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.

The conversations I'm having with senior people across the industry at the moment have a familiar shape. Someone describes a problem — payment delays, a reconciliation that won't close, a carrier partner asking hard questions about fund visibility — and then, almost in the same breath, they say some version of: "we've known about this for a while."

That's the part that interests me. Not the problem itself, but the fact that it's been known about. Because what that tells you is that the industry has been carrying a form of debt — not financial debt, but operational debt. Deferred investment in the infrastructure that actually moves money, reconciles accounts, and connects claims teams with treasury. It's been accumulating quietly for years, and the conditions that made it easy to ignore are changing.

The buffer is getting thinner

For most of the past decade, there was enough slack in the system to absorb a degree of operational inefficiency. When investment returns are strong and pricing cycles are favorable, slow reconciliation and fragmented fund management don't really show up as problems. They show up as mild annoyances, something for the back office to sort out eventually.

That buffer is narrowing. AM Best has flagged that margin pressure is likely to build through 2026 as rate moderation continues and loss severity persists, particularly in casualty lines. In that environment, those inefficiencies stop being invisible. Finance teams spending hours on manual reconciliation aren't doing liquidity planning. Treasury teams managing reactive funding calls aren't optimizing how capital is deployed. Those are real costs. In a tighter market, they start affecting results.

The timing matters. If you've been telling yourself that the infrastructure investment can wait, the window for waiting is getting smaller.

What the data actually shows

Earlier this year, we surveyed more than 200 senior insurance professionals across claims, finance, and treasury in the US and UK. Some of what came back was striking. Not because it surprised me, but because of how consistently people described the same problems.

Nearly eight in 10 identified internal process inefficiencies as a key barrier to timely claims payments. Two-thirds said accessing readily available funds was a genuine challenge, and that figure rose to 74% in the US. Only one-third of finance leaders said they had clear visibility into delegated claims funds. And just 1% described collaboration between their claims and finance teams as highly effective.

That last number is the one that stays with me. 1%. These are teams that are jointly responsible for payment execution, reconciliation, and financial oversight — and they're essentially operating in separate worlds. That's not a technology problem. It's a structural one, and it's been allowed to persist because the consequences haven't been visible enough to force a change.

Operational risk doesn't stop at your own front door

One thing that often gets missed in these conversations is that insurance is a network business. A carrier can have its own house in order and still be exposed through the weakest link in its chain. If a TPA, broker, or delegated authority is running on outdated processes — quarterly reconciliations, reactive cash calls, no real-time fund visibility — that's the carrier's problem too. It shows up in payment delays, reconciliation errors, and regulatory exposure.

We see this clearly in our own work. Some of the most sophisticated carriers we speak to have invested significantly in their own operations, only to find that the friction sits with a partner they didn't think to scrutinize. In a delegated model especially, you're only ever as good as the operational standards of the people you've trusted to act on your behalf.

The compliance dimension is hardening

There's also a regulatory dimension to this that I think gets underweighted, and the signals from both sides of the Atlantic are worth paying attention to.

In the UK, following a super complaint in late 2025, the FCA announced it will conduct formal reviews of claims handling, servicing and consumer understanding across the general insurance market in 2026. That's not a consultation paper or a future proposal. This is active scrutiny, already underway, focused specifically on how claims are managed and paid.

In the US, California's new claims laws that came into effect on Jan. 1 this year require insurers to accelerate payouts to wildfire survivors, part of a broader legislative package designed to make payment timeliness a hard obligation rather than a best-practice aspiration. These aren't isolated developments. They reflect a direction of travel that is consistent across markets: regulators are increasingly treating payment operations as a conduct and governance issue, not just an efficiency one.

The practical consequence for insurers is that the back-office processes which were once invisible to regulators are becoming visible compliance signals. Carriers that lack real-time visibility into claims funds, or that rely on manual reconciliation across distributed structures, are carrying more regulatory exposure than they may realize. Fixing the operational gap and fixing the compliance gap are, increasingly, the same exercise.

