produce inaccurate results because they lack the human context to understand the specific distribution of vehicle types or local risk factors.

When these end-to-end systems fail to deliver, adoption plummets, and frustrated teams retreat to their old manual ways of doing things. This is a failure of strategy, not technology. The myth that AI can do it all has led many to overlook the "hidden costs of delay"—the thousands of touchpoints where humans are forced to review the same long documents and messy email threads over and over again.

This observation cuts to the heart of the key myth that has driven billions in insurance AI spending: the belief that you can build a single system to handle everything.

The Human Touch

Another critical truth? People want to know there is a human hand guiding the decision-making, particularly in an industry as important as insurance. Insurity's 2025 AI in Insurance Report revealed that just 20% of Americans say it's a good idea for P&C insurers to leverage AI, and 44% of consumers are less likely to purchase a policy from an insurer that publicly uses AI. In a 2025 Guidewire survey, 40% of respondents said they would feel more confident in insurers' AI if decisions could always be referred to a human when challenged. Finally, a 2025 survey conducted by J.D. Power showed that insurance customers are most comfortable with AI when it is used to automate routine aspects such as sending claim status updates (24%), managing their billing (23%), and answering basic customer service questions (21%).

So what insight can we gain from these numbers? People are more wary of the insurance industry's use of AI when there isn't a human available to speak with or in control of ultimate decision-making. It seems that customers are far more comfortable with insurers using AI in their workflows when it is deployed for automatic, manual processes embedded with human oversight.

The Failure of End-to-End Automation

Many insurers bought AI underwriting or claims products with high expectations. These systems promised to intake documents, evaluate risk, and generate underwriting decisions and pricing. It seemed the entire underwriting process would be fully automated. What happened next was instructive.

In one recent example, a large insurer deployed an "end-to-end" AI system to handle renewal underwriting for a major account. The AI evaluated the client's profile and recommended a specific premium. But when the human underwriter, who had managed that account for years, reviewed the recommendation, the flaws became obvious. The AI had missed critical nuances about the client's composition and risk profile. The underwriter knew from years of professional experience that this contextual information fundamentally changed the risk calculation. The AI system had the same information as the human underwriter, but the AI's recommendation was simply wrong.

The outcome was predictable: the insurer stopped using the system and went back to manual underwriting. With one major near-miss, "people just go back to the old way of doing things," the expert said.

This represents a profound failure in the AI industry. After this experience, the underwriter noted "It's better to do it manually than to use an AI. Something seriously has gone wrong here."

The Real Innovation: Modular AI

If end-to-end systems fail, what actually works? The answer lies in a fundamentally different approach: "modular AI deployment." Rather than trying to automate entire processes, successful organizations break complex workflows into smaller, well-defined components and apply AI where it genuinely adds value.

Instead of attempting to automate every aspect of a human's job, AI initiatives should focus on eliminating one extremely tedious and time-consuming task.

This philosophy is particularly powerful in document-heavy operations like insurance. Rather than developing an AI that promises to fully contextualize an underwriting submission and make complex recommendations, a more effective strategy is to concentrate on a single, crucial pain point such as accurately extracting and classifying documents. This is a genuinely difficult challenge. Insurance submissions often contain mixed document types, irrelevant supplemental data, and complex tables that general-purpose AI models frequently fail to process correctly because they are not designed to do so.

This is precisely where focused AI adds clear, measurable value. Once documents are properly classified and key data is converted into structured formats, human underwriters operate with far greater efficiency. Their time is spent reviewing pre-processed data and applying their judgment, experience, and understanding of company-specific risk appetite, not manually hunting through dozens of PDFs for critical information.

Building Digital Transformation Through Integration

The path to meaningful AI advancement in insurance isn't about finding the perfect all-knowing system. It's about thoughtful integration of specialized components to increase efficiency and letting professionals get back to the real work at hand. Organizations should consider which capabilities to buy (like document extraction), which to build internally (like risk models specific to your business), and how to orchestrate them effectively.

This is building AI one small piece at a time. You might deploy document classification as a module. Then add information extraction. Then integrate those outputs into your downstream systems. Each step is validated, each component is understood, and each addition genuinely improves the workflow for the humans who ultimately make the decisions. No "end-to-end" black box AI.

