Paul Carroll

Change management has always been important, but we're now in a period of great change—that is often considered both complex and significant change. How has the importance of change management evolved, particularly in insurance?

Samit Bhandari Principal, PwC US

We've been driving, leading, and participating in large-scale transformations in the P&C insurance industry for 20 years. When you think about technology, we have become more experienced at leading these transformations. There's better tooling, more practitioners, certifications, accelerators, AI—the list goes on. That has helped to reduce several risks in the technology aspect. 

But it's still the same level of challenge in terms of the human aspect of what these programs are delivering, how they're changing the day-to-day work and operations, and what that means to a Customer Service Representative (CSR),[C(2]  to an agent, to an associate internally.

Paul Carroll

There's often a disconnect in technology adoption that goes unnoticed in business. A leader might mandate the use of AI, and employees will say they're using it—but are they really changing the way they do things, or are they just going through the motions to show compliance? How do you get people on the same page in a big technology transformation?

Samit Bhandari

First, just in terms of getting everyone on the same page, it's the fundamentals of what we've always said about getting stakeholders engaged—executive, top-down engagement early, right? The messaging from that level through various levels of the organization. And then just the strategic, tactical things—the change champion network and the communication early and often. When you bring in AI—we should be more nimble.

I do find in the tier two space, the carriers that we work with that are a little bit smaller are nimbler. They're better at embracing the transformation from the top levels of the organization and getting everyone on the same page to highlight that there is a significant effort being made by the organization. 

The tier ones can be a little bit less cohesive, just because they have multiple efforts going on. There may be a lack of alignment within one group to another group, or the change isn't happening to everyone at the same time. 

The second part of this is getting working product in the hands of the end users faster, such that they can get more aligned in the requirements they're specifying, what they're seeing, and then the changes they want to make. 

With the builder concept, we shouldn't have to take the waterfall approach—holding workshops, gathering requirements, then shutting that off and moving sequentially… so on and so forth. We’re able to showcase how we are doing things now, obtaining that builder mentality and increasing the number of engineers so we can take those requirements in real time and turn them into results.  

Are we there yet? No. But that path is becoming much more realistic. 

Paul Carroll

What do you see companies doing wrong about change management?

Samit Bhandari

It can’t just be about the rah-rah, the change champion network, the T-shirts, the slogans, and getting the sentiments higher. That's an aspect of change, and we need to do the communications. But companies don’t always start planning soon enough, and the budget may change throughout the transformation journey in a program. Some initiatives may not be considered a priority up front nor do they have enough budget, time and commitment throughout the program.

Companies may also underestimate what they should do on Day Two, after they’ve had the Big Bang. I was with a few executives for a tier-two carrier in the Midwest recently[C(5] , and we talked about how getting to the pilot was relatively straightforward. But the subsequent rollouts and expansions were a little bit more difficult than they had expected. And then the amount of feedback that came in immediately after they went live was even beyond their expectations.

What they often get wrong is not knowing that this is going to be a continuous and iterative process. The backlog of things to change, enhance and tweak is going to grow the minute you put something in the hands of your associates and your agents. You should have processes up front—both from a change standpoint and a delivery standpoint. From a change standpoint, to expect the amount of feedback that's going to come. But then from a delivery standpoint, to be comfortable in saying, "Hey, we're not going to knock all that off and react within 30 days, but we're going to systematically respond and talk about how we can improve continuously."
 

Paul Carroll

What are the biggest changes you’ve seen over the years?

Samit Bhandari

Early in my career, we spent so much time working with the C-suite. We built strong business cases that quantified why we were doing what we were doing, what the impetus for change was, and what the benefits would be. We went as far as having the different P&L leaders, whether it's claims or policy or billing or actuarial, sign up for the benefits the new platform would have for their constituents.

We've really moved away from that. The attitude became, “Well, we have to do something to keep up with the “Joneses”.” Now, the idea is, “Who needs a big core system from Guidewire  (We no longer have a JBR with Duck Creek so we can’t mention them. We could either reword the sentence to only include Guidewire OR just leave it at “Who needs a big core system.”? We can just take an Insurtech SaaS play and get the work done in months.”

We've unfortunately moved away from taking the time up front to think about the impact a new platform can have on end users or the top line of the organization. I think we should come back to that planning a little bit because it anchors the program in the proper tenets, and it can help assess whether you can just do this in months, given the regulatory complexities.

Once you hear from end users, you realize it's not that simple to just pick up a black box, tinker, and come back with a platform that can addresses various state rules and regulations, coverage needs and DOI expectations. 

Paul Carroll

What are some red flags that companies should watch for, to see they’re doing change management wrong?

Samit Bhandari

Watch out for comments like, "I miss the old system," or "The old platform didn't do this." You may get some of that, but you've really got to have your ear to the ground. Who is saying it? How much is that sentiment catching fire?

Another red flag is when business engagement and user acceptance are lacking. Users should be asking to see the new system early so they can see the results. They should want to get their hands on it and feel excited about what's going to come, because they're in it day to day.
A third warning sign is when you don't have alignment among the leadership. Somebody should be empowered to observe and then make tough decisions, to maybe say, "Hey, maybe this person shouldn't be an executive sponsor," or "We need them to shift how they're operating." Even if lack of alignment doesn't cause the program to fail during the implementation journey—after the fact, it could be just as damaging if people cast shade on what was done. "Oh, we spent too much money on that," or "We overengineered this process," or "It took too long." Leadership should wear the badge throughout the journey and even beyond.

Paul Carroll

What are three or four keys to having a successful change management program?

Samit Bhandari

I can easily flip some of those failures around into successes. Certainly alignment from the top down. The change champion network is important. Individuals should feel excited about what they're seeing. The feedback network should be working well, so you can identify pain points and then dig deeper.

Having a balance of stakeholder engagement monitoring and progress monitoring can help you identify the proper level of adoption metrics early on. 

The last thing is that awareness that it's going to be a continuous evolution and change is continuous. Even after we go live with the first pilot or the next set of states or the portal, there's going to be feedback. How we respond to it and react to it is more important than whether we got it right out the gate.

That last one is often one of the more important things to remember because a balanced mindset of "if we don't get it right, the sky isn't falling." Let's get back together as a team, a cohesive team, and respond. I was in an executive sponsor meeting this morning, and we talked about being 5% over budget. We talked about having 30 open risks[C(6] . We talked about seeing more inflow of defects at this juncture than we wanted. We had the whole C-suite on the line, and everyone was just processing the information, making sure we have the proper rigor around it, and saying, "Alright, here's how we can tackle the issues with the necessary mitigation plans. [7]

The beauty is that when the approach works well, you roll out, go live, and start changing the day-to-day operations of everyone across the different stakeholder communities, internally and externally.

