Against a challenging commercial insurance landscape, the excess & surplus (E&S) market continues to demonstrate strong momentum. For the sixth consecutive year, E&S premiums have grown at double-digit rates, with U.S. domestic direct premiums written increasing 13% year-on-year to $98.2 billion in 2024, according to S&P Global Market Intelligence. This sustained growth reflects rising demand for flexible, non-standard risk coverage as traditional markets tighten underwriting appetite.

As the E&S market grows, inefficiencies in policy processing have become more pronounced. Agentic AI, by enabling autonomous, intelligent execution, directly addresses these gaps and delivers speed, consistency, and accuracy, redefining policy workflows for specialty and E&S insurers.

Market Dislocations and Operational Challenges

Despite strong growth, the E&S market continues to face structural dislocations. Segments such as umbrella and excess liability, catastrophe-exposed property, construction, commercial auto, and healthcare continue to face profitability pressure. Contractor liability in construction defect states is particularly impacted by long-tail exposures, complex legal environments, and inflationary cost dynamics. At the same time, increased competition is gradually softening the market, even as emerging risks such as supply chain disruptions and generative AI create new opportunities.

Within this environment, operational inefficiencies remain a significant constraint. Policy binding is still heavily manual and fragmented, with 73% of underwriters citing clause review as their number one-time drain. Each policy requires manually reviewing thousands of clause variants, often without intelligent recommendation support. This is compounded by the need to analyze 50–100-page risk engineering reports, where critical insights can be missed.

The result is a slow, error-prone process. The average time to bind a complex commercial policy remains around 21 days, while manual handling increases errors by 45% and annual rework costs by approximately $2.3 million per carrier. In multi-party environments such as the London Market or U.S. E&S segments, these inefficiencies are further amplified, delaying decision-making and affecting broker relationships.

Why Agentic AI and Why Now?

Traditional rule engines have long provided structure and compliance in underwriting workflows, mostly for admitted lines. However, they are not designed to handle unstructured data such as broker emails, PDFs, and bespoke clause language. They lack the ability to interpret context, detect nuanced conflicts, or adapt to evolving risk scenarios.

Agentic AI addresses this gap by combining large language models with multi-agent orchestration. These systems can parse unstructured data, recommend clauses from libraries exceeding 10,000 variants, detect conflicts in real time, and generate plain-language explanations for decisions. Importantly, agentic AI complements rule engines rather than replacing them, handling contextual reasoning while rules enforce deterministic compliance.

This shift enables insurers to move toward adaptive, intelligence-driven workflows, resulting in 60–99% faster quote-to-bind cycles and 3–5% improvements in loss ratios.

Reimagining Policy Binding Across Specialty and E&S Lines

Agentic AI is transforming workflows across both specialty and E&S insurance. In specialty insurance, an AI-powered policy binding solution enables rapid, compliant, and highly customized workflows to meet the market's complex needs. Submissions are seamlessly captured from multiple channels, with AI-driven extraction and validation of unstructured data, including bespoke clauses and risk details. Binding agentic AI automates clause selection, real-time conflict checks, and scenario-based underwriting, ensuring regulatory compliance and accuracy.

According to recent research on U.S. insurance sector growth in 2025, digital-first binding solutions have reduced cycle times by up to 50% and improved pricing precision. Such a solution also streamlines customer communication, automates documentation, and integrates with downstream systems, empowering underwriters to focus on risk assessment and strategic decision-making, while ensuring faster and error-free policy binding.

In E&S markets, where flexibility and customization are essential, agentic AI enables more contextual and dynamic decision-making. It builds multi-dimensional risk profiles using unstructured and external data, supports scenario-based underwriting, and facilitates faster negotiations through real-time analysis of broker inputs. In certain specialty segments, these capabilities have reduced binding times by up to 50% while improving pricing accuracy.

Across both markets, risk assessment becomes more comprehensive, placement decisions more precise, and negotiation cycles significantly shorter. At the binding stage, agentic AI ensures that all compliance and authority checks are completed before execution, while automating documentation and downstream processes.

Transforming Roles With Agentic AI

The impact of agentic AI is not limited to process efficiency; it is fundamentally reshaping roles across the insurance value chain. For commercial underwriters, AI-driven clause recommendations reduce what was once a four-hour manual search to under eight minutes. With pre-built risk briefs, underwriters can shift their focus from data gathering to strategic judgment and decision-making.

For wordings and compliance analysts, the benefits are equally significant. Agentic AI can detect conflicts across more than 200 clauses simultaneously while automatically validating jurisdictional requirements for every endorsement. This reduces manual review effort while improving consistency and regulatory adherence.

Insurance brokers experience faster turnaround times, with many policies moving to same-day binding. AI-generated counter-clause responses in plain language improve negotiation efficiency, while automated coverage summaries enhance client communication and transparency.

Operations and binding teams also see substantial gains. Pre-bind checklists are validated automatically, ensuring no conditions are missed. Policy documents are generated and distributed at the point of binding, and downstream systems, such as CRM, billing, and reinsurance platforms, are all updated seamlessly without manual intervention.

A Real-World Shift in Specialty Insurance

A leading public specialty U.S. insurer's transformation illustrates how these capabilities translate into practice. Facing fragmented workflows and manual processes, the organization modernized its operations by digitizing and streamlining end-to-end policy-binding workflows using a customer communication management platform and an enterprise content management platform. This improved turnaround times, enhanced compliance tracking, and provided a unified view of policy and customer data. As a result, the insurer reduced manual effort while strengthening its ability to manage complex risks and respond more effectively to market demands.

Delivering Measurable Business Impact

The adoption of agentic AI is delivering tangible results across the board. Policy binding times are reduced by 86%, from 21 days to just three days, while clause selection effort drops by 93%, from hours to minutes. Compliance breaches are reduced by 93%, significantly lowering regulatory risk.

Underwriter productivity increases by 175%, enabling them to handle 18–22 policies per week, while rework costs decline by 83%, from $2.3 million to approximately $380,000 annually. Brokers benefit from faster responses and improved service levels, and operations teams gain efficiency through automation. Together, these improvements allow insurers to scale operations without proportional increases in cost or headcount.

The Road Ahead

Agentic AI represents a turning point for the commercial insurance industry. By enabling faster, more accurate, and scalable policy binding, it allows insurers to move from reactive processes to proactive, intelligence-driven operations. As competition intensifies and risks evolve, the ability to process unstructured data and act with speed will define success. The future of policy binding is not just faster, it is smarter, more adaptive, and built for complexity.

Penetration testing (pentesting) is a simulated cyberattack conducted by security professionals to identify and prioritize vulnerabilities in your systems, applications, or networks that can be exploited -- before a real attacker finds them first. Unlike automated scanners that generate lists of potential issues, penetration testing validates exploitability with evidence and proof of exactly how an attacker would get in, what they would access, and what it would take to stop them. Penetration testing follows a structured process governed by internationally recognized frameworks, including the Penetration Testing Execution Standard (PTES) and OWASP Testing Guide.

AI is fundamentally changing what "continuous security assurance" looks like through AI pentesting in 2026. 

