Sponsored by: ITL Partner: PwC

Sponsored by: ITL Partner: PwC

With over 25 years of operational and advisory insurance experience, Yuri Poletto leads the Open and Embedded Insurance Observatory and co-leads the Cyber Insurance Frontier, member-led, executive communities focused on the evolution of insurance distribution, ecosystems, and cyber risk within regulated markets. Both initiatives convene senior leaders from across the global value chain - including insurers, brokers, tech providers, and non-insurance brands - to shape forward-looking strategies. These communities host executive plenary sessions, workshops, strategic roundtables, and research initiatives across Europe, North America, and Asia, helping market leaders navigate complexity with better-informed strategic decisions.

Paul Carroll 

Travel insurance has long been a great example of how insurance can be embedded into another process, especially buying plane tickets, but embedded insurance is moving far beyond that. What are your favorite more recent examples?

Yuri Poletto

One of the most interesting and inspiring recent examples of embedded insurance involves a Brazilian fintech called NuBank. NuBank launched an embedded life insurance proposition and achieved remarkable sales. What's particularly interesting to me is that nearly 50% of the buyers of this life insurance were first-time insurance buyers.

This is a remarkable example of how embedded insurance is not only about seamless distribution and innovation but is effectively a way to grow the market, enlarge the pie, and reduce underinsurance—which is an issue in every part of the world.

They sold 2 million policies in just a few years, and, as I said, nearly half went to first-time buyers of life insurance. 

In hearing care, a global leader in this niche provides hearing aids and comprehensive auditory services. They have been a pioneer in the space, offering embedded insurance since 2014. Today, over 200,000 of their clients in Europe protect their hearing aids through these integrated policies, ensuring peace of mind for a critical and expensive medical device.

Embedded insurance is repositioning itself from a distribution efficiency model into a model that can create markets.

Paul Carroll

Where do you see embedded insurance breaking out in the next few years?

Yuri Poletto

I see several areas. Some of these opportunities are led by digitalization of markets. For example, home insurance and households is an area where we already see some activity, but we'll see much more thanks to the growth of proptech platforms. They see embedded insurance as a clear opportunity to gain additional margins.

Telcos, particularly in Europe, are increasing their commitment with embedded insurance. In this case, they’re driven by competition. They're suffering because of low-cost competitors, so it becomes fundamental for them to retain customers, and selling embedded insurance is probably the best way to increase their chances.

Similar dynamics apply to auto insurance. We know that car manufacturers and dealers basically don't gain margins from selling the cars, but they gain margins from services. Insurance, after-sales services, extended warranties, and maintenance are a fundamental part of this.

Another area for growth is the gig economy. Today, there are niche providers of embedded insurance for the gig economy. I think we'll see more in the future because the gig economy is huge. In the U.S., nearly 40% of workers are freelancers, and they don't have access to insurance for their freelance work.

Cyber, for me, is one of the largest opportunities for embedded insurance because attaching cybersecurity and cyber protection tools has proved to be the most effective way to sell cyber insurance. 

I think we'll see lots of evolution in embedded insurance and many areas of growth. 

Paul Carroll

Embedded insurance makes sense because it reaches people when they're already thinking about potential risks, but one significant concern is customer ownership. If a bank offers life insurance to its customers, who ultimately owns that customer relationship—the bank or the insurer? If it's the bank, doesn't that put the insurer at risk of being replaced at any time?

Yuri Poletto

It’s a critical question, Paul, and frankly, the one that keeps legal teams up at night during the implementation phase. In the U.S., the hurdle isn't just "who owns the customer" but "who holds the license." Because insurance is regulated at the state level, scaling a national embedded program across over 50 jurisdictions requires a sophisticated licensing strategy. Often, the answer lies in the MGA model. By using an MGA structure, the partners can clearly define customer ownership and data rights in the contract while ensuring that the entity facing the customer, whether it’s a tech platform or a bank, is operating within the strict boundaries of state licensing laws.

The relative strength of the partners is also important. Insurers are used to being the "big guy," but when they partner with a Big Tech firm or a major retailer, they are meeting an equal. The smart way to solve this is through modular compliance: building a tech stack that can handle different regulatory requirements state-by-state, so the insurer can remain the "manufacturer" while the distributor owns the "experience" without the insurer fearing they are becoming an interchangeable utility.

We see massive volumes of this working in Europe and Asia, and it's happening in North America, too. The compliant paths exist; it’s just a matter of designing the legal and licensing "plumbing" as carefully as we design the user interface.

It can be done.

Paul Carroll 

People generally don't like to buy insurance, so they would prefer to purchase one comprehensive policy to cover everything. However, with embedded insurance, consumers are being offered individual policies when purchasing specific items like expensive watches or jewelry. What is your view on the tension between these individual policies for specific purchases and the idea of an overarching policy that would cover everything?

Yuri Poletto

That's a good point. I see it this way: Embedded insurance isn't a replacement for traditional distribution, it’s an expansion of the market. It "enlarges the pie" by reaching customers exactly at the point of need, often during significant life milestones or transactions where insurance might otherwise be an afterthought.

For example, many SMEs are chronically underinsured because the traditional broker model often struggles to efficiently handle complex risks when premiums and commissions are low. Embedded solutions bridge that gap by automating the process within the software SMEs use to run their business.

