When it comes to addressing connected extreme risks, companies have a number of choices, from building flexibility into their operations and talent, to combining quantitative analytics with qualitative 'storytelling' approaches to better identify and manage risks.

Understanding and quantifying risk tolerance is crucial to building financial resilience in a hyper-volatile world. However, many businesses fail to evaluate the critical pathways in which severe operational or revenue disruption could push them toward insolvency. A clear grasp of these scenarios and an organization's risk tolerance, combined with sufficient cash reserves, provides a buffer against unexpected financial shocks, insolvency or default.

By setting just enough cash aside, while considering the opportunity cost of doing so, a company will have the liquidity to continue operations and meet financial obligations, even in the face of hyper-volatility where significant and unforeseen climate or geopolitical risks are more likely to occur. With robust cash reserves an organization can, for example, quickly respond to a sudden increase in raw material costs without compromising financial stability.

Events like economic shocks, geopolitical disruptions and climate-related incidents can trigger one-off, unbudgeted losses. Financial analysis frameworks can be used to evaluate an organization's capacity to absorb such losses in excess of insurance in the context of its financial priorities, such as maintaining a budget, protecting credit ratings and preserving solvency.

Aligning reserve adequacy with defined risk tolerance thresholds provides a structured approach to evaluating an organization's financial resilience under stress by assessing the potential impact of those severe, low-probability loss events on financial performance.

We see more organizations managing hyper-volatility by building operational flexibility. Some practical examples an organization might consider include:  

Diversify revenue streams to spread across multiple products, services and markets to reduce dependence on any single source and mitigate the impact of sudden market changes or disruptions.

• Build supply chain redundancy to manage and mitigate the cascading effects of risks by having alternative options to continue operations in the face of disruption. With multiple suppliers and alternative production methods, a company can switch to a backup plan quicker than competitors if their primary supplier is compromised by climate, geopolitical or another driver of disruption.
• Digitalize to enhance operational flexibility. Cloud-based systems and remote work capabilities, for example, can help a company to continue operations despite the failure or destruction of physical infrastructure.

An organizational culture that values adaptability and innovation can prove vital for building financial resilience against hyper-volatility. When employees at all levels understand and prioritize the need to pivot and innovate, implementing and maintaining strategies that protect a company's financial health becomes easier. Continuous training, clear communication of an organization's risk management goals and known incentives for identifying risks proactively can help here.

Workshops on scenario planning put this in context and in practice: they leverage the problem-solving and critical thinking of employees beyond the risk functions, allowing them to better understand and then plan to respond to hyper-volatility.

By combining scenario testing, data and advanced analytics, an organization can create hypothetical scenarios that capture the cascading effects of multiple risks, such as natural catastrophe events or geopolitical tensions, and then analyze how these scenarios would affect their supply chain and financial performance.

Consider the 2018 and 2023 wildfires in Hawaii, which had starkly different outcomes. The 2018 fire was contained with minimal damage, while stronger winds and the presence of non-native grass species exacerbated the 2023 fire, leading to significant losses. By testing scenarios that consider multiple and nuanced environmental and climate-related factors, you can better identify and then address potential vulnerabilities.

Using advanced analytics to translate stress-testing findings into financial metrics can illustrate how a severe climate event, such as a drought, could materially affect a company's operations. Quantification of material impact helps make the business case appropriate resilience-building investments.

In a hyper-volatile environment, continuous learning and adaptation can prove essential. This is about reassessing an organization's risk management strategies regularly, using quantitative and qualitative methodologies and making adjustments as needed.

Staying informed about emerging risks and trends - combined with using advanced analytics to monitor real-time data on market conditions, such as fluctuations in commodity prices - means an organization can adjust financial modelling and cost structures more effectively in real time.

Acquiring a new insurance customer takes effort. Well-thought-out advertising campaigns, cold sales outreach, and personalized discounts are primary levers for building trust and expanding the customer base. This is a time-consuming and costly affair. That's why, over the last few years, customer acquisition costs have risen by 222%. And brands today lose an average of $29 per new customer, up from $19 a decade ago.