Before any testing, the pentester and client must define the rules of engagement, including which systems are in scope, what testing methods are permitted, and what constitutes a "safe" level of disruption. This phase also covers legal documentation (authorization letters, NDAs) and defines what success looks like.

Here are the recommended steps:

A Pentester Initial Check Box

Clients choose among three testing postures:

• Black Box: Tester has no prior knowledge of the environment (simulates an external attacker with no insider information)
• White Box: Tester has full access to source code, architecture diagrams, and credentials (deepest coverage, fastest to execute)
• Gray Box: Tester has partial knowledge - typically a standard user account (simulates an insider threat or compromised credential scenario)

Mapping the Attack Surface

The tester maps the attack surface using passive and active techniques:

• Passive reconnaissance: This includes OSINT (Open Source Intelligence), DNS enumeration, WHOIS lookups, LinkedIn scraping for employee names and technology stack clues - all without touching the target system directly.
• Active reconnaissance: Common methods are port scanning (Nmap), service enumeration, web crawling, banner grabbing. The output is an inventory of exposed systems, services, technologies, and potential entry points.

Threat Modeling

Not all vulnerabilities are equally dangerous. Threat modeling is where the tester (or in AI-powered pentesting, the reasoning engine) evaluates which discovered entry points represent the highest risk given the specific business context. This is where context matters. An SQL injection vulnerability in a payment processing endpoint is materially more dangerous than the same vulnerability in a public-facing blog comment form. Traditional scanners assign the same CVSS score to all vulnerabilities. A skilled pentester (or a context-aware AI agent) weighs them correctly.

Vulnerability Analysis

With reconnaissance complete and attack paths prioritized, the tester performs systematic vulnerability analysis. This includes:

• Automated scanning (Nmap, Nikto, OpenVAS) to baseline known CVEs
• Manual analysis to identify business logic flaws that scanners miss - authentication bypasses, insecure direct object references, race conditions
• OWASP Top 10 coverage for web applications - injection attacks, broken authentication, sensitive data exposure, security misconfigurations, and more

The key distinction between vulnerability analysis and exploitation is that analysis identifies potential weaknesses. The next step is to determine whether those weaknesses can actually be leveraged.

Exploitation

• In this step, the tester actively attempts to exploit identified vulnerabilities to prove their impact. This includes:
• SQL injection to extract database contents or bypass authentication
• Cross-Site Scripting (XSS) to hijack user sessions
• Privilege escalation to move from a standard user account to an administrator account
• Chaining vulnerabilities by combining multiple low-severity issues into a critical attack path that neither issue would represent individually

Post-Exploitation and Lateral Movement

Once initial access is achieved, the tester assesses how far an attacker could realistically go. Questions to be addressed include:

• Can they move laterally to other systems on the same network?
• Can they escalate to domain administrator or cloud root access?
• What sensitive data (PII, credentials, financial records) could they exfiltrate?
• How long could they maintain persistence without triggering detection?

This phase answers the question your C-suite will ask after a breach: "How bad could it have been?"

Reporting, Remediation Guidance, and Retesting

The final deliverable is what separates a useful penetration test from an expensive PDF. This last point matters more than most teams realize. Paying for a pentest and a separate retest engagement is the standard model. It is also where AI-powered penetration testing changes the economics since retest runs become instant, not billed separately.

Expected results from a solid penetration test report include:

• Executive summary: Business-language explanation of risk severity and top findings for the CISO and board
• Technical findings: Vulnerability details with CVSS scores, evidence screenshots, and attack chain diagrams
• Reproducible proof-of-concept steps: Exact steps your team can follow to confirm the vulnerability before fixing it
• Remediation guidance: Specific, actionable fix recommendations - not "update your software" but "apply patch CVE-2025-XXXX to Apache 2.4.x and rotate the following credentials."
• Retest confirmation: A follow-up assessment to verify that remediations actually closed the vulnerability

AI Penetration Testing

Traditional penetration testing forces a choice: you can have depth (manual testing by skilled humans) or frequency (automated scanning run continuously). You cannot have both - not at a cost that scales. AI-powered penetration testing changes the underlying economics. An autonomous AI agent can:

• Map an attack surface and enumerate vulnerabilities without human supervision.
• Adapt its attack logic in real time based on how the application responds - mimicking the reasoning of a human ethical hacker rather than following a static script.
• Validate exploitability with safe proof-of-concept execution.
• Deliver remediation guidance in a developer-ready format immediately after the test completes.

The result is the equivalent of a week or more of manual penetration testing, delivered in hours and available on demand.

What Makes an AI Pentest Agent Different from a Scanner

A vulnerability scanner applies pattern matching. It looks for known CVE signatures, compares version numbers against databases, and flags anything that matches a rule. It is deterministic and static.

An AI penetration testing agent applies adaptive reasoning. It observes how the application responds to an input, infers what that response suggests about the underlying architecture, and adjusts its next action accordingly. It can:

• Notice that a 500 error on a specific input suggests a backend database query is being passed as user input, and pivot to SQL injection testing.
• Recognize that a redirect loop suggests a flawed authentication state machine, and attempt to exploit the race condition.
• Chain a low-severity information disclosure finding with a medium-severity IDOR vulnerability to demonstrate a critical data exfiltration path.

This is the difference between automation (doing the same thing faster) and autonomy (reasoning and adapting independently).

AI Pentesting for Continuous Security Assurance

With an AI agent that can run a full assessment in hours, security teams can:

• Test every significant release before it reaches production
• Re-validate remediations immediately after they are deployed (instead of waiting for the next engagement to confirm a fix actually worked)
• Run targeted retests after CVE disclosures that may affect your tech stack
• Build a longitudinal trend view of your security posture over time, not just a point-in-time snapshot

AI-powered penetration testing replaces annual compliance with continuous security. The most transformative application of AI penetration testing is not replacing the annual manual engagement - it is enabling continuous assurance between those engagements.

The insurance industry can take pride in the fact that innovation can't happen without it. Until innovators and their insurers figure out how to defray the risk from driverless cars, commercial space flight, etc., they can't go to market. But innovation also can't happen without lawyers. While we non-lawyers complain about how they slow things down, innovations can't scale until the legal system develops a framework for adjudicating the inevitable problems. 

Generative AI is moving into its early legal phase, according to a report from Gartner Group. The report predicts that by the end of the year there will be more than 2,000 legal claims worldwide related to "death by AI," as mistakes by the software or by those implementing it may be the root cause of fatalities. 

The implications will be most immediate for health insurers but will be felt soon enough in just about every corner of the insurance industry, especially where AI is being used to try to anticipate and prevent losses.

Let's have a look.

Gartner frames the "death by AI" issue as a broad one for companies in all industries, suggesting that general counsels need to be aware of the risks and need to work with insurers to purchase coverage. Gartner predicts that by 2030 there will be a 60% increased in corporate spending on security and governance related to AI. From that standpoint, AI looks like a big, new opportunity for insurers.

I'm more concerned about the potential surprises that may be waiting for insurers. 

Those insuring medical practices, for instance, may be caught by surprise if the caretakers turn tasks over to AI that then go awry. Human doctors are still very much in the loop at the moment, but there's a real push toward instituting a combination of telemedicine and automated AI advice, especially to reach people who live in remote areas or other "healthcare deserts." So decisions will real consequences may start moving quickly into the AI. 