Furthermore, in the home insurance market where the comprehensive policy model is the standard, we can now integrate coverage directly into the mortgage closing process, a home maintenance subscription, or the moment a tenant signs a lease. This context-driven approach makes protection a natural extension of the purchase rather than a separate, secondary chore. Even the most comprehensive homeowners or renters policies have blind spots, like coverage limits on high-value jewelry, specialized electronics, or specific liabilities related to remote work. Embedded insurance also serves as a vital complement here, filling the gaps that a "one-size-fits-all" policy misses.

Ultimately, it isn't a battle of "one policy versus many." It’s about utility. If a customer finds a bundled policy more convenient and in line with their needs, they’ll choose it. But when a frictionless, point-of-need offer provides targeted protection, that is where the market truly grows. In the end, the most frictionless customer experience will prevail.

Paul Carroll

Back-of-the-envelope calculations suggest embedded insurance can save 15% to 25% of the first year premium on policies like life insurance by engaging customers already active in another environment. What other numbers best demonstrate the effectiveness of embedded insurance?

Yuri Poletto

This is one of the big selling points of embedded insurance, the fact that the distribution chain is simpler, with fewer intermediaries, resulting in lower commissions and lower costs for end customers. Sometimes it's true, but other times it's not. Particularly in the past, before there was more attention around this issue, B2B2C affinity insurance was sometimes sold with commissions of 70% or 80%, which didn't make it any cheaper than insurance purchased through another channel.

I have seen significant savings in commercial lines in the U.K., for example, where embedded insurance sold through SaaS companies achieved savings of up to 40% in price. I don't have particular numbers for personal lines, but I can speak to commercial lines because I've worked directly with one of the members of the observatory in the U.K.

The theory and the model are sound—once you're dealing with a brand that already has a customer base and you manage to set up a commercial agreement that works well and is streamlined, you can definitely achieve significant savings. Obviously, if you charge huge commissions, you eat into all those savings.

But I see the market moving toward more transparent settings and lower commissions. We've also seen some cases where prices are reduced thanks to profit sharing with customers. 

Paul Carroll

Technology clearly has enabled embedded insurance. Is it now as good as it needs to be, or can we expect meaningful improvements as technology continues to advance?

Yuri Poletto 

Unlike in the past, the technology for embedded insurance is mostly available today. It's no longer niche and super expensive, but mainly commodity. So the issue is not so much the availability of technology but the fact that often organizations prefer to try to build the technology internally rather than using technology that has been developed by specialists. This is a bit of a blocker.

APIs, obviously, are the key kind of technology when we talk about integration of insurance in non-insurance flows. There are a lot of enabler companies that provide these kinds of insurance services. We are already seeing a consolidation from the vendor standpoint, and we probably will see more now that capital is no longer cheap, because many enablers rely on venture capital.

Paul Carroll

This is all super-insightful. Thanks, Yuri.

Valentine's Day brought a flurry of reports about devastating romance scams perpetrated by organized, remarkably patient cyber criminals. 

While insurance doesn't cover these individual scam victims, they dramatize what insurers are up against as criminals become more sophisticated and tap into AI's capabilities as they go after the companies that insurers do cover. 

It's a scary sight — and may get worse.

Fortune magazine told the tales of three women who have come forward to warn others as part of their efforts to combat romance scams. They described great patience by the scammers and careful wooing; one even invested $185,000 to fool his victim.

After meeting a man on a dating site, one of the women received numerous trinkets from him — then a $100,000 check. She worried the check was fake, but it was real, and she deposited it. He then began planning a birthday bash for her on a yacht and laid out $85,000 for deposits and various purchases. He texted her constantly — she counted 20,000 text messages, in all — and asked her to pray with him daily. Eventually, he suggested she withdraw her life savings and invest in a sure-thing crypto venture, which she did. She received email weekly reports, likely generated by AI, that showed thousands of dollars of gains each time — but those were fake. She lost $1 million in savings, as well as her condo.

Another woman felt she had bonded with a man and was preparing to meet him, when he asked her to help him log into his bank account while he was abroad. He supposedly needed to pay a translator as part of a deal that would net him a $10 million payment. When the woman logged into the bank account, she saw $700,000 in cash. The next day, both were locked out of the bank account, and he said he needed $20,000 for an additional payment. Reassured by his impressive bank account, she sent him the money. He pocketed it. 

The third woman was wooed by a man who not only seemed to care for her but for her six rescue dogs. When she'd talk with the scammer on the phone, if one made noise in the background, he'd ask, "Is that Duffy? Is that Trixie?" He gradually talked her out of her life savings, as well the life insurance payment she'd received when her husband died. She was left with so little money that she she couldn't repair her air conditioner, a decision that led to a fire that burned her house down and killed all her dogs. To escape, she had to flee the house in her underwear at five in the morning, in a neighborhood where she'd lived for 42 years. 

Vox describes AI as a "force multiplier" for these romance scams, which occur all year but pick up around Valentine's Day. Someone who might have been able to run a few scams at a time pre-AI can now operate 20 or more simultaneously.