And we're seeing more programs making progress. You've heard that 80% of projects fail[C(8]  (my risk team is questioning this statistic and is asking if we can confirm/source it somehow?) and all that, but that's not the case anymore in this space. The speed to market is often top of mind along with the ability to make changes, identify top-line growth by introducing new products, helping deliver significant changes faster. [9] 

Paul Carroll

Thanks, Samit.

 [C(1]Principal, PwC US should be added

 [C(2]Please spell this out in the first instance unless it is an industry known term by the audience

 [3]Please validate the '20 years' experience claim before publication and confirm the intended attribution.

 [C(4]Not necessary in this instance because the partner is speaking to his personal experience.

 [C(5]The GEP/GRP/LCP/IRP and EL, when necessary, must confirm whether the engagement letter includes restrictions regarding sharing information about the project in public communications, even if the client is not mentioned by name. They must also confirm we are not violating client confidentiality by using specific client or engagement information in the document (even when the client’s name is masked).  

 [C(6]Please confirm these statistics have been vetted and confirmed by practice leadership.

 [7]Please validate the 5% over budget and 30 open risks figures against the underlying engagement records before publication.

 [C(8]Confirm all cited information is properly sourced, independently vetted, and drawn from authoritative, authenticated sources. Check any AI-assisted or AI-generated content against factual evidence to confirm it is accurate, supported, and free from hallucinations or unsupported claims. Also confirm that use of cited materials complies with applicable Terms of Use or licensing restrictions, including whether written permission is required, and present citations on screen or in footnotes using a standard format such as APA or MLA. PwC communications, including blogs, thought leadership, and similar materials, are generally considered “commercial use.” Not all internet content is public domain or freely available for use. Refer to the AI business rules for additional information.  

 [9]Please substantiate the '80% of projects fail' statistic with a current, credible source or remove the percentage.

While we've suddenly landed in the middle of a debate about whether AI may be about to obliterate the human race, I hark back to a profile I did for the Wall Street Journal about a brilliant AI and robotics researcher from Carnegie Mellon named Hans Moravec.

The focus was his provocative idea that humans would be able to download their brains — their entire consciousness, their full personality, an exact replica of them — into computers, which could then teleport to any spot in the universe or spawn an infinite number of what Moravec called "mind children." 

The memorable headline was:

Good News: You

Can Live Forever;

Bad News: No Sex

I asked Moravec how long it would take for his vision to be realized. "Oh, a long time," he said. "Maybe 25 years."

That was 35 years ago.

So I'm not going to worry much for years about all the talk of impending doom. Timelines on sci-fi-like change tend to be way, way off. But, under the radar, there are plenty of AI issues that should be major concerns right now, including for insurers.

Let's have a look.

I'll start with Bill Gates's recent manifesto, which, among other potential dangers from AI, called out the prospect that AI will supercharge the work of malign actors, perhaps leading to bioterrorism, massive cyberattacks, and more. While we can discuss the potential long-term threats to humanity from AI, these are the kinds of threats I think we need to focus on today. These threats are already being pursued, whether by individuals looking to extort massive amounts of money or by nations looking for weapons in an increasingly belligerent world, and AI clearly provides exponentially more computing capability.

The MIT Technology Review goes into detail about how AI might produce a devastating bioterrorism attack: "A bioweapon might be a highly lethal virus that targets people according to their genes. It could be a fungus that wipes out a crop and causes food insecurity. Perhaps it would be a tasteless, odorless toxin that could be slipped into a region’s water supply, undetected....

"Today, AI bots can answer questions on topics spanning all realms of science. Anyone can use large language models trained on the knowledge and experience of 'almost every scientist who ever lived on this planet,' says Dunja Sabra, a biosecurity researcher at the University of Hamburg in Germany. Those models can provide instructions and video training on how to conduct experiments.

"Combine that with advances in biotech that have made gene editing and synthetic biology tools much more accessible (the “DIY biology” movement has already enabled many people to set up labs at home), and you’ve got a potentially very dangerous situation."

Wired, meanwhile, warns about all the vulnerabilities in software that AI bots are finding, and it's not hard to imagine how those weaknesses could be exploited. In July, hackers, thought to be based in Iran, disrupted 30 municipal water systems in Minnesota, and you can be sure Iran will ramp up attacks as fast as it can. North Korea, China, Russia, and other countries could stage similar, small attacks or could even try to shut down electric grids and stall commerce by using weapons of not-quite war.

Cyber attacks could easily lead to massive business interruptions of the sort insurers routinely cover and could increase geopolitical risks of every flavor.

Businesses and governments understand that the bots are making them vulnerable and are working as fast as they can to plug the holes, but they won't find all the holes, at least not right away. And you be sure that some hacker cartel or foreign government is storing up what are known as "zero day" vulnerabilities that can be unleashed on unsuspecting businesses and societies. 

There is also massive potential for operator error as AI is deployed more broadly. For instance, the plan to use AI in air traffic control, just now going live for DC-area airports, strikes me as an accident waiting to happen. I hope everything goes smoothly, but the potential for trouble is so great that insurers and everyone else should be wary. Air traffic control hasn't exactly acquitted itself well lately, and AI tends to amplify flaws by making everything happen faster. 

That's where I think the focus should be over at least the next couple of years, both for insurers and for society writ large: on the potential bio, crypto and other deliberate, organized attacks that AI makes possible from bad actors who would profit from those attacks, as well as on the potential for catastrophe as AI gets more involved in mission-critical efforts. 

Yes, there is always the possibility that the search for superintelligence could create an AI that will go rogue and do indescribable damage to the whole human race, for no apparent reason, so government officials should be erecting guardrails.

But business is already circumscribing what AI can do. Insurers certainly are. They've realized that justifying a decision with "the AI says so" won't fly, so they're requiring that every decision be explainable and are greatly limiting what actions an AI can take without explicit human permission.

The whole superintelligence debate has so many dimensions even beyond the technical ones — there are political elements, issues related to business models, massive public relations concerns, etc. Here, for instance, is a column in the WSJ that argues the whole apocalypse debate is an attempt by AI's leading developers to duck responsibility. 

The issue reminds me of Winston Churchill's description of the Soviet Union after it allied with Nazi Germany in 1939: "a riddle wrapped in a mystery inside an enigma." And I'm supposed to understand the flow of technology revolutions, having followed them for decades.

Fortunately, I think we can wait to puzzle out all the implications of this AI revolution — as long as we don't take our eye off the ball on the imminent threats it creates.

Cheers,

Paul

The Problem

Non-flood damage from water costs homeowners billions a year, and insurers pay $15 billion in claims, just in the U.S. One in 60 U.S. homes suffers water damage each year, and the average claim is nearly $14,000—which doesn’t even include the deductible the policyholder pays, or the huge hassle they go through. Typical solutions involve expensive shutoff valves or a host of small sensors placed throughout a home, but providers have struggled to make a compelling case that their programs deliver an ROI.