The theory is great. You outfit people with wearables that monitor their health, alerting doctors of any warning signs. You coach people on eating, sleeping, exercise and so on. Doctors are reachable by Zoom for consultation and diagnosis. 

But what happens when the AI misses the signs of an impending stroke? What happens when it misdiagnoses a diabetic? 

A columnist in the Washington Post recently wrote about an experiment in Utah that raises all of these questions. It's a very responsible test, limited to having AI refill prescriptions, and could have major benefits. The columnist, an MD and former health commissioner in Baltimore, writes: 

"Right now, getting a prescription refilled can be challenging. Many patients call a doctor’s office and struggle to reach the right person or are told it’s not possible without an in-person visit, which requires time and travel. Some end up putting off that visit and go without medications, which can be dangerous for those with chronic diseases such as hypertension, diabetes and cardiovascular issues."

But she also quotes a professor at Harvard Medical School who says that, "while some drugs might appear to be low-risk on paper, prescribing them is often complicated and patient-specific. He noted that many drugs require ongoing monitoring, including regular lab tests, attention to side effects and careful and nuanced discussions with patients. 'It’s not clear that AI is fully able to replicate that,' he said."

And I believe that people -- including those on juries -- hold machines to higher standards than they do humans. Humans can make errors in the heat of the moment. We know we aren't perfect. But software is written by very smart people who aren't under instant time pressure and are vetted by large, responsible organizations (with deep pockets). So AI can't just be good. It has to be perfect.

The potential for legal surprises won't just relate to "death by AI," either. There will also be "injury by AI," at a far greater rate. (While more than 40,000 people die in car accidents in the U.S. each year, for instance, some 2.5 million are injured.) 

And the claims won't just hit healthcare providers that may have misdiagnosed or mistreated someone. I worry about the companies that use AI to detect dangerous situations in workplaces. What happens when they miss one and someone is hurt or killed? What happens when sensors don't detect the electrical problem in a home that leads to a fire, or the leak that's about to become a flood? When the forward-looking dashcam doesn't spot the deer that has jumped into the road? 

As I've written, consumer advocates are already blaming the big, bad algorithm for any decisions they don't like on underwriting and claims. Those legal issues are about to broaden, especially for those promising prevention via AI.

We'll get through this. The legal framework will gradually develop, and we'll learn what the rules are going to be. But we need to brace ourselves for complications like the coming wave of "death by AI" claims.

Cheers,

Paul

AI in insurance is advancing, but it is not yet transforming the industry. We are moving beyond systems that analyze and recommend and toward ones that can act, by initiating claims workflows, flagging fraud in real time, adjusting underwriting decisions, and orchestrating next-best actions. This shift toward agentic AI is often described as a turning point, and it is, but not for the reasons most narratives suggest.

While the technology is evolving rapidly, most insurers remain constrained by a more fundamental issue: they cannot consistently deliver the right data, at the right time, in the right context to support real-world decisions. Until that changes, autonomy will remain limited, no matter how advanced the models become.

Most insurers are not lacking data or platforms. Over the past decade, they have invested heavily in data lakes and lake houses, advanced analytics and AI tools, and integration and data engineering pipelines, yet progress beyond pilots remains slow.

The problem is not access to data. It is making that data usable, trusted, and actionable at the moment a decision is made.

In insurance, this challenge is amplified by fragmented policy, claims, and customer systems, dependence on third-party data such as telematics, weather, credit, and health data, regulatory and compliance constraints, and the need for real-time decision-making in customer-facing processes.

Agentic AI does not solve this problem. It exposes it.

Why a Shared Data Layer is Not Enough

Many organizations respond by building a shared data foundation — a unified layer where humans and AI agents can access the same information. While this is directionally right, it is incomplete. The challenge is not that organizations lack a shared data layer; it is that they struggle to deliver the right version of data for each decision, at the moment it matters.

Insurance operates on multiple, decision-specific views of data, each with distinct requirements:

• Claims decisions depend on real-time, enriched incident data
• Underwriting relies on forward-looking risk models and external signals
• Fraud detection requires cross-entity patterns and behavioral analysis
• Customer servicing depends on a simplified, current policyholder context

These are not variations of the same dataset, they are purpose-built representations of data, shaped by different latency, governance, and semantic needs, which becomes even more critical with agentic AI. Different agents operate at different points in the decision lifecycle, and require different data, in different forms, at different times.

A shared layer can provide access, but effective decisions depend on context.

From Data Access to Decision Activation

This is where many AI strategies stall. Most architectures are designed to store, process, and analyze data, but not to activate it at the point of decision. There is a fundamental gap between data being available and data being usable within real-time workflows.

Agentic AI operates directly in this gap. Without access to live, governed, and contextually aligned data, agents operate with partial understanding, and their outputs become unreliable. This is why many AI initiatives remain stuck in experimentation.

To move forward, insurers need to rethink how data is delivered. Not as raw datasets or reports but as data products — a reusable, governed, and outcome-aligned data asset designed to support a specific decision or workflow. Instead of exposing raw data, insurers should deliver contextualized, decision-ready views, with embedded governance and policy controls, consistent business semantics, and real-time access to internal and external sources.

For example:

• A claims data product unifying FNOL, policy data, repair estimates, and external signals
• A fraud data product combining claims history, network relationships, and behavioral indicators
• An underwriting data product integrating internal risk data with third-party enrichment

These are not static datasets. They are dynamic, purpose-built representations of data, aligned to the decisions they support.

Why Real-Time, Governed Access Matters

For agentic AI to deliver value, data must be live, governed at access, semantically consistent, and traceable. This is where a logical data layer becomes critical, not just as an integration approach, but as a way to connect distributed data in real time, apply governance dynamically, and deliver consistent, business-ready views across systems. This enables both humans and AI agents to act with confidence, without introducing further fragmentation.

The insurers that lead in 2026 will not be those with the most advanced models. They will be the ones that connect AI directly to business outcomes. That means starting with the outcome, such as reducing claims cycle time, improving fraud detection, increasing underwriting precision, or enhancing customer experience, and working backwards to define the decisions, data and systems required to support them.

This is how AI moves from experimentation to operational impact.

Where AI Success is Won or Lost

The next turning point for AI in insurance will not come from smarter models. It will come when organizations accept a deeper truth; AI is only as effective as the data it can access, interpret, and act on, in real time.

Agentic AI accelerates this realization. It makes clear that data must be trusted, contextual, available at the moment of decision, and aligned to outcomes. Those who solve this will scale AI successfully, and those who do not will continue to pilot without transformation.

The future of insurance will not be defined by whether humans and AI agents share the same data. It will be defined by whether they have the right data, in the right form, to make the right decisions. That requires a shift from shared data to decision-ready data, from access to activation, and from experimentation to measurable outcomes. That is the real inflection point for AI in insurance.

The future of insurance will not be defined by better pricing of what we lose. It will be built on protecting how we live.

Two futures are available to the insurance industry. In the first, carriers retreat from low-margin markets, sharpen their focus on high-margin ones, and become progressively smaller and less relevant. In the second, insurers embrace a fundamentally different identity: providers of resilience through prevention, mitigation, and empowerment. Insurers partner in the lived experience of their policyholders, not merely compensating for their losses.