Admittedly, this approach requires discipline and is less exciting than the promise of end-to-end automation. But it actually works and leads to full adoption, rather than initial experimentation and inevitable abandonment when reality fails to match the pitch.

Since 2019, several casualty lines of business have shown a consistent pattern of adverse development through year-end 2024. Preliminary third-quarter 2025 disclosures signal that this trend is not yet reversing. The underlying experience, however, differs significantly by line and by carrier. This article focuses on where and why reserve shortfalls are occurring. We also provide some high-level suggestions to adjust actuarial methods for more adequate reserves.

Using Annual Statement data from S&P Global Market Intelligence, we examined:

• Commercial auto liability (industrywide), and
• Other liability—occurrence experience for 20 writers concentrating on excess or umbrella coverage.

Across accident years 2016–2024, published ultimate loss ratios have increased almost every calendar year. With the benefit of hindsight, initial and subsequent reserves were inadequate.

From Annual Statement data via S&P Global Market Intelligence Industry Commercial Auto Liability

From Annual Statement data via S&P Global Market Intelligence based on Other Liability Occurrence results from 20 companies that predominately write excess and umbrella business.

What is driving this pattern of inadequate reserves? We believe that the following factors are the most significant:

1) Extended Litigation: The expansion of third-party litigation funding and the improved capitalization of certain plaintiff firms mean more lawsuits proceed to trial. This causes challenges with traditional actuarial methods. Actuaries often use the past patterns to predict future patterns; however, if the environment changes significantly the methods become less reliable. With an increasing percentage of claims being litigated, historical loss emergence patterns are less reliably predictive of the future patterns. The industry has observed both longer cycle times (from claim report to claim settlement) due to more litigation and increased settlement costs as jury outcomes increasingly favor plaintiffs.

2) Backward-looking benchmarks: Actuaries often use older years' loss ratios to estimate loss ratio results for more recent years (after adjusting for premium changes and loss trends). However, if the older years' loss ratios consistently increase, the initial assumptions for the newer years start too low.

3) Under-estimated trend in a rising-cost environment: In an environment of increasing costs, it is difficult to estimate trend factors. For example, if average claim costs are increasing, some companies may believe that case reserves are more adequate and therefore not reflect the higher trends in the projections.

4) Management optimism. After the large rate increases and underwriting tightening during 2019-2022, some management teams find it hard to believe that loss ratios are not dramatically improving. This belief can delay the recognition of continuing adverse development.

The published industry results for the last few years clearly indicated adverse industry development as illustrated in the graphs above. Preliminary data published through the third quarter of 2025 indicates adverse development is continuing for some companies.

The table below displays development through the third quarter for all lines of business, separated by companies that indicated favorable development for accident years 2022 and prior and those that indicated adverse development.

*Based on Accident Years 2022 and Prior

From Annual and Quarterly Statement data via S&P Global Market Intelligence

For the companies we have summarized that reported third-quarter data, this industry composite displayed little change in prior year reserves for accident years 2022 and prior, with favorable reserve development for accident years 2023 and 2024. 53% of the companies indicated favorable development and 47% of the companies indicated adverse development for accident years 2022 and prior. We note that reserve development differs by company in the amount and magnitude due to the lines of business written.

The quarterly data reported to the NAIC is not presented in the same level of detail as the year-end data, as Quarterly Statements display development for all lines of business combined. Therefore, we segregated the companies into different groupings based on our assessment of the type of business the companies write. Additionally, the quarterly development is only available for accident years 2022 and prior, 2023 and 2024.

The cohorts of companies that primarily write personal lines business, workers compensation business, medical malpractice business and mortgage insurance displayed favorable reserve development for accident years 2022 and prior, and also for accident years 2023 and 2024. Personal lines business as well as workers compensation business are lines generally less affected by social inflation. For accident years 2022 and prior, the total combined reserves for these cohorts of companies developed favorably by approximately 3%.

From Annual and Quarterly Statement data via S&P Global Market Intelligence

However, the cohort of companies that write primarily commercial insurance, companies in run-off, and reinsurance companies displayed adverse development for accident years 2022 and prior.