"On the dark web," Vox says, "fraudsters can purchase romance scam toolkits complete with customer support, user reviews, and tiered pricing packages. These toolkits come with pre-built fake personas with AI-generated photosets, conversation scripts for each stage of the scam, and deepfake video tools, [Chris Nyhuis, the founder of cybersecurity firm Vigilant] told me. 'The skill barrier to entry is essentially gone.'"

Richard Graham, the practice lead for financial crime at Moody's, told me these scams are becoming more ambitious. 

"Five years ago, six years ago, [scammers] were just people online, trying to get your information, get a couple bucks and move on. Now... instead of asking you for money on day one, they're building rapport. They're spending a lot of time with you to better understand who you are.... sometimes over weeks, but usually months.... It's not just a $3,000 payment they want. They want everything."

Graham says it's hard to know just how many billions of dollars a year are lost to these scammers, because the vast majority of victims are too embarrassed to report the crimes. But he says a UN report found that some 235,000 people worldwide were working in professional organizations, largely in Southeast Asia, two or three years ago to perpetrate these romance scams, and he assumes the number has grown since then. 

He says AI helps these professionals develop better scripts to use as they groom their victims. It can also can help those who aren't native English speakers to smooth out any issues with their language skills. AI certainly helps gather information from social media sites as scammers try to learn as much as possible about their marks. AI also makes it easier and less expensive to cast a wide net of messages that could begin interactions with potential victims. While the techniques used to woo victims are referred to as "love bombing," the thieves refer to their goal with a crasser term: "pig butchering."

Graham says AI isn't being used much at the moment to generate deep fakes as part of romance scams — but only because they aren't needed just yet. 

"That actually has been a surprise to me. I would have thought at this point that deep fakes would have been a much bigger problem," he said. "They are a problem, but they haven't really scaled yet, because these other scams [based on dating sites, social media and text messages] have just been so low-effort and so successful."

That's not a happy thought for cybersecurity, in general: Thieves have enormously powerful tools at their disposal — but don't even have to use them much just yet because people are still so easy to fool.

The only solution is to escalate our vigilance as fast or faster than the thieves are escalating their capabilities. The stories of corporate victims aren't as dramatic as those of the poor women Fortune described, but the financial damage is orders of magnitude greater. 

Cheers,

Paul

P.S. Graham offered a pointer for anyone who thinks someone they know might be getting courted as part of a scam. He says thieves always want to get victims into a chat app as fast as possible. That way, they don't put at risk a social profile they've spent a great deal of time crafting and won't be caught in any safeguards set up by the social site. So you can simply ask whether the person you're concerned about has any chat apps on their phone. That question won't raise hackles in the way that a statement like, "I'm worried about you," would. If the person has a chat app, you can explore further and perhaps head off a financial catastrophe. 

Epic Systems dominates healthcare IT—over 35% of U.S. hospital market share, trusted by most major health systems, and increasingly positioning itself in the insurance/payer space with Tapestry (health plan platform) and Payer Platform offerings. If you work in health insurance and haven't heard of Epic, believe me, you will.

Founded by Judy Faulkner in 1979, Epic built its reputation on customer obsession, deep integration, and never selling out to private equity or going public. For health insurers evaluating Epic's growing footprint in claims, care management, and member engagement, this trust matters. But Epic's AI strategy introduces a dependency chain that should concern any COO or CDO betting on long-term operational transformation.

Epic's AI road map runs almost entirely through Microsoft Azure and OpenAI. Ambient documentation, predictive analytics, revenue cycle automation, clinical decision support—all built on the Epic-Microsoft-OpenAI stack. This isn't a vendor partnership; it's architectural dependency. And Microsoft just confirmed how deep that dependency runs: in its latest earnings call, they reported that fully half of Azure's AI inference load runs on OpenAI models.

For Epic customers, this creates compounded risk. You're not just betting on Epic's execution—you trust Judy Faulkner, and rightly so. You're betting on Microsoft's sustained healthcare commitment and OpenAI's organizational stability.

Microsoft has tried and abandoned healthcare repeatedly: HealthVault (2007-2019), healthcare cloud initiatives that quietly deprioritized. Healthcare represents less than 5% of Microsoft's revenue. Their actual priorities: Azure infrastructure, Office/Copilot, Gaming, LinkedIn. If OpenAI's healthcare AI underperforms or faces regulatory barriers, what's Microsoft's incentive to double down versus pivot Azure AI resources to more profitable verticals?

The OpenAI dependency may be more concerning. Microsoft has invested $13 billion for 49% ownership of OpenAI's for-profit entity, but that doesn't buy control over strategic direction, safety culture, or talent retention. OpenAI's November 2023 board crisis—where the CEO was fired for trust issues, then reinstated via employee revolt within 96 hours—revealed governance dysfunction that never fully resolved. The safety-focused board members and researchers who prioritized responsible development over shipping speed have largely been sidelined or left to found competitors like Anthropic. What remains is a growth-at-all-costs culture increasingly optimized for investor returns. Two years ago, OpenAI laughed off the notion of ads. Last month, they started running ads.

For insurers deploying AI into prior authorization decisioning, claims adjudication, clinical documentation, and fraud detection, governance matters. This isn't consumer chatbot territory where failures mean embarrassing screenshots. This is financial exposure, regulatory risk, and potential patient harm. If OpenAI faces safety incidents, regulatory sanctions, or capability degradation, Epic's AI roadmap stalls and you bear the operational consequences with zero recourse.