This paper argues that the second future is not only possible but necessary. And that understanding why requires looking beyond the industry's own metrics to the work of a French philosopher who died in 2007.

1. A Crisis of Relevance

The insurance industry has never been more needed, and never more at risk of being left behind.

On the demand side, exposure is growing in every direction. Climate crisis amplifies the frequency and severity of catastrophic events across most lines of business. Emerging risks multiply: cyber, AI liability, disinformation, PFAS, cannabis, political violence, supply chain fragility.

On the supply side, economics are deteriorating. Life & health carriers have watched their cost structures rise 26% over twenty years while telecommunications and automotive reduced theirs by 30%. P&C has done better with a 3% reduction, but remains structurally inefficient by comparison. The result is a widening gap between what society needs and what the industry is willing or able to provide.

The numbers are stark. The global climate protection gap reached $385 billion in 2023. The mortality protection gap stands at $414 billion. The health protection gap at $941 billion. The pension gap, $106 trillion today, is projected to quadruple by 2050. Meanwhile, the top 19 global reinsurers have more than halved their exposure to insured catastrophe losses over the past decade, pushing risk back onto primary insurers and ultimately onto policyholders.

The industry's response, the rapid growth of fronting, MGAs, and E&S, has been creative and necessary. It has preserved relevance in pockets. But it has not answered the deeper question: what is the insurance industry actually for?

The protection gap is not primarily a pricing problem. It is a product architecture problem, uncovering an identity problem.

2. The Innovator's Dilemma, applied to insurance

In 1997, Clayton Christensen described how successful companies sow the seeds of their own disruption. By focusing relentlessly on higher-margin customers, products, and distributions they systematically abandon market segments, leaving them open for insurgents who discover pockets of profitability that incumbents could no longer find.

The pattern is recognizable in insurance. As risks become increasingly systemic, too large, too correlated, too well-known, the rational incumbent response is to preserve margin by retreating. The logic is sound. The consequence is existential.

I had a conversation with a reinsurance company who was considering exiting the cyber market entirely. Raising premiums while reducing coverage had not solved their loss ratio problem. What they had not considered was whether the loss ratio could be improved by design, not through underwriting levers alone, but through prevention, empowerment, and crisis response built into the product itself.

Educate policyholders on their actual cyber exposure to reduce frequency. Provide a real-time crisis response service when a breach occurs to reduce severity. Monitor risk profiles continuously rather than annually to improve selection. Empower individuals to take measures to become more resilient to nudge behavior. That is how an insurer becomes simultaneously the cheapest in the market and the one making a profit. The reinsurer I spoke to could not see this path because their why was defined narrowly. "We are a single-digit, pure-play, cheap, follower reinsurer," they told me. Seen from that vantage point, retreating was the only option.

Christensen's insight was that incumbents cannot disrupt themselves from within, unless they fundamentally reframe their purpose, I would add. The P&C industry has done this partially and structurally through the MGA and E&S boom. But the deeper reframe, from risk transfer to resilience provision, requires a shift in identity, not just in distribution.

The Job To Be Done, as defined by the policyholder, is not "how do I get an indemnity if my house burns down." It is "how do I make sure my house does not burn down and how do I get back into it (or an alternative) as quickly as possible if it does."

Only by seeing risk from the end user's perspective can the industry and policyholders align their respective incentives. Prevention first. Mitigation second. Risk transfer last. This is how commercial risk managers think. It is how an anthropologist would approach the disruption to lived experience in consumer lines. It is not how most insurers currently operate.

The carriers who survive the next disruption cycle will not be those who polished their underwriting diamond to become ever smaller. They will be the ones who become resilience providers, purveyors of risk management as a service in P&C, and longevity as a service in life & health and in doing so, gain relevance.

3. Experience as a Service. Insuring the Hyper-Real

To understand where the insurance industry can go next, we can find inspiration in philosophy and futurism. Beyond P&C and L&H, enters a fifth branch of insurance, the lived experience.

In 1981, Jean Baudrillard published Simulacres et Simulation, the work that inspired the Matrix quadrilogy, in which he described a world where the distinction between the real and its representation ceases to matter. In Baudrillard's hyper-reality, the simulation becomes more real than the reality it was meant to represent. Facts and opinions conflate. Physical and virtual experiences blur. What matters is not whether something is real, but the meaning attributed to the experience of it.

We are living in that world now. Every new generation spends more time on screens and derives more of its understanding of reality from social media and its cognitive bubble. When ChatGPT retired its model 4o in February 2026, the flirtier and more sycophantic version of its LLM, 50,000 people found themselves airing their grief on Reddit over the loss of their AI boy/girlfriend. The economic cost of disinformation is estimated at $78 billion annually, and the World Economic Forum has named it the number one global risk for the next two years and one of the top risks for the next decade. Yet the insurance industry has produced very little in response.

The gaming industry is larger than the combined book, music, and film industries. Billions of dollars of value exist in digital skins, virtual artefacts, and online identities. When a gaming account is hacked, that loss is as real to its owner as a stolen car. The insurance industry insures almost none of it.

We are no longer just expected to insure bricks and mortar. We are being called to insure the integrity of digital environments and their lived experiences.

This is the new paradigm: moving from a safety net for what we lose, to a navigator for how we live. And it is only achievable through partnership.

Consider what this looks like in practice from other industries. A flight is no longer just transportation from A to B. It is a door-to-door experience orchestrated across Delta, Uber, and YouTube, with Skymiles linked across every touchpoint. A Samsung smart fridge connects to Instacart to replenish automatically and soon with a smart toilet to buy the most adequate food. Volvo's CEO described partnership at CES as "the new leadership" to deliver any product as an experience. Insurance has deployed elements of this thinking in narrow lines, kidnap and ransom, cyber, health, but has barely begun to apply it systematically.

The insurer who partners with a real-time fact-checking service to protect policyholders from disinformation exposure is not just adding a service feature. They are repositioning themselves as a trusted partner in navigating reality itself. That is a fundamentally different relationship than the one built on an annual premium and a possibly disputed claim.

But a word of caution. Services added to products without genuine incentive structures do not nudge behaviour, they invite suspicion. Policyholders have learned to read wellness trackers and telematics devices as surveillance tools: "any information gathered will be used against you." Prevention and empowerment services earn trust only when they genuinely serve the policyholder's goals, not just the insurer's data needs. The test is simple: does this service make the policyholder's risk profile meaningfully better and enrich their lived experience? If not, it is marketing, not resilience.

Conclusion: The Choice

Two paths are available. Some insurers will follow the logic of the innovator's dilemma to its conclusion, remaining pure-play underwriters, preserving margin in an ever-narrowing market, profitable so long as capacity is scarce. That is a viable strategy. It is not a relevant one.

Others will make the harder choice: to reframe their purpose around the lived experience of the people they serve, to become partners in resilience rather than processors of loss, and to build the trust that the industry's current model has eroded. Those carriers will grow into the protection gaps that their competitors abandon. They will build products that people want to use, not just need to have. They will become, in Baudrillard's terms, navigators of the real, in whatever form the real takes next.