The Solution

LeakBot takes a different approach, one that begins before a leak occurs and stretches to a solution. The approach starts with a LeakBot sensor that policyholders clip to the main water pipe in the house. The sensor detects any leaks anywhere in the house that exceed a teaspoon per minute. LeakBot’s app walks a policyholder through steps to find leaks, then, if necessary, sends one of its plumbers to visit the house to find any remaining ones, at no charge. The LeakBot plumber documents the repair, giving carriers visual validation of every claim saved.

The approach finds even tiny, hidden leaks weeks or months before they can cause significant damage. In 2025, LeakBot completed 7,000 leak repairs, 1,470 of which revealed water damage that had already begun.

More than two dozen carriers provide the LeakBot solution for free, paying LeakBot $5 a month per house, all in. LeakBot reduces the number of water loss claims by 60%, generating a significant ROI for its carrier partners.

LeakBot covers the full loop, detection through documented repair, which makes it the only true end-to-end platform in this space.

The Documentation

LeakBot has produced a white paper that clears the actuarial bar for detail and reliability. The paper draws on hundreds of thousands of device-exposure years. It uses cohort-level (not anecdotal) evidence. It relies on auditable repair records — rather than programs still running on pilot-stage promises.

Sponsored by Leakbot

Over the past several years, digital musculoskeletal (MSK) care has evolved from an emerging concept into a validated healthcare category. Companies such as Sword Health and Hinge Health have demonstrated that technology-enabled MSK solutions can attract significant investment, expand access to care, and create new models for managing one of healthcare's largest cost areas. Sword Health has reached multibillion-dollar private valuations, while Hinge Health successfully shifted from a highly valued private company to the public markets, demonstrating strong investor confidence in digital MSK care models.

Their success has proven something important: employers, healthcare systems, and insurers are ready for technology-enabled approaches to musculoskeletal health.

The need for innovation has never been greater. Musculoskeletal disorders represent one of the largest drivers of pain, disability, healthcare usage, and lost productivity. According to research published in the Journal of Medical Internet Research, annual healthcare spending associated with MSK conditions in the United States is estimated to be approximately $300 billion. The impact extends beyond direct treatment costs, as MSK conditions are often associated with other chronic health challenges, including obesity, diabetes, cardiovascular disease, arthritis, frailty, and falls. The true healthcare and societal burden is therefore significantly broader than the cost of treatment alone.

But the next question is even more fundamental:

How do we move from delivering care more efficiently to understanding changes in human function earlier?

The next evolution of healthcare risk management may not simply be better treatment after a condition develops. It may be the ability to objectively measure functional changes before they progress into disability.

This shift is increasingly reflected in healthcare research priorities, including efforts focused on aging, musculoskeletal health, digital health technologies, and artificial intelligence. The National Institutes of Health and the National Institute on Aging are emphasizing the importance of technologies capable of objectively measuring functional changes, understanding age-related decline, and supporting approaches that preserve mobility and independence.

Maintaining mobility and physical function is a fundamental component of healthy aging. Changes in strength, muscle activation, movement quality, balance, and physical performance can contribute to increased risk of falls, frailty, disability, loss of independence, and reduced quality of life.

Yet functional decline is often gradual. A person does not suddenly become frail. Changes may occur over months or years before they become apparent through traditional healthcare encounters.

Today, functional status is primarily evaluated through episodic clinical assessments, performance-based measures, and subjective reporting. These approaches provide valuable information, but they offer only limited snapshots of an individual's health and may not capture subtle changes in physiological and biomechanical function over time.

Healthcare has become increasingly sophisticated at measuring disease. The next opportunity is developing the ability to measure changes in human function. Wearable technology has already transformed personal health monitoring. Individuals can track steps, heart rate, sleep patterns, and activity levels. However, the next generation of digital health technology must move beyond activity tracking toward functional intelligence.

The question is no longer only:

"How much did someone move?"

The more important question is:

"How did someone move?"

Mobility is influenced by complex interactions among muscle activation, coordination, biomechanics, range of motion, and movement quality. Technologies that integrate physiological sensing, biomechanical measurement, and artificial intelligence have the potential to provide deeper insight into individualized functional patterns.

An AI-enabled platform integrating technologies such as electromyography (EMG), inertial measurement units (IMUs), and advanced movement analytics could establish a foundation for objective digital measures of mobility, functional aging, and early changes that may affect independence and quality of life.

The challenge in healthcare is no longer simply collecting data. The challenge is transforming increasingly complex streams of information into meaningful insights that can support better decisions.

Artificial intelligence offers the opportunity to analyze large amounts of physiological and movement information and identify individualized patterns that may not be visible through traditional assessments. The goal is not to replace clinicians, but to provide clinicians, insurers, employers, and individuals with better information to support earlier and more personalized decisions.

AI-enabled healthcare has the potential to move the system away from simply reacting to decline and toward identifying meaningful changes earlier, when there may still be an opportunity to preserve function.

For insurers, this represents a potential transformation in risk management. The traditional healthcare model has largely followed a reactive path: a condition develops, treatment begins, and a claim occurs.

The future opportunity is a more proactive approach in which functional changes are identified earlier, personalized strategies are developed, and risks associated with decline may be reduced before they progress into costly healthcare events.

Objective measures of functional health could support earlier identification of risk, more personalized care pathways, improved management of chronic conditions, support for aging populations, and value-based healthcare models focused on maintaining function rather than simply treating decline.

This shift is especially important as populations age. Aging does not automatically mean disability. Many individuals remain active, engaged, and independent when changes in mobility, strength, and function are recognized early and appropriately addressed.

Preserving function is not only a healthcare goal; it is becoming an essential component of managing healthcare costs, improving outcomes, and supporting a more sustainable healthcare system.

The future of healthcare risk management will require a fundamental shift in how we define prevention. Prevention can no longer focus only on identifying disease after it develops. It must also include understanding changes in human function before they become disability.

The most valuable healthcare technologies of the future may not simply tell us what happened after a problem occurs. They may help us understand what is changing — enabling healthcare systems, insurers, employers, and individuals to act earlier to preserve mobility, independence, and quality of life.

The future of healthcare risk management will not only be defined by how effectively we respond to disease and disability, but by how well we can identify change earlier, preserve function, and help individuals maintain healthier lives.

There is always going to be another new platform, another integration, and, increasingly, another conversation about AI. But the value of technology isn't in simply having more of it. It's how well it connects and whether insurers get better information when they need it.

At a time when AI is redefining how the insurance industry will function for the next 30 years, a converged environment unifies underwriting, policy administration, billing, claims, and customer engagement on one operating platform, rather than a chain of systems handing data to each other. A claims signal reaches pricing while the claim is still open, informing the next risk assessment in real time. An underwriting decision immediately shapes how service and retention teams treat that customer afterward. Information moves in near real time, following what the business needs rather than the limits of the original system architecture.