The investment logic has changed

For a long time, the case for investing in operational infrastructure was framed around efficiency, doing things faster and cheaper. That case was always true, but it wasn't always urgent enough to compete with other priorities.

The framing has shifted. Real-time fund visibility, accurate reconciliation, and controlled disbursement aren't just operational improvements anymore. They're signals to your carrier partners, your regulators, and your claimants that you are a capable and trustworthy counterparty. In a market where margins are compressing and scrutiny is increasing, that signal is worth more than it used to be.

The industry has the tools to make this shift. What it needs now is the recognition that the good years, which helped absorb the cost of operational inertia, may not be coming back in quite the same form. The debt is coming due. The question is whether you address it on your own terms, or wait for the market to force your hand.

How many of you are Amazon Prime members? Amazon launched Prime in 2005—over two decades ago. And for those who are members, when was the last time you actually spoke with a human being at Amazon? My answers: 14 years and never. (Consumer Prime Visa inquiries don't count; those belong to Chase and Visa.)

That's not an accident. From day one, Amazon viewed the call center as a symptom of operational failure. Every inbound customer service call represented something that had already gone wrong—a lost package, a complicated return, a process that should have been invisible or idiot-proof but wasn't. The goal was never to answer calls faster. The goal was to build a business so well-engineered that you didn't have to call at all.

Jeff Bezos understood something most executives still haven't internalized: operational excellence is marketing excellence. The way you run your business—its mechanics, speed, and reliability—is what brings customers back. And repeat customers are the whole game.

Yet many organizations still treat customer service as a cost center to be minimized, not a growth engine to be optimized.

Most service operations run on an open loop. A complaint comes in, a reply goes out, a ticket closes, a metric turns green. Nobody learns anything, and nothing changes. Three months later, the same failure repeats—different customer, same root cause, another quiet erosion of loyalty. That's not a service operation. That's a very expensive complaint acknowledgment system.

Closing the Loop

A closed loop works differently. A complaint doesn't just get answered—it triggers investigation, learning, and change. Feedback drives action, action drives improvement, and improvement becomes permanent. Six Sigma's DMAIC model—Define, Measure, Analyze, Improve, Control—maps to this perfectly.

Define the real problem, not just the symptom. Measure what's actually happening: cycle times, reopen rates, handoffs—the hard data organizations instinctively and politically avoid. Analyze where things break and why. Improve the process. And then, critically, Control to make the fixes stick rather than quietly dissolving into next quarter's backlog.

This is where AI earns its keep—without the hype. At Define, natural language processing can spot ticket patterns across thousands of tickets that no human analyst could find in time. At Measure and Analyze, real-time dashboards make the ugly numbers impossible to ignore (which is the point). At Improve, automation handles standard paths so humans can focus on exceptions. And at Control, AI monitoring keeps improvements from quietly unraveling when attention shifts elsewhere.

The Value of Exceptions

Those exceptions—the weird complaints, the one-off issues, the edge scenarios—aren't burdens. They're signals. Gifts, actually. They're the system telling you something it doesn't yet know how to say. Henry Ford put it bluntly: "Don't find fault, find a remedy; anybody can complain." Exceptions are where the remedy lives, and where the next round of improvement begins. Humans belong there not as a fallback, but as the innovation layer.

Why It Matters

None of this is busywork. You close the loop because closed loops create repeat customers, and repeat customers cost far less to keep than new ones to win. When operations reliably turn complaints into improvements, customers notice. Not always consciously—but they notice the refund that arrived fast, the problem that vanished, the experience that felt frictionless. Wow, that was easy.

Frictionless experiences build loyalty in a way no marketing campaign ever will. Bezos built one of the most valuable companies on exactly that principle. The obsession with operations wasn't separate from growth; it was the growth strategy.

Close the loop. Not because it feels good—though it does—but because it's the surest way to win customers who stay, spend more, and bring others with them.

That's not a customer service story. That's a revenue story.

Rising inflation and the cost of living, geopolitical conflicts, market fluctuations, and mounting debt have left today's consumers feeling overwhelmed by financial uncertainty. As insurance agents, it's important to help them reestablish a sense of control where they can: in their insurance policies.