The most valuable insurance product of 2040 may not pay claims. It would prevent them. The shift from indemnity to prevention is one of insurance's deepest structural changes already underway. The second movement yet to come is to empower policyholders navigating how we live, including the lived experience of whatever the real is.

The question is not whether this transformation will happen. It is which carriers will lead it, and which will be left explaining why they chose not to.

The insurance industry has long treated a certain level of fraud as the cost of doing business—much like a grocery store plans for produce that never makes it off the floor. But generative AI is changing that equation.

The Coalition Against Insurance Fraud estimates that fraud costs the U.S. more than $300 billion annually, with property and casualty fraud accounting for roughly $45 billion of that total.

The defenses that carriers have spent decades building—special investigation units, predictive modeling, contributor databases—are struggling to keep pace with the rapid increase in AI-generated fraud. According to Gen Re, the estimated number of AI-enhanced insurance fraud cases in the U.S. jumped from fewer than 20,000 in 2022 to more than 80,000 in 2025. And Verisk's State of Insurance Fraud Study found that 99% of insurers have encountered manipulated or AI-altered documentation.

AI's evolution has put powerful tools in nearly everyone's hands, making fraud far more scalable. Fraudsters aren't just submitting a single doctored photo and hoping it slips through—they're generating entire claim packages: fake damage photos, repair invoices, contractor assessments, and supporting documentation, all internally consistent and built to pass automated checks from intake through adjudication.

What makes these claims harder to catch

Photo fraud used to be easy to detect—borrowed images, mismatched metadata, or inconsistent lighting that an experienced adjuster could quickly flag. What we're seeing now is fundamentally different. Today's image models can generate damage photos tailored to a specific property, with realistic lighting, weather, and perspective. The images align with the claim. The invoices support the images. Everything appears to belong to the policyholder's home.

Lower-quality fraudulent submissions still give themselves away. A roofing claim might mention window damage but show no window in the photos. AI is sophisticated, but these errors still happen when details aren't carefully cross-checked. Close scrutiny can surface these inconsistencies—but only if you're looking for them.

There's also a pattern in how these claims are priced. Lower-value submissions often move straight through processing with limited human review, and fraudsters know where those thresholds sit. When a claim comes in at $4,999 on a policy capped at $5,000, it's worth asking questions.

How carriers are detecting AI-generated fraud

Detection is layered, with each layer building on the last. It starts with metadata; timestamps that don't match the loss date or geolocation data that places a photo far from the insured property are immediate red flags.

Contributory databases add another layer. By pooling data, carriers help surface emerging fraud patterns quickly—much like antivirus software matching known signatures. Even well-constructed claims leave patterns, and these systems are built to detect them.

Experienced adjusters remain irreplaceable. A claims professional with 20 years on the job has reviewed thousands of legitimate claims and can pick up on subtle details that automated systems miss, like a medical member ID number formatted incorrectly. That institutional knowledge doesn't live in a model.

Carriers are also tightening the intake process itself. Requiring policyholders to submit photos through dedicated apps—ones that establish a verified chain of custody for the image, with embedded metadata—makes it far harder to substitute AI-generated photos after the fact. Video evidence requirements add another layer; high-quality video remains significantly harder to fabricate convincingly than a still photo.

Staying ahead of the threat actors

There's an arms race quality to all of this, and the industry needs to be honest about what that means. The tools for generating fraud are becoming more sophisticated on a faster timeline than most carriers' detection capabilities are improving. Contributory databases and human expertise are necessary but not sufficient to combat this enhanced fraud. The feedback loop between detection and response has to shorten.

Regulators are paying attention. The National Association of Insurance Commissioners launched a 12-state pilot to examine how insurers use AI in claims decisions, with a nationwide rollout targeted for later this year. The same AI capabilities that enable fraud can also enable carriers to flag legitimate claims incorrectly, and the industry needs to be able to demonstrate where those boundaries are.

The volume may also be larger than headline fraud cases suggest. According to Verisk, 55% of Gen Z consumers and 49% of millennials say they'd be at least somewhat likely to make a small, rule-bending edit to a claim photo or document. Most of them probably don't think of that as fraud. They think of it as clarifying. But as AI editing tools become more accessible, the line between a touched-up photo and a fabricated one is collapsing—and the volume of altered photos will grow with it.

The most effective response keeps experienced humans in the loop, invests in shared detection infrastructure across carriers, and shortens the feedback cycle so new fraud signatures are captured and shared faster. None of this is a permanent fix. But in a contest where the other side is constantly iterating, the carriers that move fastest will absorb the least damage.

The Palisades and Eaton wildfires reinforced that wildfire losses do not behave like traditional property claims. Rather than isolated damage events, they function as community-wide construction, environmental remediation, and recovery projects. Outcomes were driven by regulatory complexity, access constraints, sequencing, labor and material availability, documentation quality, and expectation-setting, not solely by coverage interpretation.

Now more than one year removed from the event, rebuilding efforts continue, and lessons are still being learned. Public permitting data, construction progress, and long-tail recovery programs demonstrate that wildfire recovery is a multi-year process. While each wildfire presented unique conditions, the recovery challenges and lessons learned were highly consistent across both footprints. This paper summarizes key lessons learned from direct involvement in Palisades and Eaton wildfire losses and offers practical guidance for insurers and claims leaders seeking more predictable and defensible outcomes in future wildfire events.

1. Wildfire Losses Are Community Rebuilding Projects

Wildfire losses differ fundamentally from wind, hail, or flood events. In the Palisades and Eaton wildfires, total and partial losses were concentrated within dense geographic areas, creating shared constraints related to debris removal, access, labor, materials, permitting, inspections, and utility restoration.

Traditional claim workflows often assume losses can be evaluated independently. In wildfire environments, that assumption breaks down. Reconstruction timelines, costs, and feasibility were influenced by neighborhood-wide demolition activity, agency sequencing, and competition for limited resources. Claims that recognized these realities early progressed more efficiently than those treated as isolated events.

Access restrictions further complicated recovery. In the early weeks following the wildfire, owner authorizations, vehicle passes, police checkpoints, road closures, and traffic congestion added significant administrative and travel time. These constraints gradually subsided, resulting in meaningful budget and schedule variation depending on when evaluations were performed.

2. One Year Later: Permitting and Rebuild Timelines Remain Extended

More than one year after the Palisades and Eaton wildfires, rebuilding remains an extended and uneven process. Los Angeles County permitting data illustrates that while thousands of rebuild applications have been submitted, a significantly smaller number have progressed to active construction or completion. Large volumes of projects remain in zoning review, plan review, or awaiting permit issuance.

Average durations for zoning review, plan approval, and permit issuance continue to be measured in months, not weeks. This reinforces that wildfire recovery timelines extend well beyond the first year, and that claim strategies, rough order assumptions, and policyholder communications must reflect the prolonged permitting and rebuilding durations.

3. Regulatory and Environmental Complexities

Wildfire recovery in California is shaped by a complex regulatory environment. Debris classification, hazardous material identification, soil testing, clearance protocols, and disposal requirements frequently dictate the critical path of recovery.