The Case for Converged Platforms

Across this platform, AI becomes part of how the workflow runs. An agent reviewing a claim has policy history, the underwriting file, and prior customer interactions available at the same moment, because they live in the same context rather than three different logins. That context lets AI move from flagging an exception to resolving it, and from recommending a next step to taking it, with a human reviewing the outcome afterward. The reasoning behind each action stays visible and auditable, so the people accountable for the outcome can see exactly how the system reached it.

The practical benefits show up quickly. Teams configure new products within a shared environment instead of rebuilding them system by system, so what used to take months of coordinated releases now takes days. Renewal pricing, fraud screening, and service routing draw on the same live data rather than nightly batch exports, so all cross-referenced with each other and reflect the real-time state of the business.

The autonomous AI platform we are describing doesn't mean anyone should abandon the judgment that experienced underwriters and claims handlers bring. A converged environment pairs automation with quality gates. An organization can route a routine renewal straight through and hold a complex commercial risk for human review, using the same data for both. The goal is to make sure the people making the decision are working from the full picture, instead of whatever fragment their siloed, legacy systems offer.

Rethinking Process Design in an AI-First World

It is important that companies should think carefully about the shape of their AI models. There is a natural tendency for people to focus on human-driven, effort-based, bottom-up processes and then apply AI efficiency factors to achieve better productivity results. I believe that approach is fundamentally flawed as it quietly assumes the old process as the correct baseline, when the old process is the very thing that should be questioned.

Companies should work back from the assumption that every function is AI-automated and design that as the starting point, not the improvement point. Then justify, line by line, the human effort that is genuinely needed to be added back: the judgment calls, the client-facing decisions, the regulatory sign-offs, the things an autonomous AI agent demonstrably should not be permitted to do.

The best approach is to question the requirement itself and what value it delivers to the business; wherever possible, delete the step entirely rather than automate it, simplify the real requirements that survive, and only then automate the complete end-to-end process with the minimum human intervention possible. There is no business value in automating a step that should have been removed completely, as this is the most expensive mistake that organizations make.

The clearest sign that this transformation is working comes down to a simple test. Can a service representative see a customer's full history the moment the call connects, without transferring them or putting them on hold while three systems are checked in sequence? Can an underwriter check how similar risks have actually performed against what the rate manual predicts? Can a claims handler settle a straightforward loss the same day it's reported, because the verification, the policy check, and the payment authorization all draw from the same source of truth?

That is the standard worth building toward. For companies where coherence is increasingly a transformational priority, my advice is to build an environment where every part of the business can see what every other part already knows, and where AI has enough context to act on that knowledge instead of merely describing it.

The NAIC's AI Risk Evaluation Supplement is the questionnaire regulators will draw on to examine how insurers govern their AI. It asks whether a model's outputs were tested for accuracy, but only for the models a carrier itself rated high risk. When a customer-facing model gets a coverage question wrong, the policyholder acts on the wrong answer. 

That is the exposure a high inherent risk rating exists to capture. Rate one low instead, and the inquiry ends there. The model keeps answering coverage questions, the answers keep reaching policyholders, and nothing requires the carrier to check whether they were right. Few carriers check on their own.

What a low rating leaves unchecked surfaces later, in a claim file rather than an examination. Misrepresenting policy provisions is a listed unfair trade practice in nearly every state, and a dated coverage statement the policy contradicts is what a misrepresentation claim is built from. The customer who heard "you're covered," paid for the repairs, and then had the claim denied is the one who brings the complaint.

When that complaint arrives, a court will ask how often the model's answers were checked and what the checks found. Errors and omissions underwriters, who now ask about AI at renewal, want the same answers. A carrier that rated its model low will have no file to produce. There is no record of what the model said and no comparison against the forms in effect at the time. 

What it will have is a self-attested risk assessment concluding that the model was low risk. Plaintiffs' counsel look for exactly that pairing: a carrier that never checked, and a document explaining why it did not have to.

How the rating works

The rating is set early, in an inventory. Each carrier lists its AI models, describes what each one does, and assigns each an inherent risk level. Only the models rated high move on, and there the questions turn specific: were the outputs tested for accuracy, how is performance monitored on a continuing basis, and how is the model reviewed against unfair trade practice and claims settlement laws? 

Models rated moderate or low do not reach those questions, and a regulator reading the inventory has discretion to stop at the rating and request nothing further about the model. That is sensible design, since regulators cannot examine everything. It also makes one self-assessed rating the single point of failure for whether a model's accuracy is ever examined, and the carrier is the one holding the pen.

How a customer-facing model goes unexamined

Two judgments decide whether a customer-facing model is ever examined. Does its output reach a consumer, and how much risk does it carry before controls? A carrier can get either one wrong without meaning to.

Destination settles the first question, not who speaks the words. The supplement's background section covers decisions and actions “made or supported by” AI, and a coverage answer that reaches a policyholder is one of them. The pitfall is the supplement's support category, defined as a system that provides information without suggesting a decision or action, which is not counted as having direct consumer impact. A model that drafts a coverage answer is not just providing information. It is supplying the decision the representative delivers, and the support definition excludes exactly that. But because a representative delivers the answer, the model looks on paper like one that only informs an employee, and that resemblance is what puts it in the wrong box. Filing it as support keeps the model out of the count regulators use to decide what to examine, and out of any question about whether its answers were right. Those answers still go out to policyholders, unchecked.

A model that is counted can still be rated low by mistake, and the same representative is usually why. Most carriers put two guardrails around a model's answers. The representative is expected to check it before it goes out, and the platform runs its own internal check against policy language. Both are controls, and because they are built into the process, crediting them when rating the model is an easy mistake to make. The supplement asks for inherent risk, the risk the model carries before any control is applied, and the word "inherent" is easy to read past. Under the supplement's own terms, the representative and the platform check belong in the residual rating, after controls. A carrier that credits them in the inherent rating has rated its model lower than the supplement intends, and taken it out of the testing questions in the process. Both judgments are already recorded, one line per model, in the carrier's own inventory.

Why the cheaper rating costs more

Rating a model low is the cheaper answer. It closes a line on the inventory, raises no question the carrier has to answer, and creates no obligation to find anything out. Rating it high creates recurring work: finding out whether the model's coverage answers were right and continuing to do so while the model runs. The pressure runs one direction, and nothing in the supplement pushes back. The saving lasts a quarter. The gap it leaves is permanent. That work cannot be backfilled later, because the evidence exists only while the conversations are happening, and controls do not close the gap. A representative who catches a wrong answer fixes that conversation and leaves no record of it, so even a carrier with an excellent review process cannot separate an isolated error from a model-wide one. It also cannot see drift. Models do not hold still after launch. They get updated, the policy forms they draw on are amended, and the questions customers ask shift. A test run before deployment describes the model that was deployed, not the one answering calls this quarter. What a low rating costs is a year of conversations nobody measured, and nobody can go back and measure.