Too often, people manage their insurance passively, only reaching out to their insurance agents when a claim arises. This may leave them vulnerable to coverage gaps and financially unprepared for the unexpected. As your clients' agent, it is important to encourage them to be more active by setting up regular reviews, assessments, and communication opportunities to keep insurance top of mind. The good news is that many of today's consumers recognize that insurance is essential to overall financial wellness, as it can offer needed coverage during life's most unpredictable moments. 

According to a recent survey by the Independent Insurance Agents & Brokers of America (the Big "I"), nearly nine in 10 Americans (88%) say having insurance is very or somewhat important to their financial security. However, what many insurance consumers fail to recognize is that insurance is not simply a "set-it-and-forget-it" bill they need to pay every month. Not only must they have the proper insurance in place for their assets, but they also need to understand what their policies do and do not cover, and they must be maintained and replaced when necessary to ensure they perform when it matters most. 

According to Big "I"'s survey, only about three in 10 Americans (32%) review or shop for insurance each year, with many waiting until premiums increase, major life changes occur, or coverage issues arise. Half of those surveyed reported that they only revisit their insurance after a premium increase or said they never review their policies at all.

Without a more hands-on approach to policy management, an individual's coverage can drift out of sync with their fiscal goals, assets and priorities. This oversight often goes undetected until a claim, premium increase, or coverage issue brings it to the surface. Yet by the time the policy is revisited at the moment of a triggering event, the consequences of having outdated or misaligned coverage may already be underway.

As insurance agents, it's up to you to help your clients avoid these potential pitfalls. First, it's important to position yourselves as advisors to your clients, not just brokers. The most effective agents in the industry recognize that their roles go beyond transactions, creating opportunities for frequent communication to provide insights and answer questions, thereby building trust and encouraging client retention.

As agents, it's important to move away from the "contact me anytime" advice to creating a system for regular policy reviews. This can mean setting up annual or semiannual check-ins with clients, as well as updates for any trigger-based events, such as a marriage, the purchase of a new home, a new job, or the launch of a new business. Agents can position this as part of their professional service model, creating an opportunity not only to provide advice and answer questions but also to build rapport with their client base. Depending on the preferences of the agent and client, this can be offered in the office or remotely by phone or video call.

During these check-ins, it's important that agents take the opportunity to ask about their clients' recent or coming life or financial milestones. This may uncover triggering events such as welcoming a new child, moving homes, or starting a new side business. Directly prior to or during these meetings, agents may also consider providing a gap assessment. This can help both the client and agent see where the gaps in coverage may be lurking. When laid out in a visual assessment, it is sometimes easier for clients to understand where they may be exposed, such as having an uninsured property, or where they may need additional protections, such as an umbrella policy.

Another tactic agents can use to help clients stay on track and informed is by leveraging digital touchpoints. This includes email newsletters, email blasts, short educational content shared on social media and timely automated reminders. These regular digital communications can keep insurance top of mind for clients and remind them about policy renewals and seasonal risks like hurricanes, wildfires and winter storms. Agents should focus on digital touchpoints that provide value and are timely, relevant and personalized, rather than sales messaging.

Easy-to-digest content in the form of checklists, short videos and real-world scenarios can help clients bridge the understanding of their policies and where there are potential oversights in coverage. Based on responses from a gap assessment, an agent can automate a system to send out a personalized message based on that client's milestone or life update. For example, sending a personalized card to a client congratulating them on the purchase of a new home or welcoming a new baby. Not only are agents providing needed information to clients, but they are also reinforcing a stronger professional relationship with them as their trusted advisor. These efforts in tandem can help encourage clients to be comfortable communicating more regularly with their agents and being more active in the management process.

A successful insurance agent knows that their role goes beyond just transactions. In times of financial uncertainty, today's consumers will lean on their professional network for guidance. This presents a unique opportunity for agents to further educate clients on evolving risks and get them more involved in the overall management of their insurance. By establishing regularly structured check-ins, initiating conversations around life milestones and updates, and leveraging value-driven, consistent communication, agents can build trust and create opportunities to integrate them into the process. When clients are empowered to actively engage with their insurance agents, they are likely to be better informed and protected from the unexpected.