Then there's pricing. OpenAI's current API costs are venture-subsidized loss leaders. Post-IPO pressure or when Microsoft demands ROI on their $13 billion investment, inference pricing will spike—potentially three to five times current rates. Epic will pass these costs through as "AI-enhanced module" increases. Your negotiating leverage? Approximately zero. You're a third-order customer with no direct relationship to the entity setting prices.

Microsoft's earnings revelation—that half their AI load runs on a single vendor with documented governance issues—should trigger every Epic customer's (or potential customer's) risk management protocols. Epic has consolidated AI strategy into Microsoft; Microsoft has consolidated its AI capabilities into OpenAI. Three single points of failure, any of which could spike pricing, degrade capabilities, or shift strategy in ways misaligned with your operational needs.

When Epic comes knocking, you can trust Judy Faulkner's execution. But Epic's AI future depends on Microsoft's healthcare commitment and OpenAI's organizational stability—neither of which has a reassuring track record. The health insurers negotiating on this dependency will maintain leverage. Those that don't may find themselves funding Microsoft and OpenAI's margin expansion while paying for the privilege.

At least 94 million families own a pet in the U.S., yet just one in 10 rental properties allow animal companions without restrictions. In the context of a housing affordability crisis that is driving more Americans to rent, many face the inevitable question of whether to choose convenient housing or their pets.

Property managers and insurers alike face continued uncertainty around pet-related liability in this scenario. Many are left with no choice but to fall back on blanket restrictions; when lacking documentation reigns, these stakeholders operate blind.

With the average dog-related insurance claim now hitting nearly $69,000, managers and insurers can no longer afford one-time approvals or all-encompassing measures; dog bite claims specifically have risen over 48% in the past decade.

Meanwhile, communities increasingly demand both pet-friendly environments and safe, fair and forward-looking processes from their insurers.

Nuance in underwriting

Risk models rest on flawed assumptions, policies may not match real-world conditions, and when claims do arise, they depend on interpretation more than on established facts. Pet risks get obscured, and are quietly shifted to insurance carriers.

A property, for example, allows for all dogs under 25 pounds, because managers assume they pose less risk than a 100-pound Rottweiler. A tenant then signs a lease, and with them comes a Chihuahua with a long history of aggression: it has bitten humans and, once, latched onto the upper lip of a neighboring Rottweiler.

The lease insurance policy does not capture such nuance. Months later, when the Chihuahua bites a visitor, an insurance agent is contacted, and the claim is evaluated: the insurer could interpret the situation as "small dog equals low risk," or not. Without consistent and accurate record-keeping, coverage might be disputed or delayed.

Alternatively, if a blanket ban is imposed, the Chihuahua will probably still be living in the property – out of sight, until a problem occurs.

Precedents attesting to the complexity have long been set. In California's landmark 1995 Donchin v. Guerrero case, Alpha Donchin and her Shih Tzu were attacked by two Rottweilers that escaped from a rental property four blocks away, leaving her with a broken hip. The property manager denied knowing the Rottweilers even existed in his property, yet they had escaped through a damaged fence, resulting in the court ruling liability; he knew, or should have known, about his tenant's dogs and their aggressive behavior.

Risk does not need to be unknown. New approaches, including standardized pet screening, continuing documentation and compliance, data-driven risk insights, proactive mitigation measures, and risk-based insurance policies can make liabilities quantifiable.

Solutions combining pet screening with dog bite insurance are new, but very much needed. Consistency in processes can be tracked – including everything from emotional support animal verification to vaccination monitoring and document management –, flexibility poses wins for all parties involved, fast screening offers convenience, and new revenue is generated through responsible pet ownership programs and curing manual screening costs.

Why is the insurance industry lagging?

One size does not fit all. Restrictions, including banning certain breeds, sizes, weights, behavioral requirements, or vaccinations are not born from previous negative experiences, but rather indirect sources of concern. Because of a lack of information, managers and insurers alike also tend to overestimate costs associated with pets.

Bottom line, there is no way to analyze risk without getting the complete picture. In looking at pet insurance, this industry golden rule rings even truer. For too long, rentals have relied on vague and outdated restrictions that leave tenants and managers in the dark; traditional safeguards like breed discrimination are not strong predictors of a pet's behavior.

Legislation is already catching up. Last year, a Florida County Council required owners of "dangerous" dogs, excluding specific breeds but including those who had severely injured other animals without provocation more than once, to carry at least $500,000 of liability insurance. But even here, terminology like "trained to attack" or "bred for fighting" are poorly defined.

Standard homeowner policies, however, continue to provide from $100,000 to $300,000 on average in liability coverage. To mitigate this gap, stakeholders have turned to exclusion rather than innovation: blanket bans from tenants or limitations in coverage, with some carriers refusing to write insurance for renters with animals. It is now time to catch up.

Measurable pet injury liability

Accurate pet screening must be combined with liability coverage to make dog bite risk quantifiable. Teams must have access to all-encompassing systems that help verify pets, track vaccinations and documentation, manage compliance and maintain consistent records.