From Annual Statement data via S&P Global Market Intelligence

Drilling down within the commercial lines writers provides additional insights. The following table displays the reserve development for commercial lines writers that:

• write limited amounts of workers compensation;
• write both commercial and personal lines;
• are excess and surplus lines companies; and
• are writers of other commercial lines of business including workers compensation (i.e., "other commercial writers").

The cohort of companies that primarily write commercial lines with limited workers compensation business displayed higher adverse development (2.6% of adverse development for accident years 2022 and prior) compared to their more diversified peers that also wrote either workers compensation or personal lines (these cohorts displayed 0.3% of adverse development for accident years 2022 and prior). It is reasonable to assume that commercial lines carriers that are more diversified (e.g., write workers compensation or personal lines business) are benefiting from the favorable development on these lines which mitigates the development they may be experiencing in their commercial business.

The cohort of commercial companies with limited workers compensation business also write commercial automobile liability. The companies that primarily write commercial auto liability are displaying higher adverse development. We did not separately segregate these companies as the reserve base is limited and the development is driven by a few companies. Commercial auto liability is a line of business more affected by social inflation that has had significant rate increases and re-underwriting over the past few years, which increases the uncertainty in the reserve estimation process.

We note there is variability within the various cohorts and for certain cohorts of companies, a few large carriers had a significant effect. Within the "favorable" cohorts, 41% of companies posted adverse development for accident years 2022 and prior. Conversely, 49% of insurers in "adverse" cohorts reported favorable development for those same accident years. For the lines of business affected by social inflation, prior years' development hinges on how effectively each insurer has captured social inflation effects in past analyses and how aggressively they are recognizing those pressures today.

Favorable cohorts: Companies writing personal lines, workers compensation, medical malpractice and mortgage insurance

Adverse cohorts: Companies writing commercial business, companies in run-off and reinsurers

Given the factors outlined, we expect unfavorable reserve development to persist for certain lines of business and companies. However, favorable and adverse development will affect insurance carriers differently depending on the lines of business they write and their prior recognition of social inflation in the actuarial methods.

Although accident years 2023 and 2024 are generally indicating a favorable run-off, we have a concern that adverse development will occur in these accident years as the historical adverse development may not be fully reflected in the actuarial assumptions.

To reflect social inflation in actuarial methods, we recommend companies:

• Reevaluate the expected loss ratios that are used in actuarial methods to not only reflect historical adverse development but also current claim activity; and
• Separate lines of business into more granular groupings which segregate those segments more affected by social inflation and those less affected by social inflation (e.g., litigated versus non-litigated claims).

After year-end 2025 data is released, we will publish a companion article that presents updated results with more details by line of business, along with greater discussion on how to adjust actuarial methods.

Today's 25-year-olds are far less likely to have life insurance than the 25-year-olds from the late 20th century. In 1989, for example, 65% of adults aged 20 to 39 owned some form of life insurance; by 2025, that total dropped to as low as 36% of Gen Z members.

Though part of a general trend toward less life insurance ownership today (Figure 1), the reasons behind the decline among younger consumers are distinct, according to LIMRA and Capgemini's recent World Life Insurance Report 2026. Among them: Historical triggers such as marriage and parenthood aren't happening as soon for today's under-40 consumer – if they happen at all.

This is just the latest data point that spotlights the challenge of selling insurance to the next generation.

For insurers seeking to attract and turn young customers into long-term policyholders, it is essential to develop tailored solutions that address their needs and perceived challenges. RGA's research and experience reveal four key design considerations – relevance, simplicity, affordability, and flexibility. By incorporating these four factors into product development, insurers are starting to find success in attracting younger generations.

Why are young consumers not buying life insurance?

Delays in life milestones that historically prompted insurance purchases present a challenge for the industry. The average age at which a man and a woman marry today has increased approximately eight years since 1960, from 22.8 to 30.5 for men and 20.3 to 28.1 for women.

The LIMRA survey also revealed that 63% of consumers under the age of 40 have no immediate marriage plans, and 84% of both single and married under-40 adults have no immediate plans to have a child. This could be a contributing factor behind data showing Gen Z life insurance ownership is more than 20 percentage points lower than it is for Baby Boomers, and 14 percentage points lower than their own next-closest generation, Millennials (Figure 3).