Wildfire smoke and soot consist of a complex mixture of ash, char, particulate matter, and combustion byproducts that can travel significant distances and infiltrate structures through vents, openings, and normal building air exchange. The presence and severity of contamination varies widely based on location, fire behavior, weather conditions, and building construction.

Assumptions that all structures within a wildfire perimeter are uniformly impacted often lead to over-scoping, disputes, and delays. More effective outcomes are achieved when environmental conditions are validated through visual inspections, targeted sampling, and objective clearance criteria rather than generalized assumptions.

4. The Importance of Local Expertise in Wildfire Recovery

Many insurers rely on national emergency service contractors through approved vendor programs. While these firms often mobilize quickly, challenges arise when deployed resources lack familiarity with local environmental regulations, debris handling requirements, and jurisdiction-specific processes.

In both Palisades and Eaton, this frequently resulted in delays, scope revisions, disputes, and frustration for both policyholders and carriers. Contractors with established local or regional wildfire experience were better equipped to navigate agency coordination, testing protocols, clearance requirements, and sequencing challenges. Local knowledge proved to be a meaningful differentiator in execution quality and predictability.

5. Scope and Pricing: Why Simplified Models Fail

Square-foot and lump-sum pricing models were commonly used for fire, smoke, and soot cleaning during the early stages of loss triage. While attractive for speed, these approaches consistently failed to reflect site-specific conditions.

In practice, contractors often provide aggressive low pricing before visiting the site to secure the work. Once complexity became apparent, repeated supplements followed, increasing total cost and extending timelines. More defensible outcomes were achieved when scopes and pricing were developed based on site inspections, documented conditions, and loss-specific requirements rather than generalized assumptions.

6. Sequencing and Scheduling Matter: Managing Recovery and Rework

In an effort to return insureds to their homes or businesses quickly, fire, smoke, and soot cleaning was frequently performed while adjacent properties were still undergoing debris removal or demolition.

As a result, completed cleaning scopes were compromised by dust, ash, and continuing construction activity, requiring re-cleaning and added expense. In high-density total loss areas, sequencing proved critical. Cleaning, debris removal, and reconstruction activities needed to be coordinated at a community level rather than on a claim-by-claim basis.

On both Property Damage and Builder's Risk claims, establishing a realistic and defensible repair timeline proved critical. Effective schedules were not high-level directional estimates, but detailed recovery schedules built from quantities of work, scope of damage, sequencing constraints, and estimated labor hours. These schedules supported mitigation efforts, accountability, and reduced downstream disputes related to delay and prolonged loss durations.

In wildfire events involving properties already under renovation or reconstruction at the time of loss, additional execution complexity emerged. Builder's Risk claims frequently involved two policies: one covering the existing structure that predated renovation and was not included in the contractor's scope of work, and another covering work completed or in progress. From the insured's perspective, repair costs were often viewed holistically, while from a claims standpoint, costs required careful allocation between policies and scopes.

In several Palisades and Eaton losses, this complexity was compounded when approved change orders, scope expansions, or increased construction costs were not fully reflected in reported project values at the time of loss. Identifying and addressing these issues early helped avoid interruptions to recovery schedules, clarified true exposure, and reduced downstream disputes once reconstruction resumed.

7. Engineering and Structural Observations

Wildfire damage patterns differed materially from those typically observed in individual structure fires. Structural damage tended to be binary, with buildings either sustaining minimal damage or being largely or completely consumed.

In completely consumed structures, fire-related damage to concrete foundations was more prevalent than typically observed in isolated fires. Prolonged heat exposure and limited suppression contributed to foundation degradation, requiring careful evaluation. Conversely, at smaller structures where minimal foundation damage was identified, it was often more economical to remove and replace foundations rather than attempt salvage.

Engineering evaluations also highlighted the effectiveness of flame-resistant construction features. Many intact residences adjacent to completely burned structures exhibited little to no damage beyond smoke and soot exposure, due to the presence of non-combustible materials and adequate property setbacks.

8. Smoke, Soot, and Secondary Damage Considerations

Wildfire smoke impacts frequently manifested as secondary damage rather than direct structural loss. Unlike interior structure fires, wildfire soot is typically powdery and externally driven, affecting how contaminants migrate into buildings and contents.

Observed impacts included soot accumulation on horizontal surfaces, infiltration through vents and window assemblies, and contamination of HVAC components. These conditions required careful evaluation to distinguish between cosmetic contamination, restorable damage, and true health or safety concerns. Failure to validate smoke impact early often contributed to over-cleaning, unnecessary replacement, or re-cleaning.

9. Financial, Business Interruption, and Long-Tail Cost Implications

Wildfire reconstruction costs were influenced by tariffs, labor shortages, material availability, and logistics challenges. These impacts varied significantly by location, scope, timing, and material selection.

Applying broad percentage-based multipliers often overstated or understated actual impacts. More accurate outcomes were achieved when financial impacts were evaluated on a loss-by-loss basis.

Business interruption exposure was frequently driven by factors unrelated to physical damage. Evacuation orders, access restrictions, power outages, and air-quality concerns resulted in BI claims even where structures sustained little or no damage. These claims required alignment between construction schedules, environmental clearance, and BI analysis.

10. Public Debris Removal Programs

Following major wildfires, many property owners enroll in publicly administered debris removal programs coordinated by the California Governor's Office of Emergency Services (Cal OES). These programs play a critical role in expediting debris clearance for residential properties but can create long-tail insurance considerations that are often misunderstood.

Cal OES may seek recovery of debris removal costs from insurance proceeds years after the work has been completed, limited to unused insurance funds remaining after a rebuild or replacement purchase. Counties often wait until reconstruction is complete before pursuing recovery, making delayed invoices common.

Public debris removal programs traditionally prioritize single-family residential properties. Commercial buildings, cafés, retail stores, bars, office buildings, and many mixed-use properties are often excluded and remain responsible for clearing their own sites.

Disputes have arisen regarding the reasonableness of invoiced items, including charges for services not rendered. Insurance companies are not expected to pay more than the reasonable cost of equivalent private services. A consistent lesson from Palisades, Eaton, and prior wildfire events is the value of obtaining a private debris removal estimate regardless of program enrollment. Independent estimates provide a benchmark for reasonableness and help avoid disputes years later.

11. The Value of Periodic Pre-Loss Property Inspections

Periodic pre-loss property inspections provide meaningful benefits to both insurers and policyholders, particularly in wildfire-prone regions. Risk inspections and thorough documentation establish an objective record of a property's pre-loss condition, construction features, and improvements.

Claims progressed more efficiently when reliable pre-loss documentation was available. Inspection reports and photographs reduced disputes over scope, condition, and valuation, supporting accurate indemnity determinations and reducing reliance on assumptions.

Pre-loss inspections streamline the claims process by enabling faster scoping, more accurate estimating, and clearer communication. They also support fraud prevention by making it more difficult to mischaracterize pre-existing conditions or attribute unrelated issues to the loss event.

In wildfire-exposed areas where recovery timelines extend over multiple years, pre-loss inspections serve as a foundational reference throughout the entire claim lifecycle, from initial assessment through final resolution.