For example, traditional policies recognize that multi-family properties face higher liability exposure than single-family homes because of their communal components: shared walls, common areas, multiple tenants, and manager responsibility for maintenance of spaces. Yet, cases like Donchin v. Guerrero demonstrated high risk also exists by merely living in residential areas, regardless of whether tenants rent apartments or single-family houses.

Vulnerability should be analyzed individually, proactively, and consistently: pets screened singly, risks minimized both before animal companions move in and during their tenancy, and liability coverage must reflect these nuances.

There is already evidence of managers setting up these good practice pillars by setting up pet meetings prior to lease signing, for instance, but these have proved futile in many cases; pet bite claims remain alarmingly high.

Expecting stakeholders to manually follow up with pet records, vaccination requirements, and bookkeep audit-ready documentation as data becomes more granular is also increasingly unrealistic.

This is where technology, in the form of AI-powered automation, centralized real-time insights, and integrated application programming interfaces (APIs), comes into play. Such a measured approach supports premium adjustments, deductibles or coverage caps, rather than relying on traditional – and often ineffective – pet bans.

Five pillars that guide technological adoption in pet-related insurance claims:

• Centralization: Portals that integrate pet approvals and bundled dog bite coverage reduce breed bias, ensure compliance and reduce liability.
• Customization: Managers can set their individual pet approval criteria that align with insurance policies.
• Automation: Streamlining of resident onboarding processes reduces staff time spent on pet approvals.
• Derisking: Minimize liability and discrimination risk with standardized and transparent processes and records.
• Inclusivity: Objective scoring model that reduces bias and promotes transparency.

This change in paradigm offers more than the peace of mind for managers and insurers; it also supports community-building, the promotion of longer tenancies, and better communication.

In the end, pet bite liability and provisions to support all stakeholders involved have shown the real power insurers can leverage, for the benefit and betterment of communities.

The next step, then, will come from them, as processes are smoothed out through technologies, responsible parties assume the eagle-eyed view that is now required, and seize the opportunity to move from reactive to proactive.

The tech world is currently obsessed with "how smart AI can be." From large language models to multi-agent systems, the race is on to build more capable "brains." However, for the insurance industry and the broader commercial world, a far more critical question remains unanswered: Once an AI is smart enough to act, how do we manage the consequences of those actions?

We are entering the era of Agentic Commerce, where AI shifts from a "recommendation engine" to an autonomous "actor." But as we move from low-stakes tasks (like ordering coffee) to high-stakes institutional actions (like claims automation or asset allocation), we face a structural wall.

The reality is that a $10 trillion Agentic Economy cannot exist without the insurance industry. Why? Because authority can be delegated, but accountability cannot be outsourced. For AI to truly participate in commerce, it needs more than better code; it needs the "institutional road rights" that only the insurance and financial sectors can provide.

The Underwriting Prerequisite: Causal Delegation

Most AI experiments fail in real-world transactional environments because they rely on "correlation-based" models. These models are inherently unpredictable because they don't understand why they are acting; they only know what is likely to happen next based on patterns.

For an insurer, a "black box" agent is un-insurable. You cannot price the risk of an entity that doesn't recognize its own boundaries.

This is where the design layer becomes a prerequisite for underwriting. I argue that trust is an engineering outcome achieved through Causal Delegation. This involves three core requirements:

1. Causal Authorization: The agent's actions must stay within a human-defined "Causal Space."

2. Boundary Clarity: The agent must possess a "Self-Awareness of Authority"—knowing exactly where its right to act ends.

3. Reversible Design: A "Circuit Breaker" mechanism that allows the agent to hand control back to a human when it encounters a "Causal Shift" (a situation its logic cannot resolve).

From an actuarial perspective, these are not just technical features; they are risk mitigation controls. Only when an AI's behavior is logically bounded can the "Residual Risk" become measurable and, therefore, priceable.

Finance: The Gateway of Action

Even with a perfect design, AI actions remain in a sandbox until they interact with the financial system. In the Agentic era, finance serves as the "Action Gateway."

Current verification systems (like OTP or SMS) are human-centric and fail in an automated economy. We are seeing a transition toward tokenized authorization, a concept mirrored in the recent collaborations between Google and Mastercard to standardize how AI agents interact with payment gateways. By issuing a restricted digital token, the financial institution constrains the agent's spending power within specific causal boundaries.

When the financial system recognizes an AI agent's "status" and its "stop" commands as valid legal instructions, the agent transitions from a demo to a commercial reality. But even then, who pays when the system fails?

The Insurance Endgame: Pricing the Residual Risk

This is where the Agentic Insurer finds its greatest opportunity.

Even the most robust design cannot eliminate 100% of uncertainty. In risk management, this is known as "Residual Risk." The true function of insurance is to transform these unbearable potential losses into a predictable, manageable cost: the premium.

As shown in the Accountability Pyramid, trust is built through three layers of structural filtering:

• The Design Layer resolves the logic of AI actions.
• The Financial Layer provides the real-time road rights and security gates.
• The Insurance Layer provides the final financial backstop.

Figure 1 - The Accountability Pyramid of Agentic Commerce: The three-layer structural filtering for building systemic trust in AI agents.

When the insurance industry can accurately price the behavior of an AI agent, it essentially provides that agent with a "Credit Certificate." It tells the world: "This agent is designed so rigorously that we are willing to back its failures with our balance sheet."