This decline is not due to lack of intent. More than a third (39%) of consumers said in a 2023 U.S. survey they intend to purchase life insurance in the coming year, with higher numbers for Gen Z (44%). Yet those intentions are not turning into actual purchases in sufficient numbers to halt or reverse declining sales.

The reasons younger consumers give for failing to purchase the coverage they know they need suggest opportunities for insurers to develop new solutions addressing those challenges, which include:

• Concerns around affordability, real or perceived
• Uncertainty about protection needs and what products to purchase, indicating potential product complexity or lack of relevance from existing offerings
• Lack of access, pointing to the limitations of traditional life insurance distribution models for reaching that demographic, as evidenced by the larger share of Gen Z and Millennials saying that no one has approached them about life insurance, or that it is not offered by their employer, relative to survey respondents from older generations

Designing life insurance solutions for younger consumers

Insurers have an opportunity to design life insurance propositions that are tailored to the needs of younger customers and that address the concerns they have identified. In doing so, the following design principles should be considered:

• Relevance of the offering, including the core insurance benefits, value-added services or perks, and the distribution approach
• Simplicity, including easy-to-understand insurance benefits and terms, authentic language, and streamlined processes
• Affordability of the overall proposition, both real and perceived
• Flexibility of the coverage and payment options

Relevance

Young consumers are not a monolithic group. They range in stage of life from those just graduating high school through those who could be considered mid-career professionals.

Insurers can design and offer more relevant insurance products that align with key milestones and associated protection needs across this timeline, including life insurance products that offer value-added services relevant to younger consumers. Distribution approaches may also need to be adapted to different life stages, to help mitigate the concerns mentioned by younger customers about lack of access. This may involve considering alternative distribution approaches through partnerships with companies that already have a relationship with younger customers, rather than relying exclusively on traditional distribution models.

For example, home prices in India have increased 1,500% in the past three decades, according to one estimate. That has led more people to delay home purchases and rent instead. One digital real estate marketplace in India partnered with an embedded insurance startup to offer a rent protection plan that can be purchased with a click of a button when consumers submit their online rental payments. Premiums are embedded into the monthly rent payment workflow. Benefits include critical illness coverage from 15 conditions, a personal accident payment, and medical expenses in case of accidental hospitalization.

Increased relevance can lead to growth in the younger-consumer market. Once this younger generation is familiar with a relevant, affordable form of coverage, they can grow into other products as new life events occur and protection needs emerge – from home purchases to marriage to children.

Simplicity

Young consumers have indicated their uncertainty about how much coverage they need and which product to buy (Figure 4). This is likely in part due to the perceived complexity of existing insurance products, and of the language used to describe policy benefits and conditions. Insurers have an opportunity to design simple and easy-to-understand products that young consumers may be more comfortable purchasing, particularly in cases where they have limited access to advisors.

There is also an opportunity to leverage behavioral science techniques to increase comprehension. RGA's research shows that making key policy details more salient, such as by using FAQs or visuals, and improving the relevance of the information presented, for example by leveraging tools and calculators, as well as using video content, can significantly improve comprehension and engagement in digital life insurance customer journeys. These strategies should be particularly relevant for younger demographics accustomed to consuming visual content on social media platforms.

Furthermore, real-world case studies show that improved comprehension through behavioral science techniques can lead to a 48% increase in policy renewal rates and a 32% reduction in policy cancellation rates.

Insurers also need to continue streamlining the life insurance purchase journey they offer to match younger consumers' expectations based on the experiences they're accustomed to from the apps they use daily. As an example, a digital life insurance distributor partnered with RGA and insurers in Canada to offer easy-to-purchase term life and critical illness insurance products, which have achieved success in the market, particularly with younger customers. They offer a streamlined digital customer journey, which allows users to complete a jargon-free fully underwritten application in as little as 20 minutes and receive an instant decision if eligible.

Affordability

The insurance industry is battling a perception problem. One of the most frequently cited reasons for not having life insurance is that it is too expensive. But when asked for a cost estimate, younger consumers are far more likely to give a price far above the median than older consumers.

Innovations attached to low-cost products offer a direct way to change the narrative.

For example, an RGA-supported effort with a bancassurance product uses banking data to assess the risk of applicants and offer discounts to those who prove to be better risks. This tends to favor younger consumers, who generally are also healthier. Discounts range up to approximately 20% for the lowest risk segments.