12. Setting Expectations Early

Clear expectation-setting early in the claim lifecycle consistently drove positive outcomes. Aligning on pre-loss conditions versus elective redesign or betterment decisions helped avoid downstream disputes.

Early, evidence-based communication reduced conflict, particularly when insureds compared outcomes to neighboring properties with different exposure profiles. Claims progressed more smoothly when expectations regarding scope, cost, and duration were established early rather than reconciled at the end.

Key Takeaways for Insurers and Claims Leaders

• Wildfire losses behave as complex, multi-year recovery projects.
• Permitting and rebuild timelines often extend well beyond the first year.
• Early engagement of technical and local expertise improves predictability.
• Simplified pricing and percentage-based adjustments create downstream challenges.
• Sequencing and coordination are critical in dense total loss areas.
• Builder's Risk losses require early clarity around scope, cost allocation, and recovery scheduling.
• Independent validation, both pre- and post-loss, reduces disputes.
• Early, objective documentation supports accurate indemnity.

Additional Contributors: Brooks Armstrong, senior vice president and regional lead, J.S. Held; Keegan Petty, senior managing director. J.S. Held; Christopher Wilkens, senior vice president, J.S. Held; Daniel L. Williams, senior vice president, J.S. Held; Bill Zoeller, senior vice president and EHS claims service line lead, J.S. Held.

Insurers aren't struggling to collect data. They're struggling to use it before it goes cold.

The difference between catching fraud before payment and spending weeks recovering funds typically comes down to whether data moves through their systems in real time or in batches. That gap is fixable, and it doesn't require replacing core systems to close it.

The business case for real-time data is well established, from faster fraud detection to more efficient claims handling, and sharper underwriting decisions.

What's less straightforward is the path to getting there without destabilizing the systems the business depends on. Legacy architecture, batch-processing dependencies, and deeply embedded operating models represent genuine organizational risk, and treating that concern seriously is the starting point for solving it.

Here's where insurers typically get stuck, and how to move past barriers.

The Barriers to Real-Time Data Adoption

For most insurers, the obstacles are organizational as much as they are technical:

Batch Processing Architecture

Many policy administration systems (PASs), billing platforms, and claims management systems (CMSs) are built to process data in batches, typically writing updates to a database once every night.

The data is accurate, but by the time it reaches an analytics engine, it could be 24 hours old.

For AI-powered fraud detection, the lag is a window of exposure.

Data Silos

Modern, cloud-based software and risk management platforms have torn down many data silos, but enough persist to create operational friction. Claims, underwriting, and billing often run on different systems, and gaps between them can have real-world consequences.

For example, an auto insurer may be collecting telematics data in real time. But claims data is only fed downstream after the first payment is made. So, insights from claims information may arrive weeks after a claim is made.

When fraud history lives in yet another analytics environment, investigators are left to perform time-consuming, manual analyses.

Latency Built Into the System

An API call made every 30 minutes is not real-time data, even if it's often treated as such.

Fraud rings don't operate on half-hour cycles; they execute in minutes. Even a short delay can be the difference between interrupting a payment beforehand or recovering funds days or weeks later.

Organizational Disruption

Replacing core platforms is expensive, time-consuming, and organizationally disruptive. The good news is that building real-time capabilities doesn't require a wholesale system replacement.

Five Steps for Building Real-Time Capabilities Without Starting Over

Step One: Start with Decisions That Can't Wait

Not every process needs real-time data, and trying to modernize everything at once is how transformation projects stall. The better approach is to identify where latency creates the most exposure. For most insurers, that means FNOL triage, claims severity scoring, underwriting risk signals, and fraud assessment prior to payment.

Step Two: Stream Events as They Happen

Instead of importing entire databases, the goal is to stream individual events, such as a claim submitted, a policy bound, a payment requested, as they occur.

The most common mechanism for this is change data capture (CDC), which detects updates in your database and publishes them instantly to a downstream application. Tools like Amazon Kinesis and Apache Kafka are widely used for this purpose. CDC can run in parallel with your existing systems, so you don't have to choose between modernization and stability.

When those event streams feed an AI-powered analytics engine, the model is working with data accurate to within moments rather than many hours, a difference that matters tremendously in fraud detection and claims triage.

Step Three: Build a Real-Time Data Layer

A real-time data layer aggregates events from multiple systems as they continuously update using a message broker to receive, store, and deliver events to consuming applications like an AI analytics engine.

The practical value goes beyond speed. Because the message broker sits between your transactional systems and your AI models, you avoid direct integrations that are brittle and expensive to maintain. The data layer becomes the connective tissue, retaining event history for as long as needed and providing your models with both current signals and historical context.

Step Four: Enrich Your Data

Data enrichment is all about adding information to raw data so it's easier to use it to power a decision.

Enrichment pulls context from external sources such as geolocation, weather data, claims history, and fraud signals, and transforms a data point into a decision-ready insight. This is where AI earns its place in the architecture. An LLM can ingest and synthesize contextual data at a scale and speed no manual process can match, surfacing the risk indicators your team needs before the window for action closes.

Step Five: Connect Insights to Actions

Real-time insights have no value if they don't reach the right person or system at the right moment. That means building automated workflows that route findings directly into operations: flagging a claim for SIU review, pausing a payment pending investigation, or holding an underwriting decision for additional scrutiny.

AI can support both ends of this process: generating enriched insights and helping the teams who receive them determine next steps. The goal is to make sure that judgment is informed by current data.

The technology is mature and the implementation path is clear. Insurers who start with a single high-value use typically see measurable efficiency gains within weeks, not months. What's left is the organizational will to start. For most insurers, that's the only thing still standing between where they are and where they need to be.

Insurance has always been about navigating uncertainty, but the kind of volatility we face today is different. In just a few years, underwriters have had to absorb the impacts of a pandemic, new conflicts, evolving sanctions, and persistent inflation, all while global trade routes and partnerships grow less predictable.

The difficult truth is that many major risks can't simply be avoided. Crude oil still passes through the Strait of Hormuz. Agricultural goods still move through contested territories. The job for insurers is not to reroute around these risks but to reprice them as conditions change.

That shift is forcing a fundamental rethink of how the industry perceives exposure, how it uses data, and how quickly it can make decisions.

When Stability Assumptions Break Down

Most analytical and AI models are built on an assumption of stability. They work best when trade patterns, political conditions, and market behavior stay within the limits of historical norms. But that isn't how the world works anymore.

In a structurally unstable environment, it's not that insurers lack sophisticated tools. The problem is that the information those tools rely on is changing faster than the models can adjust. A sanctions update, a sudden military escalation, or a disruption in shipping routes can alter risk conditions overnight.

When that happens, the gap between model predictions and real-world conditions widens, leaving insurers uncertain about when and how to act.

The True Constraint: Decision Velocity

The biggest limitation facing insurers today is not computing power or model design. It's decision velocity: the ability to act at the speed of change.

Underwriters constantly face a tradeoff. They can make quick decisions based on incomplete information or slower, more informed ones that come too late to matter. That tension is especially visible in specialty markets like marine or trade credit, where exposure conditions shift daily.

To stay ahead, insurers need to move from fixed risk assessments to continuously updated ones that integrate internal and external signals in near real time.