I cannot tell you how often AI models give wrong coverage answers, and neither can anyone else, because almost nobody is measuring. The draft is open for comment through Sept. 29, with a vote on a later version expected in November. The questions are being written now, and the evidence they ask for can only be gathered before they are asked. Rating a model low does not change what it told a policyholder. It only changes whether anyone was looking.

What if your computer didn't just help you type or access YouTube but acted like a smart assistant instead? That's what we broadly call agentic AI. Some might think AI is here to help us improve productivity and reduce costs, but the change is much deeper (Afanasyev, 2026). It defines what the insurance industry would be in five years and how your organization should adapt.

Asia's First-Mover Advantage and Early Missteps

Asia is the first-mover in AI adoption among financial institutions, compared to developed markets often hindered by tech debt and bureaucracy. The learnings here uncover that, for the industry, agentic AI is a change in the "rules of the game."

Asian financial institutions originally rushed into Agentic AI by treating it like a project focused on multiple use cases, prioritizing 'impact vs. feasibility'. This approach targets quick wins, assuming the organization is the only player implementing AI and hence making the journey risky and expensive. No surprise to see both academia, MIT (Challapally et al, 2025) and consultants, BCG (Apotheker et al, 2025), report that 95% of organizations struggle with Agentic AI. Such organizations miss that their customers and partners are also deploying AI Agents (Citrini and Shah, 2026).

When Productivity Gains Backfire: The Email Case Study

What happens when Agentic AI use cases are assessed without context? Take AI for emails, a productivity use case deployed three years ago, which used AI to help write emails or summarise the received emails. It seemed like a win but lacked context; certain employees used AI Agents to imitate work and secure visibility by generating volumes of emails. AI-generated content is hard (with some researchers claiming impossible) for a human or an algorithm to distinguish from genuinely important messages. The recipients would have to apply AI Agents to search for information. Both senders and receivers myopically reported AI bringing gains, although the insurers likely deployed a use case that resulted in miscommunications and productivity drains.

The New Reality: Everyone Has AI Agents

The right approach for an insurer deploying AI is to accept that others are doing the same, so focusing on internal productivity may not be enough. Think about claims. Your policyholders' and providers' AI Agents are scrutinizing policies and preparing documents to maximize approval. Recall your dental insurance policy, which offers reimbursements only if certain symptoms, such as tooth pain, are present. The AI Agent ensures that the symptom is stated, even when the policyholder visits dentists for routine checks.

In underwriting, similar to how Mythos from Anthropic identifies cybersecurity vulnerabilities (Bloomberg, 2026; Azhar and Williams, 2026), your customers' agents might be identifying gaps. Insurers whose underwriting processes do not handle AI-generated applications may end up underpricing risks. For wealth management, why would a customer having access to LLM models pay advisors who use generic LLMs for generating ideas?

Think about distribution, where an insurance broker deploys AI Agents to identify and message prospects. The competing brokers are doing it too, leading to overwhelmed prospects who might avoid purchases altogether.

The latter is highly illustrative and applies in reverse to a financial institution thinking about its agentic strategy. Previous breakthroughs, such as electricity and the internet, accelerated business but the ability to make intelligent decisions broadly remained intact. AI Agents generate volumes of persuasive, credible content but the overload reduces decision-making ability.

The "Business First" Approach: Three Pillars for Success

Asian financial institutions concluded that to succeed, they need to shift to the "Business First" or "Applied AI" approach, which combines three views—Business domain, Transformation, and Technology—under a single leader (Afanasyev and Milind, 2026). McKinsey used to name leaders with such skills as "Advanced Analytics Translators" and, since the early AI days, has been urging organizations to grow leaders who "ensure that organizations achieve real impact from their analytics initiatives" (Henke, 2018).

Pillar 1: Business Domain — Reimagining Market Forces

The Business domain reimagines how market forces will change, how the industry will provide value, and what an organization's role will be in the long run. This is the least-discussed pillar and precisely what puts organizations at risk.

The majority of Asian organizations are privately controlled, meaning limited dependency on reporting cycles and an ability to prioritize long-term needs. Rapid economic changes over the past two decades have led them to embrace practicality and challenge the status quo. For instance, in developed markets with high labor costs, many insurers seem to be prioritizing Agentic AI technology to improve efficiency and cut costs. Most Asian markets are, however, known to have low labor costs, and local institutions have rightly questioned us about deploying AI for just operational efficiency in the regional context. However, young people, the largest segment of Asia's population, adopt AI Agents for their financial needs the fastest. This has accelerated economic changes not yet noticeable in developed markets.

How Generative AI Disrupts Insurance Fundamentals

We have discussed how Generative AI disrupts signaling theory, which is foundational for modern insurance. In some cases, the policy buyers may know more about the likelihood that they will suffer a loss than the insurance company. The 'market for lemons' theory shows that only the ' worst ' buyers (expecting larger, more likely losses) may end up interested in a policy. Signaling theory explains how parties with superior information (actors, for example, policyholders) convey attributes to parties with less information (decision-makers, for example, policy underwriters) to overcome information asymmetry. For the industry to function, credible signals—think medical records for life insurance—must be costly to imitate. Generative AI has empowered any actor to generate strong signals, disrupting the fundamentals of risk-taking, with empirical studies already demonstrating an erosion of trust.

How can actors credibly signal quality when signals can be cheaply fabricated? The answer, we believe, is grounded in mechanism design, the economics discipline concerned with a decision-maker designing the rules so that rational actors reveal truthful attributes even when their interest might be in using Generative AI. The Applied AI leader has to possess deep insurance domain knowledge and also a practical level of academic perspective. Such a leader analyses disruptions to come up with a view on what the organization's role would be. The outcome could be that the value proposition should be in risk prevention instead of risk pricing; changing target customer segments; vertical integration bringing certain services in-house; or leveling AI to address moral hazards.

Pillar 2: Transformation — Rethinking Processes and Culture

Transformation should focus on agility, changes in processes, decision-making, and human roles. "A growing number of studies reveal that human–AI systems do not necessarily achieve better results than the best of humans or AI alone" (Vaccaro et al, 2024). Many organizations ground their AI performance evaluation on efficiency, accuracy, or productivity. However, "nowadays, productivity gain is no longer the single evaluation criterion. In many instances, computer systems are expected to enhance our creativity, reveal opportunities and open new vistas of uncharted frontiers" (Avital et al, 2009).

Legacy institutions have to unwind certain cultures religiously followed for years. Certain advancements have ensured that employees follow rule books and processes prescribed by management. As a result, many organizations operate as factories, with processes equivalent to production lines of the early 1900s. Employees and mid-management have limited or no discretion outside of what is prescribed. This practice might have been appropriate earlier, but the Agentic AI economy is unpredictable; rule books and processes become outdated in the blink of an eye.