Insurers can also address affordability concerns by offering benefits that young consumers appreciate and are able to use today, enhancing the overall value proposition of life insurance. For example, a life product sold through one of the largest retailers in Spain incorporates a wellness rewards program that provides incentives for physical activity. The product includes a mobile app that tracks daily steps and syncs with popular activity trackers. Clients can earn maximum rewards by averaging 10,000 or more steps per day. The program rewards users for maintaining an active lifestyle by converting their daily steps into monetary rewards, which can be accumulated and redeemed as gift cards.

The offering is tied to a popular shopping destination, and is the first insurance product in Spain rewarding customers for wellness activity. It attracts younger people by offering a relevant, simple, and affordable living benefit that encourages healthier living through flexible rewards.

Some items insurers must consider in balancing costs and customer cover include:

• Full underwriting vs. simplified (or even guaranteed) issue
• Level vs. YRT premiums, including any premium guarantees
• Basic life cover vs. accidental cover vs. comprehensive covers, including critical illness and disability

This leads directly to the final consideration.

Flexibility

People tend to procrastinate until they see a solution that matches their unique needs. Young people need to see insurance products relevant to them. Each customer has specific needs that must be balanced in coverage type and levels, premium structures, and underwriting.

For example, offering low and affordable covers initially that can flex as life events occur – such as home purchases, marriages, parenthood – or have the option to renew or convert into further insurance covers can be seen by younger consumers as relevant to their lives right now and, thus, individually tailored.

Similarly, while full medical underwriting traditionally leads to the lowest premium for healthy applicants, it can be too complex, and a barrier to entry, for younger consumers used to quick purchases. Offering flexible options that accommodate different trade-offs between price and customer experience can help address the wide range of needs expressed by younger consumers, from the most cost-sensitive, to those prioritizing the smoothest customer journey.

Conclusion: Gaining customers for life

The insurance industry is seeing a change in life triggers, not an evaporation. Marriage and children might be happening later, but other milestones exist for insurers to offer relevant, simple, affordable, and flexible protection products.

Younger consumers may be harder to convert than in past generations, but product innovations can create a rewarding win-win proposition for both the young policy holder and the insurer.

As of early 2026, artificial intelligence (AI) has made only a modest dent in the daily practice of law. Adoption is rising, but cautiously. Many lawyers still avoid AI altogether; others limit its use to narrow, low-risk applications. This restraint sits uneasily alongside the predictions that have circulated since early 2023, when evidence emerged that ChatGPT could earn passing grades on law school exams—and even on the bar exam.

The gap between promise and practice has fed a familiar narrative: if AI were truly transformative, law firms would already look different. Because they largely do not, skeptics argue that AI's disruptive potential has been oversold.

That conclusion gets the timing wrong — and misunderstands what disruption in law actually looks like.

From the beginning, strong performance on exams was a poor proxy for real-world impact. Legal practice is governed by ethical obligations, professional judgment, and client risk—not multiple-choice questions. Lawyers must closely review AI-generated output, especially given well-documented risks of hallucinations and subtle errors. In many contexts, reviewing and correcting AI-assisted work has been slower than producing it directly, or has resulted in lower-quality outcomes than human-only work—particularly when the lawyers involved are highly skilled or when precision matters more than cost savings.

The problem, in short, was not overhype. It was the wrong benchmark.

The question that actually matters is not whether AI can perform legal tasks on its own, but whether lawyers using AI outperform comparable lawyers who do not. Until recently, the answer to that question was unclear at best. Now, emerging evidence suggests it is beginning to turn decisively in AI's favor.

In a recent study, my colleagues and I reported the first randomized controlled trial evaluating two AI innovations with direct implications for legal work. The first is Retrieval-Augmented Generation (RAG), which grounds AI outputs in authoritative legal sources rather than free-form text. The second is the rise of AI reasoning models that structure complex analysis before generating responses.

In the study, upper-level law students were randomly assigned to complete realistic legal tasks using either a RAG-enabled legal AI system, an AI reasoning model, or no AI assistance at all. The results mark a clear break from earlier studies focused on prior generations of large language models. Across multiple tasks, participants using modern AI tools produced meaningfully higher-quality legal work. They also worked much faster. In five of six tasks, productivity increased by between 50% and 130%.