Consider the moment a major commercial building changes hands. Thousands of hours of engineering work, such as structural specifications, systems commissioning data, compliance documentation, and material certifications, are packaged and transferred to the new owner. Those files land on a server, in a cabinet, or across a set of folders. One ownership cycle passes. Then another. The files are gone. Not destroyed deliberately, simply abandoned, scattered across drives of firms no longer involved, locked inside obsolete platforms, or surviving only in the memory of a facilities director who retired years ago.

The building stands. Its documented history does not. For commercial property insurers, that missing history is not an abstraction, it is a direct source of claims uncertainty, underwriting exposure, and loss adjustment cost.

A Systemic Gap with Direct Underwriting Consequences

The construction sector has invested heavily in digital modernization over the past two decades. Collaborative project platforms, cloud-hosted repositories, and real-time coordination tools have transformed how structures are built. The volume of technical data produced on a contemporary commercial project would have been unimaginable a generation ago.

Virtually none of it survives into the operational life of the asset.

The failure is not technological — it is structural. There is no durable identity layer linking digital records to the physical asset they describe. Documentation is organized by project, by vendor platform, by the organization that commissioned it. When any of those containers ceases to exist, the records disappear with them. What the industry lacks is a permanent, asset-anchored identifier that survives every platform migration, ownership transfer, and organizational change.

Where the Chain of Custody Breaks — and Why It Matters to Insurers

The project closeout package is the most complete record of a commercial building that will ever exist in one place — engineering rationale for every system, installation records, test results, and compliance evidence. From the moment it transfers to an owner, that record begins to degrade.

Maintenance logs accumulate in facility platforms that tag equipment by internal numbers with no link to original design records. Renovation files are organized around a contractor's billing structure rather than the property's longitudinal history. Alterations and remediation work exist in isolated project files, disconnected from everything that came before.

For insurers, this fragmentation has an immediate operational cost. When a claims professional investigates a roof membrane failure, a fire suppression malfunction, or a structural movement event, the material specifications, installation records, and service history that would clarify how and why the loss occurred are typically inaccessible — or no longer exist.

The Real Cost to Commercial Property Insurers

Documentation fragmentation creates measurable exposure throughout the commercial property insurance lifecycle. Underwriters pricing a risk on a building with no reliable maintenance history must load additional uncertainty into their assumptions. Loss adjusters investigating claims without installation records face extended timelines and higher settlement costs. Subrogation teams cannot build defensible chains of causation without continuous documentation.

When a disputed claim turns on whether a building system was properly maintained — and the records to establish that compliance no longer exist — carriers absorb costs that a functioning documentation infrastructure would have prevented. Commercial properties generate technically rigorous documentation. That it routinely vanishes within a decade of project completion is a structural failure with direct and quantifiable insurance consequences.

Persistent Infrastructure Identity: A Framework Insurers Should Know

Solving this requires intervention at the identity layer. The emerging approach treats identity itself as foundational infrastructure: a permanent, globally unique identifier assigned to every physical asset at creation and maintained across its complete operational life.

This concept — Persistent Infrastructure Identity (PIID) — draws on precedents that have operated reliably for generations. The automotive industry has used Vehicle Identification Numbers since the 1950s, maintaining continuous records across manufacturers, dealers, insurers, and owners. Aviation assigns registration codes that follow aircraft across operators for the life of the asset. Capital markets use standardized securities identifiers to track instruments across institutions without interruption.

A persistent infrastructure identifier gives every commercial building a stable reference point that belongs to no platform, depends on no organization, and survives every ownership transfer. Engineering documents, construction records, maintenance logs, inspection reports, and renovation filings all point to the same underlying identifier — forming an unbroken chain of custody that follows the structure itself.

What This Means Across the Policy Lifecycle

For commercial property insurers, persistent infrastructure identity offers concrete improvements at every stage.