Pillar 3: Technology — Building the Infrastructure for AI Agents

The Technology view is most discussed and depicts how non-human resources are augmented by AI Agents. The learning from Asian progress in Agentic AI is a need for Applied AI leaders to take a broad view on financial industry technology and ground hot topics, such as tech debt, data quality, MCP and A2A, Agentic AI workflow designs, etc.

Asia's Unique Technology Landscape

Asia is home to 5 billion people—about 10x of Western Europe and US populations combined—with interactions among various ethnic groups who have their distinct languages, cultures, and business styles. Asia accounts for about 50% of non-cash transactions globally (Capgemini, 2025), with most countries, including India and China, already cash-free and unimaginable GDP growth in developed markets. Asian institutions deal with unprecedented transaction volumes and diversity, so scale and agility have traditionally been prioritized over politics and offering sophisticated products. Moreover, many overly complicated software products adopted in developed markets for the past two decades found little relevance in Asia.

Before LLMs and Agentic AI, the Asian tech ecosystem had been focused on developing local software solutions to handle volumes. Native software providers have mastered agentic workflow designs using Directed Acyclic Graphs with state management, allowing agents to "Plan, Act, Observe and Reflect". MCP, the "USB-C for AI", has landed in Asia as a standardized interface, allowing agents to pull real-time context from sources like SQL databases, CRM systems, and core banking. For developed markets, MCP would solve the chronic pain point of tech debt by creating an abstraction layer over legacy core systems. Complementing this is the A2A protocol, which facilitates peer-to-peer coordination, and AP2, empowering agents to do financial transactions with or without human presence.

Architectural Challenges: Making AI Agents Work Together

Most legacy systems were architected for workflows that functioned adequately for rule-based operations. Meanwhile, Agentic AI demands open interfaces where workflows are not hardcoded and any given process can be decomposed, assigned to an agent, and reassembled. Organizations are adopting AI capabilities in separate departments and in relative isolation. The containment limits risk, allowing teams to demonstrate value; the actual issue is how these modules function as a coherent whole.

Agent loops, where Agent A triggers Agent B which re-triggers Agent A, require modern architectures to include circuit breakers that terminate chains exceeding defined depth or duration thresholds. Agent disagreements surface when two agents with complementary mandates reach different conclusions. Authority boundary violations occur when agents act beyond their defined scope.

Data Governance and Schema Management

A schema-driven model is recommended. Insurers should adopt a master schema configurator that allows users to govern agent behavior without code. Insurance is challenging in the volume of its unstructured data. Decades of paper-based documentation and inconsistent digitization have left organizations with data states that cannot be queried in real time. For Agentic AI to function, three areas are foundational. Vector databases enable semantic search across document corpora, allowing context retrieval. Structured extraction pipelines convert unstructured documents into queryable records. Master data management maintains reference datasets which can be used for real-time lookup, matching and adjudication.

Regulatory Safeguards: Ensuring Accountability and Control

As regulators, we are of the view that autonomous systems making customer-impacting decisions must be auditable, explainable and interruptible (IAIS, 2024). To ensure agents do not overstep authority, safeguards must be included. Confidence scoring requires that every AI output carry a numerical confidence estimate, with automatic escalation to human staff when results are low. Mandatory review workflows gate high-stakes decisions behind human approval that cannot be bypassed. Continuous feedback loops mean that each correction trains the models. Comprehensive audit trails provide thorough records. 

A Five-Step Modernization Roadmap

A five-step modernization approach is required:

Step 1: Start with structured discovery. Most attempts fail because organizations jump to engineering straightaway. Insurers must produce a clear blueprint of their processes. This discovery phase should identify opportunities to separate capabilities into components — units that can become plug-and-play services with open APIs, allowing future agents and partner systems to come in flexibly.

Step 2: Separate design and validation from execution. Insurers should introduce a solutioning phase where a small team defines the future state by determining which capabilities will be rebuilt or replaced, how workflows should operate, etc. Only once the design has been validated should engineering teams begin executing. AI systems can assist by extracting undocumented patterns from interviews, translating requirements into architectures, and simulating potential models to stress-test assumptions.

Step 3: Build in parallel. The target platform should be developed independently while legacy systems continue running operations. A specific sequence is recommended — load non-member-facing master data first, accepting a short window (as brief as possible) during which updates may need to be reflected in both legacy and new systems. Once it's loaded, validate the setup by running test policies.

Step 4: Migrate data through controlled, incremental processes. A single large cutover concentrates risk and creates extended recovery windows if things go south. Hence, a controlled migration strategy is beneficial. Bring the new system online and then migrate data in staggered batches. Not all data needs to migrate; historical data is often better suited for archival storage or data lakes. Before migration, the new platform should be tested in lower environments. Once validated, the system can go live while legacy systems continue managing the existing portfolio. AI systems support this by inferring undocumented schemas, mapping fields, and extracting structured data from records.

Step 5: Retire legacy systems. The final, frequently overlooked step is decommissioning legacy systems. Leaving them partially operational introduces unnecessary cost and risk. Instead, insurers should archive historical data where necessary, ensure compliance for record retention, and fully decommission said infrastructure.

The Kodak Moment: Adapt or Become Obsolete

Organizations focusing on productivity use cases miss the big picture, but the insurance industry will likely not exist in its current form in five years. Think about Kodak 30 years ago: a leader in optimizing film production costs that missed how digitalization changed preferences. In this democratization, insurers need to shift to business models redesigned to cater to change and structure their AI journeys accordingly.

For the full white paper from which this article is adapted, click here.

The general assumption about innovation is that it's top-down. Someone invents something complicated and expensive. As it achieves scale, the innovation becomes simpler and less expensive, making it accessible to broader audiences. Rinse and repeat until the innovation becomes global.

In insurance, that trend has typically meant an innovation in the U.S. or Western Europe that finds its way over time into developing economies.

But there's also an interesting trend known as "reverse innovation" that I've watched play out for some 15 years. If you have a multimillion-dollar medical device, you can take cost out, time and again (and again and again), and still not make it cheap enough to be economic in a poor, rural community. So some smart folks in India working within the severe cost constraints of a poor area invent a version of the medical device — and sometimes that device works well enough that it can reverse the normal flow, migrating from developing areas into the major economies.

To learn more about what reverse innovation is happening — or could be happening — in insurance, I turned to George Kesselman, the founder of InsurTech Asia. He grew up in Canada and went to school there, before spending the last nearly two decades working in Hong Kong and now Singapore, so he has a thoroughly global perspective.

His short answer: Pay very close attention to China.

His longer answer follows.