Conclusion: From Risk Managers to Credit Providers

The competition in Agentic Commerce will eventually shift from model capability to credit systems. The most powerful agents will not be those with the most parameters, but those that can clearly present their boundaries and complete the responsibility loop within an institutional framework.

Finance is the prerequisite for all transactions, and insurance is the backstop for unknown risks. When we complete the loop in these two most conservative and rigorous fields, we will have truly traveled the path from the reconstruction of action rights to the realization of trusted, systemic action.

We have moved beyond the stage of merely designing how AI should act. We are now in the business of backing those actions with the institutional credit that only insurance can provide.

For decades, insurance has existed as a discrete decision: a policy purchased in advance, reviewed infrequently, and activated only after something goes wrong — often long after risk has already materialized. That model was built for a slower, more predictable world.

Today's digital economy operates in real time, while most insurance products still operate on static assumptions. Transactions happen instantly, services are increasingly automated, and risk often materializes faster than humans can react.

In this environment, episodic protection creates a growing mismatch between how risk emerges and how insurance responds.

Embedded insurance is often described as a distribution innovation or a customer experience improvement. For insurers, however, its implications run deeper — affecting underwriting models, risk ownership, and how protection is triggered in the first place.

Increasingly, embedded insurance is evolving into a layer of digital infrastructure that enables real-time, AI-driven protection and allows trust to operate continuously in the background.

In this model, customer experience is not the goal. It is the byproduct of intelligence, architecture, and timing.

From products to systems of protection

The shift toward embedded insurance mirrors transformations already seen in other industries. Payments, identity verification, and cybersecurity all followed a similar path: visible, effortful processes became invisible capabilities woven into digital platforms. Users no longer "experience" payments or authentication, they simply trust that they work.

Insurance is now undergoing the same transition.

When protection is embedded into transactions, platforms, or services, coverage is no longer something customers must anticipate or manually activate. For insurers, this shifts protection closer to the moment of exposure rather than the moment of purchase. It becomes context-aware and event-driven, triggered when risk is detected, not when paperwork is completed.

Trip protection appearing at checkout or device coverage offered at the point of purchase were early signals of this shift. What's changing now is scale and sophistication. Embedded insurance is expanding beyond transactional moments into continuous protection models that adjust dynamically as behavior, environment, and exposure change.

This evolution fundamentally redefines what insurance does. Rather than transferring risk after the fact, insurance begins to function as a system that monitors exposure, interprets signals, and responds to risk as conditions change.

At the center of this transformation is AI-driven intelligence.

Traditional underwriting relies on static data snapshots and infrequent reassessments. Embedded insurance, by contrast, relies on continuous data ingestion (from transactions, devices, sensors, usage patterns, and behavioral signals) to evaluate risk as it evolves.

AI enables this shift by acting as a decision engine rather than a personalization tool — within constraints that insurers understand well, including explainability, auditability, and regulatory oversight.

These systems can assess exposure, inform pricing, and recommend or activate protection in near-real time, while still requiring governance frameworks that define when automation ends and human accountability begins. Coverage can expand during periods of elevated risk, pause when exposure drops, or adjust without requiring customers to renegotiate policies.

This is particularly visible in areas such as mobility, cyber, and on-demand services, where risk fluctuates constantly. But the implications extend across lines, from home and health to financial protection.

The result is not simply faster insurance. It is a different operating model where underwriting, pricing, and activation converge into a single, real-time process.

Why customer experience becomes the outcome

Customer experience has long dominated discussions around embedded insurance. But focusing too heavily on UX risks misunderstanding why embedded models work.

Customers do not want insurance experiences. They want certainty. And insurers benefit when that certainty reduces friction, disputes, and delayed claims activation.

When protection is always on, automatically triggered, and aligned with real-world behavior, the experience becomes effortless by design. Coverage simply exists when needed.

This is why embedded insurance adoption often outperforms standalone offerings. Not because it is marketed better, but because it aligns with how people already behave.

Good CX emerges when systems remove friction, reduce uncertainty, and deliver value without demanding attention. Embedded insurance succeeds because it minimizes the need for interaction while increasing alignment between real-world behavior and coverage intent.

Implications for insurers and ecosystems

Treating embedded insurance as infrastructure has significant implications.

First, it shifts where value is created — from policy issuance to risk intelligence, integration depth, and the ability to operate inside partner ecosystems.

Second, it requires new operating capabilities. Real-time protection demands explainable AI, resilient data pipelines, and governance models that balance automation with accountability.

Third, it changes how insurers engage with partners. Embedded insurance thrives in ecosystems with platforms, mobility providers, financial institutions, and digital services that already own customer relationships. Insurers must evolve from standalone providers into system collaborators.

Finally, it reframes the competitive landscape. As protection becomes infrastructural, insurers that fail to embed risk intelligence into digital flows risk becoming invisible or irrelevant.

The strategic question ahead

The most important question for the industry is no longer, "How do we sell insurance better?"

The questions are now, "Where — and how — should insurance operate inside digital systems?" and, "How do we architect trust in real time?"

Embedded insurance is not just a new way to distribute coverage. It is a redefinition of insurance's role in the digital economy — shifting from a product people buy to a capability systems rely on.