Underwriting becomes more precise when verified construction data, material specifications, and a documented maintenance record replace self-reported property information. Properties with continuous, verifiable histories present a fundamentally different risk profile than those without.

Claims resolution is faster and less contested when the technical record connecting a loss event to the property's history is traceable. The ambiguity driving prolonged disputes is, in most cases, a direct product of documentation gaps that persistent identity would close.

Portfolio management improves when insurers can assess documentation quality across their commercial book — identifying concentrations of risk in poorly documented assets before losses occur.

The Asset History Insurers Have Always Needed

Commercial property insurers have long managed risk without the benefit of continuous, asset-anchored documentation. That constraint has been accepted as an inherent feature of the built environment. It need not be permanent.

As the national registry initiative progresses toward incorporating approximately 160 million addressable U.S. structures, commercial property insurers are well positioned to engage early — and to help define the documentation standards that will inform underwriting, claims, and portfolio management for decades to come.

Buildings carry the weight of the people who rely on them. They should also carry their own history — and that history should be available when it matters most.

Key Takeaways

• Legacy system fragmentation remains the primary barrier to ROI rather than the AI technology itself.
• Agentic systems replace rigid "if-then" logic with dynamic reasoning to navigate complex underwriting and claims.
• Poor data quality in autonomous loops creates a feedback cycle of bad decisions and financial liability.
• Scaling requires an escalation tier where humans verify AI confidence scores to maintain fiduciary responsibility.
• Insurers without real-time API connectivity risk total disintermediation as brokers and aggregators shift to AI-native ecosystems.

The insurance industry is currently captivated by the promise of agentic AI. Unlike the static "if-then" logic of traditional RPA, agentic systems reason, use tools, and pursue goals. They promise a world of touchless claims, autonomous underwriting, and a fraud defense that evolves in real-time.

For insurers operating under sustained combined ratio pressure, volatile catastrophe (CAT) exposure, and shrinking distribution margins, this shift is strategic. Agentic AI appears to offer operating leverage at scale, compressing expense ratios while improving loss performance and portfolio steering.

Yet, as pilot programs move toward production, a frustrating pattern is emerging: enterprise architecture was built for human-centered silos, not autonomous orchestration. Most global carriers still operate across regionally fragmented cores and vendor-locked policy administration systems (PAS) designed for human-mediated workflows and batch reconciliation.

However, these environments were never built for autonomous orchestration across underwriting, claims, and reinsurance. Without architectural modernization, deploying agentic AI onto these brittle foundations does more than just stall ROI. It introduces new forms of operational and regulatory fragility.

We are moving beyond digital transformation. The real inflection point for insurers is agentic readiness.

The Shift from Rules to Reasoning

Traditional insurance automation is deterministic. A rule engine flags claims above a monetary threshold. A rating engine recalculates the premium based on predefined variables. A referral workflow escalates risks outside delegated authority. These systems are efficient within narrow guardrails, but brittle when context shifts.

Agentic AI changes the operating model. Consider a complex auto claim following a severe weather event. An agentic system can validate storm intensity data, correlate telematics feeds, benchmark repair estimates against regional inflation trends, evaluate prior FNOL behavior, and dynamically recommend reserve adjustments aligned to actuarial development patterns.

In commercial lines, it can ingest broker submissions, extract exposure data from the schedule of values, analyze five-year loss runs, interpret manuscript endorsements, and draft underwriting rationale aligned to delegated authority and treaty structures. The misconception is that these capabilities can be layered onto legacy cores.

In reality, most multinational insurers operate across heterogeneous policy administration systems spanning geographies, lines of business, and regulatory regimes. Human underwriters, adjusters, and operations analysts still bridge gaps between claims, billing, reinsurance, and finance. When an autonomous agent attempts cross-system orchestration, it encounters API limitations, latency constraints, inconsistent data lineage, and fragmented identity management.