I met George through a sister organization, the International Insurance Society, for which he has been hosting a working group on innovation that I'm part of. (George will present the findings from the IIS's global survey on insurers' priorities, as well as insights from our working group, at the IIS' annual gathering, the Global Insurance Forum, being held in London at Lloyd's on Nov. 15 and 16.) Here is what he said about innovation in Asia, edited slightly for length and clarity:

KESSELMAN: This year, I had the pleasure of doing a coast-to-coast tour over two weeks in the U.S. and Canada, spending a few days in New York, Toronto, Vancouver, and San Francisco. My read is that innovation is really quite different in North America than what's happening outside. 

The last wave of innovation, from roughly 2014 to 2023—let's call it a 10-year span—was very much an Asia-first wave. It was all this digital insurance that started in China, then Southeast Asia, with a lot of new models when it comes to embedded, small insurance. Then I think the innovation went a little bit more to Europe. In North America, it hasn't really taken off in the same way.

This time, the wave is kind of inverted. With all the AI innovation, all the investments, it's North America first, then Europe. Asia is seeing bits and bobs but not nearly as much as the other two regions.

My theory is that the scale is massively different in the regions, and the problems are also quite different as a result. Asia is very much about the growth of insurance. Insurance wasn't really popular here because the region was a lot less developed. The last 10 to 15 years have seen tremendous economic growth, so innovation and insurance started to come in. Insurers couldn’t scale the same face-to-face distribution, so they found different ways, including digital distribution.

But the scale still remains drastically different. When I was in North America and Canada, everybody talked in terms of billions of dollars. In Asia, people talk about millions, maybe sometimes tens of millions. 

CARROLL: What kinds of embedded insurance innovations emerged in parts of Asia that have filtered out to other parts of the world?

KESSELMAN: In e-commerce, we saw quite a bit of innovation in warranties and the return shipping space. Now we're seeing innovations around trying a new product and getting coverage if you don't like it—you experience an upset stomach after eating something, or face delayed delivery. These are very region-specific and emblematic of the logistics and distribution challenges in Asia.

Travel is another ecosystem that saw significant innovation. Mobility is the third. Then fintech-related insurance is probably the fourth. Each has its unique characteristics. In fintech, for example, we've seen a lot around cyber incidents—if your wallet gets hacked, you get paid—or bill payment protection, where if you get sick, the bill gets paid on your behalf. These are embedded and bundled propositions that are very continuous to the core offering.

These innovations started here because digital leapfrogged in a lot of countries where the infrastructure didn't exist before and really accelerated adoption. Last week I talked to one of the big e-commerce players here, and they estimate about a billion dollars in these micro embedded insurance propositions being conducted yearly now. The growth is definitely there, and the trend has reached scale across the region, though it's still relatively small compared with an average MGA in the States.

As for how this is spreading, some of it is now landing in Europe. Warranty products, lens insurance, hearing aids—these are probably more specific to Europe because the needs and market development are different. In the mobility space—scooters, car insurance, and ride-sharing—I'm hearing that's happening there, as well.

CARROLL: I assume China has played a big role.

KESSELMAN: Embedded and digital insurance really took off from China. The big players were Ping An and ZhongAn—both kind of protection-focused players—and both of them really aggressively invested in innovation. China probably remains three or four years ahead of the rest of the region.

From China, innovation propagated to Southeast Asia. I think India is now also quite a significant market for it, but China was very clearly the first because of the combination of digital payments penetration being super high there, the development of super apps that put everybody—all the distribution—in the same place, and then e-commerce and all the logistics. Everything developed at the same time pretty rapidly.

CARROLL: What are some examples in Asia that other parts of the world could learn from in terms of insurance innovation?

KESSELMAN: Ping An and ZhongAn remain interesting examples. They have been investing very actively in AI, and I think they're still very much focused on distribution and servicing, and developing propositions that are very targeted.

In India, there are some insurers that are starting to do some really interesting stuff. Acko is one that comes to mind in the mobility space that has been quite active. The rest of the region has pockets of innovation, but because the countries are generally smaller in scale, it's very hard for insurers to really grow very rapidly. You know, you start in Singapore, then you need to expand into a neighboring country, and then you need to go and redo the whole growth journey again.

CARROLL: What sorts of interesting startup ideas are you seeing at Insuretech Asia?

KESSELMAN: There have been a couple of waves of startups. The first was really about trying to disrupt distribution. That wave has kind of passed now.

As part of the second wave, we have a couple of really interesting startups. Two are unicorns. Bolttech is one. They focus on distribution and have a couple of different complementary models. They have a very big presence in the U.S. and a global presence, but their headquarters are in Singapore.

Then we have InsureMO, which is like an infrastructure for insurance distribution. They have very big operations in India and Southeast Asia, and now they have growing operations in Europe, the U.S., and Latin America, as well, but they’re also based in Singapore.

CARROLL: What makes those companies different?

KESSELMAN: I think it’s exactly the fact that they started in Southeast Asia, so they needed to do everything very cheaply and fast even though the platforms that are here are very demanding in terms of the operational support that’s needed, in terms of technology. Being incubated here, I think, gave them that natural foundation of really building a scale-up in a demanding environment. They tell me that when they go to the States and talk to insurers there, insurers are very surprised that they can do things for a fraction of the price and at double the speed.

CARROLL: What has surprised you about how AI is being adopted—or not being adopted?

KESSELMAN: The part that surprises me is that it is still very much a technology conversation rather than a business conversation. To give you an example: Recently a couple of insurers in the region came here, and they have flagship programs, but they're all kind of stuck. They don't really know how to solve the problem of creating business value with AI.

This is the same theme we saw in the IIS survey.

Everybody kind of saw this shiny object and had experience with really interesting outputs of this technology, but there's a disconnect between the solution that exists and the problems that insurance companies need to solve. I also think there's a disconnect between the leadership level of the organizations and middle management organizations, which is not as bought into this whole idea of "let's bring AI everywhere, let's augment every job with it."

CARROLL: That's such a common problem—bosses demand change, and underlings nod their heads… but they also find ways to keep doing things the way they always have. Peter Drucker once told me that people don’t change. They just die off, and the next generation then does things differently. But we were talking 25 years ago. I hope we’ve learned a bit since then.

This has been great, but any final thoughts?

KESSELMAN: There are interesting developments in health insurance, again starting with China. Everything is getting so integrated. Hospital data is getting a lot more integrated with insurance data, and it's getting linked up in the hospitals to make it much more proactive—managing health, managing well-being. So rather than putting strain on hospitals, with insurance being really at the back of it, the loop is starting to get closed.

The rest of the world will eventually need to tackle that integration issue as populations age and healthcare systems are probably redesigned. Insurance right now is still very much on the receiving end.

That integration in health insurance is probably one of the most interesting things happening in this part of the world, and, again, I’d use China as the reference market. 

CARROLL: Thanks, George. This is super helpful.