For high-net-worth (HNW) families, risk is no longer concentrated in property alone. In 2026, it extends across lifestyle choices, digital exposure and public visibility, often interacting in ways that amplify loss when something goes wrong.

At the same time, the insurance market itself has changed. Capacity remains constrained in many regions. Underwriting is more granular. Terms are tighter, and exceptions are fewer. The result is a new reality for affluent households: protection is no longer about optimizing premiums at renewal. We must help our clients make thoughtful decisions around what to insure, what to retain and where to invest in prevention — long before the market forces the issue.

That shift is already visible in client behavior. According to HUB's 2026 Outlook High-Net-Worth Survey, 25% of HNW respondents remain willing to assume more risk to save on premiums, down sharply from 39% just two years ago. The priority has moved from short-term savings to long-term predictability. For insurance professionals, this marks a fundamental change in the advisory role.

Risk Appetite and the Shift Toward Predictability

The risk clients are willing to assume is shifting from premiums alone to predictability. HNW families are asking, "What level of uncertainty am I willing to live with?"

That question shows up differently across exposure categories:

Property: Higher deductibles, wildfire exclusions and water sublimits are now common. Without documented resilience, such as defensible space, water detection, roof upgrades, households can find themselves carrying more risk. Thoughtful property decisions require advisors to stress-test deductibles and exclusions against real loss scenarios.

Cyber: Homeowner policy add-ons for cyber coverage often don't respond when a household faces a six-figure wire transfer fraud, crypto theft or AI-driven impersonation. A standalone family cyber policy, modeled after business coverage, can cover a fast-moving, high-severity claim.

Reputational and Social Risk: Visibility brings vulnerability. Social media incidents, harassment campaigns or AI-generated deepfakes can escalate quickly and trigger costs that an umbrella liability policy alone rarely covers. Reputational coverage allows families to cap potential fallout with PR response, crisis management or relocation.

Across all three areas, the pattern is consistent: risk appetite directly shapes policy structure and limits, while active risk prevention is critical to securing and sustaining coverage.

Advisory Roles Are Expanding

As underwriting becomes more data-driven, HNW clients expect their advisors to do more than just place coverage. They expect a more advisory approach to help identify and translate risk decisions into underwriting outcomes.

That starts with defining risk appetite clearly and operationally. Help your clients articulate what they are prepared to retain versus what risks they wish to mitigate or transfer, then ensure deductibles, limits and specialty programs reflect those choices.

It continues with identifying and managing risk as a continuing discipline. For HNW households, exposure rarely stays static. Changes in assets, lifestyle and visibility can materially alter loss potential, often faster than coverage is updated. Effective advisors anticipate these shifts and lead reassessments when risk changes.

Common trigger events include renovations, acquisitions, new drivers, increased travel or changes in digital presence. Independent risk reviews add credibility and help surface blind spots that increasingly matter to underwriters, such as:

• Undisclosed property changes, including major renovations or added amenities that alter replacement values
• Household complexity, such as staff, frequent guests or multiple residences, which can introduce additional liability exposure
• Behavioral risk, including teen drivers or high-frequency overseas travel
• Digital and reputational exposure, from online visibility and social media activity to crypto assets, wire transfer activity or public-facing roles

Most importantly, effective advisors turn prevention into leverage. Insurers increasingly expect proof of resilience before offering additional capacity or favorable terms. Documented mitigation gives underwriters something concrete to evaluate. When risk mitigation efforts are organized and communicated well, they strengthen negotiating power and reduce disruption at renewal.

A More Thoughtful Path Forward

In 2026, the advisor is called on to build a risk strategy that holds up under tighter underwriting and faster-moving loss scenarios. HNW families need advisors who can do more than simply respond to the market. They need partners who help them define risk appetite, identify emerging exposures and translate prevention into underwriting leverage. When risk decisions are careful and well-documented, they improve access, continuity and outcomes over time.

From underwriting and risk assessment to claims processing to fraud detection to customer service, the efficacy and turnaround times for these core insurance functions have, for decades, been contingent on expertise and the availability of human agents because insurers needed to manually investigate applications using static, rule-based systems. This often took days or weeks to complete, as the process involved significant human judgment. As for customers, they had no choice but to wait before a policy was issued or a claim was processed. Many insurers continue to operate under this model today. These fragmented views don't provide a complete view of the customer's journey.

But what has changed today is a consumer's digital appetite. This is why the need for a unified customer view that spans channels, products, and interactions has never been greater. It brings all insurance data together in one place, enabling insurers to deliver more accurate services faster. Here's why this matters now more than ever.

The High Cost of Disjointed Customer Interactions in Insurance

Personalized interactions and 24/7 brand availability unquestionably influence consumers' purchase journeys today. Unlike a few years ago, they have more options now and have experienced frictionless digital journeys. Amazon and Netflix are a few brands that stand out. Often, the great digital experiences these brands offer elevate expectations, making consumers very selective.

Sure, it would be unfair to compare these to a distinct, complex service like insurance, which must comply with evolving regulations while protecting sensitive information. But human beings don't operate in this manner. Studies show that thoughts, feelings, and behaviors influence how people buy, use, and dispose of products/services. For example, customers can inquire about the product/service on a social media channel and call the helpline number upon minor dissatisfaction. And shift to a competitor right away if the answer is unsatisfactory. Therefore, insurers must be fully prepared to engage customers at all times.