This evidence reframes the AI debate in law. The story is no longer about machines replacing lawyers—or about AI's ability to ace exams. It is about augmentation that finally works: tools that allow lawyers to do better work, in less time, without sacrificing professional standards.

That shift carries real consequences for legal institutions.

Firms that continue to treat AI as an experimental add-on or a compliance risk may soon find themselves at a competitive disadvantage. If AI-enabled lawyers can reliably produce higher-quality work more efficiently, then billing models, staffing decisions, training pipelines, and even partner expectations will come under pressure. Early adopters will not simply save time; they will set new baselines for quality and speed that others will be forced to match.

The implication is clear. The window for cautious observation is closing. In 2026, the strategic question for law firms is no longer whether AI will meaningfully affect legal practice, but how quickly—and whether they will shape that transition or be shaped by it.

AI chatbots have become a regular part of how insurance services operate. Customers turn to them to review policies, follow claim updates, or get quick answers without sitting on hold. For insurers, they help handle high volumes of requests while keeping support costs in check.

As these tools become more visible, expectations sometimes drift. Not every task in insurance should be automated, and not every interaction benefits from a chatbot. Understanding where AI chatbots should step back is key to using them well.

This article looks at the functions that still require people, even as insurance chatbots and broader AI insurance services continue to evolve.

Where AI chatbots make sense

There's no question that AI chatbots add value in the right places. Tasks that follow clear rules and don't depend on interpretation are a good fit. Simple policy questions, coverage summaries, payment reminders, and claim status updates are all examples where automation works reliably.

That's why many insurers treat chatbots as the first point of contact. They take on routine requests, ease pressure on call centers, and keep basic information accessible at any time. In that role, chatbots help guide users and filter requests, rather than make final decisions.

Issues usually arise when the same tools are pushed into areas that require judgment, risk assessment, or clear accountability.

Why judgment-based decisions still need people

Many insurance decisions live in gray areas. Coverage disputes, claim denials, and policy exceptions are rarely decided by one clear rule. They usually depend on context, intent, and how similar situations were handled before.

A chatbot can help surface the explanation or point to relevant policy language, but it shouldn't be the authority behind the decision. Once financial impact or legal exposure is involved, a person has to be responsible for the outcome. This boundary matters not just for compliance, but for trust.

AI chatbots in the insurance industry and emotional context

Insurance conversations don't always happen at calm moments. Accidents, property damage, and unexpected losses bring stress with them. Customers often need reassurance as much as information.

AI chatbots in the insurance industry can respond politely and quickly, but they don't genuinely understand emotional nuance. They can't sense frustration building or know when a conversation needs to slow down.

In these situations, escalation to a human agent isn't a failure of automation. It's a necessary part of good service design.

Claims handling beyond the simple cases

Some claims are simple and move quickly. Others take time, context, and careful review.

For low-risk cases with clear documentation, chatbots can play a useful role. Once a claim includes conflicting information, unclear responsibility, or a higher financial impact, automation starts to fall short.

At that point, human adjusters are essential. They review evidence, interpret policy wording, and make decisions that may need to be held up later under review or dispute. Chatbots can assist by organizing information, but they shouldn't own the outcome end-to-end.

Why AI fraud detection still needs people

AI fraud detection is often presented as one of the strongest areas for automation, and in many ways it is. Systems can scan large volumes of data and surface unusual patterns far faster than any manual process.

What these systems struggle with is intent. In real-world use, AI works best as an early filter, pointing investigators toward cases that deserve a closer look and leaving the final judgment to people.

Insurance chatbot use cases that need a handoff

Many insurance chatbot use cases work best when they are set up as shared workflows rather than fully automated paths. The chatbot handles the first interaction, gathers the required information, and then routes the case forward.

Policy changes, renewals, endorsements, and compliance-related questions often fall into this group. Rules can vary depending on region, policy type, or specific circumstances, which means final guidance usually needs confirmation from a person.

In these situations, a smooth handoff isn't a limitation. It's what keeps the process accurate, compliant, and trustworthy.

Negotiation is another clear boundary. Settlement discussions, premium adjustments, and special terms require flexibility and judgment that chatbots don't have.