Data Quality Debt is the Silent Destabilizer

In the context of agentic AI, data quality is a solvency risk. When an agentic system is given the autonomy to adjust reserves or initiate endorsements, "dirty" data, such as inconsistent loss history or fragmented policy records, becomes a feedback loop of bad decisions.

An agentic-ready carrier requires modular, API first architectures where rating events, reserve movements, underwriting referrals, catastrophe exposure updates, and reinsurance recoverables are observable within unified event streams. Agents must learn against actual loss emergence and settlement outcomes — not synthetic feedback loops detached from financial reality.

The Necessity of Human-in-the-Loop Governance

A frequent concern among regulators and C-suite executives is the loss of control. How do we ensure that a non-human identity doesn't errantly deny a valid claim or misprice a catastrophic risk? The answer lies in replacing vague oversight with structured role-based governance.

The architecture must support both Underwriter-in-the-loop (UITL) and Adjuster-in-the-loop (AITL) controls. These are integrated UI/UX components where the AI presents its reasoning, its confidence score, and the specific data points it used to reach a conclusion.

This is particularly vital in specialized lines like Directors and Officers or Cyber insurance, where the risk landscape shifts faster than any model can retrain. By designing architecture that treats the human as an escalation tier rather than a manual processor, insurers can scale without abandoning fiduciary responsibility.

Defensibility in the Age of Autonomy

When an AI agent takes an action such as denying a claim or adjusting a premium, insurers must provide a defensible audit trail that stands up to regulators and reinsurers. Traditional logs that show updated system records are no longer sufficient. We need immutable agent action logs.

This technical requirement involves documenting what tools were queried, what version of the model was used, and what specific data inputs were retrieved at that exact millisecond. In healthcare and life insurance, where compliance is non-negotiable, this level of transparency is the difference between a successful deployment and a multimillion-dollar fine. If you cannot reconstruct the logic of an autonomous decision six months after the fact, that decision is a liability.

Distribution Disruption: Agentic AI Beyond the Core

The disruption is not confined to internal operations. AI-native insurance apps embedded within conversational platforms are reshaping distribution economics. When quoting, comparison and policy binding move into AI ecosystems, insurers with brittle core systems will struggle to expose pricing, underwriting rules, and policy data through secure, real-time APIs. Agentic readiness is both an operational capability and a distribution survival requirement.

In personal lines, AI-enabled aggregators can dynamically compare pricing and coverage language across carriers in seconds. In commercial lines, digital brokers are beginning to pre-qualify submissions using AI copilots before they ever reach an underwriter. Insurers that cannot expose pricing, appetite, capacity constraints, and policy data through secure, scalable APIs risk being disintermediated.

Agentic readiness is therefore not just an operational capability. It is a distribution survival requirement. Architectural modernization determines whether an insurer participates in AI native ecosystems or becomes invisible within them.

Rethinking Accountability and Compliance

The biggest compliance risks emerge when accountability for AI-led decisions is poorly defined. If an agentic system in a personal risk management workflow makes a discriminatory pricing error, who is responsible? The data provider? The model developer? The enterprise architect who enabled the integration?

To mitigate this, we must shift our view of enterprise risk management (ERM). We are entering an era where agent identities must be managed with the same rigor as human employees. This means assigning specific permissions, spending limits, and kill switches to autonomous agents. In areas like disaster recovery and planning, agentic AI can be a massive asset, but only if the guardrails are hardcoded into the architecture, not just the policy manual.

The Path Forward from Silos to Orchestration

The payoff for solving these architectural challenges is measurable and profound. Insurers who move beyond the pilot purgatory of agentic AI see higher straight-through processing (STP) rates, lower leakage, and significantly faster cycle times. But more importantly, they build a resilient foundation that is ready for whatever the next generation of intelligence brings.

The transition from a process-centric organization to an agentic-ready one is a necessity for survival in a high-frequency, high-data-volume environment. We must stop asking if the AI is ready for insurance and start asking if our insurance architecture is ready for AI. The future of the industry belongs to those who treat their enterprise architecture not as a collection of legacy systems, but as a living, breathing nervous system capable of supporting autonomous thought.