Amid this landscape, insurers still reliant on legacy systems will find it tough. These legacy apps are ill-equipped for the digital age. They give limited visibility into customer interactions. Also, sifting through multiple tools to gather information not only hampers agents' productivity and customer service metrics such as average handling time, net promoter score, and first response time but also affects a brand's long-term image.

Transitioning from disjointed customer interactions to a unified 360-degree customer view helps insurers meet heightened expectations. Rather than relying on separate apps, portals, or user interfaces for sales, billing, claims, and policy service, agents can access all these capabilities on a single platform. And policyholders can track their transaction history, get renewals updates, and submit claims online using self-service portals. This unified approach enables insurers to deliver the ultimate "Amazon experience" modern customers demand today.

Laying the Foundation for a Unified 360-Degree Customer View

Achieving a 360-degree view of a customer begins with a few key considerations, such as:

Incremental modernization:

One of the most asked questions by the insurers is: Which core system (policy, billing, or claims) should be modernized first? These systems are the foundation and modernizing them can help extend their life and position them to meet heightened customer expectations. Instead of a full-scale transformation, incremental, step-by-step modernization can be initiated. Once the pilot is successful, the solution can be rolled out at the enterprise level. Based on the results of this enterprise-wide adoption, a similar strategy can be applied to the remaining core systems.

Data management:

Asking the customer for the same information multiple times or storing information incorrectly can affect the customer experience. That's why the acquisition and capture of data, and its integration into the transaction workflow, are vital. Organizing and managing clean, deduplicated, governed, and up-to-date data in a centralized workflow can help insurers meet expectations more effectively.

Integration with core systems:

Building modern UIs is easy, but the challenge lies in connecting the front ends to the complex core systems that manage mission-critical transactions. Modern architectural features such as APIs, microservices, and cloud deployment can integrate with digital channels and CRM tools, making seamless digital experiences a reality.

Adopting agile systems:

The era of monolithic systems is over. Component-based architectures, "microservices", are the approach to building more flexible, agile, and adaptable systems. These can help enable faster time-to-market for new products, integrate new technologies such as AI and cloud computing, and dynamically cater to multiple channels.

Alternatively, insurers can opt for a powerful, centralized CRM platform. It consolidates policy, claim, customer, and agent data into a single view that helps teams track leads, issue quotes, manage renewals, update claim status, and more within a single platform.

Benefits of a Unified Customer View

This approach goes beyond simply collecting data. A unified 360-degree customer view ensures enhanced data quality, accessibility, and usability across the organization. Here are some of the advantages:

Enhanced personalization: There are multiple customer touchpoints: online forms, mobile apps, customer service, and even IoT devices. A 360-degree, unified view consolidates these insights, giving insurers a holistic visibility into customers' needs, behaviors, and preferences. This can help insurers build more personalized marketing campaigns (pitching tailored insurance products based on claim history), close deals faster, and navigate complex queries effectively.

Streamlined claims processing: The manual, paper-based claims process is outdated. Self-service portals/apps give policyholders a window to register/file a claim proactively. Integrating an AI chatbot can help reduce settlement time because AI can gather information faster and resolve queries based on historical patterns. These cases can then be transferred to human claim adjusters for one final review. All the while, the customer is kept updated on the status in real time. Complex claim requests that require unique human judgment, nuance, and expertise can be routed directly to agents, reducing turnaround time.

Smarter underwriting and risk assessment: Underwriters can access a comprehensive profile, including past claim history and driving behavior (e.g., telematics data from vehicles), to more accurately assess risk before issuing a policy. Good behavior can be rewarded with reduced policy fees, thereby increasing customer satisfaction and promoting road safety.

Detecting and preventing fraud: According to the Coalition Against Insurance Fraud, fraud accounts for an estimated $308.6 billion in losses each year in the United States alone, mostly in property and casualty insurance. This underlines surging fraud in the industry over the years. Application lies (hiding health issues/income), faking claims (inflated bills, doctored accident images), or creating scams are common ways to commit fraud.

A unified 360-degree customer view is aided by consolidating disparate data, enabling the identification of suspicious patterns and anomalies that might otherwise go unnoticed. Clubbing this with an AI-powered data quality monitoring solution that auto-corrects data, eliminates duplication, and fixes incorrect customer identification can further curb potential frauds.

The Bottom Line

Traditionally, insurance companies interact with customers in multiple separate business areas. As a result, customer profiles and complaints were stored in isolated databases operated by different units within the enterprise. But that era is long over now. This rigidity can frustrate customers and lead to a disjointed experience. As more digitally advanced businesses enter the market each year, competition is bound to get fiercer. The rise of insurance aggregators has already intensified the competition.

Because customers expect speed, convenience, and personalization across every interaction. Customers don't care much about a business's age or its market expertise. All that matters to them is how a business makes them feel upon every engagement. New-age startups have been able to tap into this consumer psychology effectively, thanks to their relevance and mindset. Creating a unified policyholder record gives carriers complete visibility, enabling them to deliver more personalized interactions suited for the digital age. Insurers that invest now will be better equipped to earn trust and loyalty, compete, and achieve long-term growth.