Conclusion. Why knowing the limits matters

It's easy to judge progress by what AI chatbots can handle. In insurance, setting limits often matters more than expanding capabilities. Chatbots work well for simple interactions, but customers expect human involvement when the stakes rise.

Insurers that design around this reality tend to see stronger outcomes from automation. They gain efficiency without giving up control, and they improve service without undermining trust.

A year after the devastating Los Angeles wildfires of January 2025, the insurance and reinsurance industry is still absorbing the scale of their impact, as well as the lessons learned for future risk assessment. What is now clear is that these fires were not an anomaly but a warning signal for how wildfire risk is evolving in a changing climate.

The financial consequences of the LA wildfires were significant. To date, insurers have paid out approximately $22.4 billion, with total insured losses reaching around $40 billion overall. These figures place this firmly among the most costly natural catastrophe events in recent US history, reinforcing wildfire's status as a primary driver of loss rather than a secondary peril.

The sparks behind the blaze

In the year since, investigators have been able to piece together a clearer picture of how the fires began and why they escalated so rapidly. The initial blaze, the Palisades Fire, was started by human activity. Emergency services believed the fire had been successfully extinguished, but a combination of Santa Ana winds and exceptionally dry conditions caused it to re-ignite, with devastating consequences.

The second major event, the Eaton Fire, was ignited by a nearby power line. While these two fires inflicted the most severe damage, they were only two of 14 separate wildfires that occurred across the region during January 2025.

Climate change also played a central role in amplifying their severity. Heavy rainfall during 2022 and 2023 drove extensive vegetation growth across California. This period of abundance was followed by a prolonged drought, which dried out that vegetation and transformed it into highly combustible fuel. In effect, climate volatility, not just warming, created ideal conditions for wildfire spread.

A global shift in wildfire behavior

The human cost of these events has also been profound. While 31 deaths were attributed directly to the fires, a medical study published in JAMA (The Journal of the American Medical Association) estimates that up to 400 additional deaths may have been caused indirectly, driven by poor air quality and reduced access to healthcare during and after the events. Further, more than 100,000 homes were evacuated, disrupting communities and livelihoods on a massive scale.

Taken together, these impacts underscore a sobering reality: wildfire risk is no longer confined to historically defined burn areas or traditional seasonal expectations. The LA fires echoed patterns seen elsewhere, including the Australian bushfires of the same year, where successive wet years followed by extreme heat produced similarly combustible landscapes.

The diversity of ignition sources - human activity, infrastructure failure, weather-driven re-ignition, and climate change - highlights a critical challenge for risk modelling: wildfire cannot be understood through a single causal lens and the parallels seen across hemispheres point to a global shift in wildfire behavior.

What this means for our industry

For underwriters, the key takeaway is clear and urgent: past bushfire and wildfire burn areas are no longer a reliable predictor of future fires. Historical loss data, while still of value, cannot on its own capture the rapidly changing interactions between climate, vegetation, weather extremes, and ignition sources.

These distinctions matter. The latest wildfire models, such as BirdsEyeView's, for instance, explicitly focus on different ignition mechanisms, recognizing that the probability, timing, and severity of fires vary materially depending on how they start and how environmental conditions evolve around them. Treating wildfire as a homogeneous peril obscures these dynamics and increases underwriting blind spots.

This has significant implications for pricing, accumulation management, and capital allocation. Wildfire has firmly shifted from a 'secondary' peril into a core driver of portfolio performance. Models calibrated on assumptions of climate stationarity risk lagging reality at precisely the moment when precision matters most.

Progress lies in prediction

Looking ahead, the industry's ability to adapt will depend on its willingness to embrace climate-aware, data-driven analytics. Advances in satellite observation and machine learning now allow us to monitor fuel load, vegetation stress, and environmental conditions in near real time, enabling earlier detection of emerging risk patterns and more responsive underwriting decisions.

The lessons from the LA wildfires, one year on, are therefore not only about loss – they are about learning. If reinsurers and insurers can move beyond retrospective modelling and adopt adaptive intelligence, they will be far better positioned to navigate the growing volatility of wildfire risk.

In a world where climate dynamics are rewriting the rules, resilience will belong to those who can see risk forming before it ignites.