Building Trusted Context at Scale

Improving decision velocity starts with better data, but it doesn't end there. The real challenge is turning large amounts of fragmented data into a foundation of trusted, connected context.

Consider a marine insurer covering shipments through the Red Sea. By pulling in vessel tracking data, shipping advisories, satellite imagery, and even local security updates, that insurer can build a live picture of exposure as conditions evolve.

The same applies to other lines of business. Trade credit insurers can monitor political developments, sanctions dynamics, and partner credit signals to anticipate defaults. Property and business interruption insurers can track supply chain issues or regional cost surges to better understand how claims severity might shift.

When these insights are connected, decision-making becomes faster, sharper, and more confident.

From Static Underwriting to Continuous Risk Assessment

Traditional underwriting cycles were built around periodic reviews: evaluate, bind, and revisit at renewal. In a world where risk conditions evolve daily, that cadence no longer fits. The industry's next step is continuous risk assessment. With a connected data ecosystem, insurers can refresh exposure views constantly, manage forms, endorsements, and pricing as new intelligence arrives, and align capacity decisions with live market conditions.

This approach doesn't replace actuarial discipline; it enhances it with context. The result is underwriting that keeps pace with the environment it's meant to protect against.

Seeing, Trusting, and Acting Faster

The future of insurance will belong to organizations that can see more, trust their data, and act faster than disruption can spread. Speed, in this case, does not mean cutting corners. It means using connected, contextual insight to make sound decisions at the right moment.

In a fragmented, fast-changing world, the winners won't necessarily have the most complex models. They will have the clearest view of reality. Because when everything is connected, the real constraint isn't intelligence. It's decision velocity.

Key Takeaways

• Direct buyers invest heavily in consumer-facing marketing with a single objective: acquire policies at the lowest cost possible.
• The vast majority of policyholders who sell their life insurance never receive a competing offer, creating a structural information asymmetry in the market.
• Fiduciary brokerage representation introduces competition into the transaction and shifts the incentive structure in favor of the seller.
• The life settlement industry needs to prioritize brokerage advocacy as a consumer protection standard, not treat it as optional.

The life settlement market has grown significantly over the past two decades. More policyholders are becoming aware that selling a life insurance policy is a legal, regulated option. More institutional capital is flowing into the space. And more technology platforms are making the process faster and more accessible than it was even five years ago.

But there is a structural problem sitting at the center of this growth that the industry has been slow to address. The players with the largest marketing budgets and the most aggressive consumer outreach are the ones whose financial incentive is to pay sellers as little as possible.

The Asymmetry Problem

When a senior decides to explore selling their life insurance policy, the first point of contact almost always determines the outcome. And in the current market, that first point of contact is overwhelmingly a direct buyer.

Direct buyers, also known as life settlement providers, are institutional investors or companies backed by institutional capital. Their business model is straightforward: acquire life insurance policies from policyholders at the steepest discount possible. Some hold those policies to maturity, collecting the death benefit when the insured passes away. But many do not hold the policy at all. Instead, they turn around and resell it, often immediately, to institutional investors, hedge funds, or bundled portfolio buyers at a significant markup. They are functioning as middlemen, buying low from an uninformed seller and selling high into the institutional market. The policyholder takes the discounted payout while the direct buyer captures the spread.

This is the part of the life settlement market that rarely gets discussed publicly. A direct buyer who purchases a $500,000 policy from a senior for $80,000 and resells it into the institutional market for $160,000 has just made a substantial profit without ever holding the policy as a long-term investment. The senior, meanwhile, accepted what felt like a windfall without ever knowing their policy was worth twice what they received.

None of this is illegal. But it reveals a structural imbalance that the industry has been slow to address. Direct buyers are spending millions of dollars on television ads, direct mail campaigns, digital advertising, and call center operations designed to reach policyholders before anyone else does. Their goal is to be the only offer on the table.

And it works. A significant number of policyholders who sell their life insurance in the United States receive only one offer. They have no basis for comparison, no competitive tension in the process, and no independent representation looking out for their financial interest.

The Marketing Budget Gap

Consider the economics. A direct buyer who acquires a $500,000 policy for $80,000 instead of $150,000 has just improved their return by a significant margin. That $70,000 difference is real money, and it came directly out of the seller's pocket. This means every dollar a direct buyer spends on marketing to reach that seller first is a high-ROI investment, because the payoff is a cheaper acquisition.

Brokerages, by contrast, earn a commission on the transaction. Their fee is a percentage of the sale price. They have an incentive to maximize the payout, but their marketing budgets are a fraction of what direct buyers spend. The result is a market where the loudest voice in the room belongs to the party with the least alignment to the seller's financial interest.

This is not a niche issue. According to the Life Insurance Settlement Association (LISA), the life settlement market processes billions of dollars in face value annually. But the gap between what sellers actually receive and what their policies are worth on the open competitive market remains significant. That gap is the direct consequence of a market where most transactions happen without competitive bidding.

What Brokerage Advocacy Actually Changes

A life settlement broker is licensed by the state and, in most jurisdictions, carries a fiduciary obligation to the policyholder. The broker does not buy the policy. Instead, they take the policy to a network of competing institutional buyers and facilitate a competitive bidding process. The result is straightforward: More buyers see the policy, more offers come in, and the seller receives a higher payout.

The data supports this consistently. Policies that go through a competitive brokerage process routinely settle for multiples of what a single direct buyer initially offers. This is not because direct buyers are acting in bad faith. It is because a buyer in a non-competitive environment has no reason to offer more than the minimum a seller will accept.

Brokerage representation changes the dynamic entirely. It introduces market forces into a transaction that would otherwise be a private negotiation between an institutional buyer and an individual seller who has no leverage, no information, and no representation.

What the Industry Needs to Do

The life settlement industry has made real progress on transparency, technology, and regulatory standards over the past decade. But if the default path for most sellers is still a single offer from a direct buyer with no competing bids and no independent representation, then the market is not functioning the way it should.

There are several things that need to happen:

• Regulatory bodies should require disclosure at the point of sale informing policyholders of their right to independent brokerage representation before accepting any offer.
• Financial advisors and estate attorneys need to understand the difference between referring a client to a single buyer and referring them to a fiduciary broker who will create competitive tension.
• Industry associations should advocate for brokerage representation as a best practice standard, not a secondary option.
• Consumer education efforts need to come from sources other than the buyers themselves, who have a vested interest in keeping the process simple and non-competitive.

None of this requires new legislation or a fundamental restructuring of the market. It requires the industry to acknowledge that a market where most sellers receive exactly one offer is a market that is underserving the people it claims to protect.

The Bottom Line

The life settlement market is not short on capital, technology, or regulatory infrastructure. What it is short on is seller advocacy. The policyholders entering this market are overwhelmingly seniors on fixed incomes making one of the most consequential financial decisions of their later years. They deserve more than a single take-it-or-leave-it offer from the party with the most to gain from underpaying them.

Stronger brokerage advocacy is not about attacking direct buyers. It is about building a market where the seller has a real seat at the table, with real representation, real competition, and a real chance at receiving fair market value for their asset. Until that becomes the standard, the life settlement industry will continue to leave billions of dollars on the wrong side of the transaction.