Cheers,

Paul

A teenager's first driver's license represents freedom, responsibility and, for parents, more than a little anxiety. For insurers and independent agents, it also introduces one of personal auto's most challenging exposures: a driver with little experience, no established record and a statistically higher likelihood of a crash.

The financial effect can be dramatic. Bankrate data cited by CNBC estimate that adding a 16-year-old to a family "full-coverage" policy can increase the average annual premium from $2,671 to $5,910. The human cost matters more. AAA reports that 2,636 people were killed in crashes involving teen drivers in 2024, underscoring that the consequences extend beyond young drivers to their passengers and others sharing the road.

For most families, insurers address this exposure within the personal auto policy rather than through a separate teen-driver product. They adapt through inexperienced-operator classifications, vehicle-assignment rules, underwriting requirements, and pricing that reflects years licensed, vehicle type, garaging, mileage, and use.

What Agents Should Do

For agents, the process should begin when the teenager receives a learner's permit, not after the first solo trip. The first obligation is accurate disclosure of everyone the application or policy requires to be identified, including household residents and customary operators. How a disclosed driver is treated for rating purposes is a separate question governed by the insurer's filed rules. This early conversation also provides an opportunity to discuss formal training. In some states, waiting until age 18 may eliminate a mandatory driver-education requirement, but it does not make the newly licensed driver experienced and may mean missing an available training discount.

That distinction becomes especially important when parents live separately. The ISO Personal Auto Policy uses "family member" for a person related to the named insured by blood, marriage or adoption who resides in the named insured's household. The Massachusetts Auto Policy uses the term "household member," with a substantially similar definition. Neither definition designates a single primary residence or makes the address on a license or registration controlling. Depending on the child's actual living arrangement, the child may qualify under policies issued to both parents. Agents should therefore review both households, both parents' auto policies and umbrellas, and every vehicle the teen may operate.

Ownership creates another potential trap. Some families place the policy in the child's name while the parent retains legal title to the vehicle. A carrier may accept that arrangement, but acceptance does not change the policy definitions. Being listed as an operator does not necessarily give the parent the rights or insured status of the named insured. The mismatch can affect liability arising from ownership, permission for other drivers, automatic coverage for a replacement vehicle, physical-damage proceeds and umbrella protection.

Coverage Vs. Price

Coverage should receive at least as much attention as price. Liability and uninsured or underinsured motorist limits, physical-damage coverage, deductibles and the family's personal umbrella should all be considered. A resident teen relative generally qualifies as an insured under the ISO personal umbrella, but forms vary and the underlying auto insurance must be coordinated.

Affordability still matters. Depending on the carrier and state, savings may come from good-student or low-mileage discounts, completion of approved driver education or advanced crash-prevention training, and telematics programs that evaluate speeding, hard braking, mileage, time of day and cellphone distraction and provide financial incentives for safer driving.

Insurance cannot eliminate the risk that comes with a new driver. But thoughtful underwriting, properly coordinated coverage, safety-focused incentives and an informed independent agent can help families manage it—giving teenagers room to gain experience and parents a little more peace of mind.

$117 billion: the insured average annual loss (AAL) estimated for natural catastrophe events in the United States, according to Verisk's 2026 Global Modeled Catastrophe Losses Report. This figure represents roughly two-thirds of the $171 billion global AAL benchmark. The AAL is not a forecast for next year; it is the level of loss the insurance industry should be prepared to absorb over the long term. North America remains the dominant contributor, accounting for $124 billion of the global total.

A benchmark built for today's risk environment

Global insured losses have topped $100 billion for each of the past six years. Since the report's first edition in 2012, estimated global modeled insured average annual losses have increased from $59 billion (in 2011 USD) to $171 billion (in 2025 USD). The increase reflects several factors, including exposure growth, expanded model coverage, advances in catastrophe science, and continuing refinement of the industry's view of risk. The 2026 AAL benchmark also reflects the latest generation of model updates, so it captures current science rather than relying on the assumptions of the last decade.

Why the average is only part of the story

The insured AAL is a long-run expectation, not a prediction. Especially in the U.S., frequent perils such as severe thunderstorms, wildfires, inland floods, and winter storms drive the average, while severe perils, including hurricanes and earthquakes, drive the extremes. Losses in any single year can land far from the mean, which is why the full range of modeled outcomes matters more than any one number.

The difference between average and extreme outcomes illustrates why insurers focus on more than the mean. Against the $171 billion global AAL, the same models estimate a 100-year industry loss approaching $477 billion and a 250-year loss exceeding $600 billion. Catastrophe models are designed to look beyond historical experience and quantify plausible events the industry has not yet experienced.

A single year of lower loss activity doesn't mean lower risk

Catastrophe losses were below average in 2025, but a below-average year reflects a favorable draw, not a change in the risk landscape. Despite the absence of a landfalling hurricane in the continental United States, global insured catastrophe losses still exceeded $100 billion. Wildfires and severe thunderstorms demonstrated how the accumulation of frequency-peril losses can generate significant industry losses even without a headline catastrophe event. The first half of 2026 further illustrated the point. The U.S. p&c industry's combined ratio improved to 92.7 from 96.5 a year earlier, and its estimated net underwriting gain nearly tripled to $31.7 billion. Those results show where the industry landed on the curve this year, not a shift in the underlying risk. The $117 billion expectation for the U.S. doesn't shrink because the first half of the year was kind.

A favorable year can improve financial results, but it does not alter the underlying distribution of catastrophe risk that insurers must manage.

Better to be prepared than surprised

For risk leaders, preparation starts with the AAL, the anchor for catastrophe risk appetite and capital planning and the number with the deepest data behind it. Catastrophe models rely on increasingly detailed exposure information, including building characteristics, reconstruction costs, and claims experience. The quality and currency of that data influence how insurers evaluate risk appetite, reinsurance needs, reserving assumptions, and capital allocation decisions. As exposures evolve and catastrophe science advances, maintaining an accurate view of risk becomes increasingly important.

Three considerations stand out in an environment where catastrophe losses regularly exceed historical expectations. First, evaluate risk appetite against the full range of modeled outcomes rather than recent loss experience alone; the tail of the distribution is often what places the greatest strain on balance sheets. Second, consider how reinsurance programs respond to the perils that drive extreme loss scenarios, not just the events dominating recent headlines. Third, reassess capital assumptions when exposure profiles or catastrophe models change materially, rather than relying solely on periodic review cycles. The purpose of catastrophe modeling is to look beyond what happened this year and quantify what could happen across thousands of plausible future scenarios. The industry's challenge is not preparing for the year it just experienced, but for the years it has not yet seen.

Sources

2026 Global Modeled Catastrophe Losses Report: https://www.verisk.com/resources/campaigns/modeling-insured-catastrophe-losses-a-global-perspective/

1H 2026 underwriting results: https://www.verisk.com/company/newsroom/