While the exact figures may vary, especially for a highly variable expense such as insurance, one thing is clear. All that effort can be undone by just one negative claim experience. A recent policy research report by Which? exploring consumers' experiences of the insurance claims process found that:

• 48% making a claim experienced at least one issue
• 28% consumers felt their insurer's actions were unjust
• 24% said they didn't understand why their claims got rejected

The message is clear: a single bad claim experience can erode trust, trigger churn, and damage a brand's overall reputation, especially in health insurance. A claim denial can occur due to errors in paperwork, missing documents, undisclosed pre-existing conditions, or other technical reasons. But there's no question that denial is brutal.

In this article, we explore why claims experience often matters more than customer acquisition, especially in health insurance, and how insurers can prioritize it.

The Reality of Claims Experience in Healthcare Insurance

Policyholders need clarity, trustworthiness, responsiveness, and timely claim settlements. Instead, they often get a claims stage that is marred by delays, manual paperwork, opaque communication, lack of explainability, and even denials. 

The TeleTech P&C Customer Satisfaction Survey highlights multiple factors, including how policyholders are treated, channel interactions, and the overall claims process. The most influential was, "my insurance company acted in my best interest."

Source: TeleTech Survey

This is the potential of a good customer journey, being there for the customer when they need you the most.

Where the Challenge Lies

It's not that insurers don't want to deliver superior customer experiences. However, that's just not possible today while operating on legacy CRM tools or spreadsheets. Processes are outdated, require repeated re-entry of customer data, are prone to errors, and are time-consuming. Also, these legacy, disparate systems offer no real-time insights into customer behavior or interactions, leaving insurers to guess based on historical patterns rather than available data, and customer behavior is dynamic and constantly evolving today.

The digital appetite is growing exponentially. Customers demand 24/7 brand availability. Customers want a self-service, AI-assisted portal. Instead of spending minutes on hold and then repeating every detail, they can now submit a claim through a conversational AI that's integrated with an insurance database and auto-populates the most relevant information. Also, this can help them check claim status in real-time, instead of calling up the agent to inquire about progress. This even empowers the employees, who can then focus on more innovative and complex tasks that machines can't replicate, like approving claims faster, offering empathy and transparency, and building stronger customer relationships.

AI-powered healthcare claims processing software can help. It streamlines claims processing, reduces administrative burden, and minimizes claim denials. With AI-driven insights and rule-based processing, insurers and providers can achieve a real-time, 360-degree integrated customer view, enabling them to take strategic initiatives to improve the customer experience.

Financial and Strategic Benefits of Prioritizing Claims Experience

Higher renewal rates and customer lifetime value (CLTV):

For health insurers, policy renewals constitute a significant source of recurring revenue. An insurer that is trusted in the market due to its higher claims settlement ratio, lowest rejection rate, and transparent communications will face far fewer challenges at renewal. This is far more effective than chasing individuals over calls, WhatsApp, or email, and it increases CLTV and brings predictable revenue streams.

Lower operational costs over time

Integrating new technology into a complex insurance process may seem complicated and costly, but a phased implementation can make it manageable, scalable, and beneficial. Unlike the traditional claims processing cycle, which involves manual paperwork, long wait times, and multiple checks, modern automated workflows lower operational overhead and reduce errors. AI-enabled claims management can reduce claims handling costs.

Less manual, repetitive work fosters employee satisfaction, and faster settlement leads to more satisfied customers. This makes claims operations both a service differentiator and a cost-saver.

Competitive differentiation and compliance

How do you build a competitive advantage in such a fierce market? Delivering outstanding claims experience can be a key. Most insurers are good at selling policies (acquisition), but they lack a strong retention strategy, especially in how to support a policyholder when they file a claim. An insurer that can offer a fast, fair, transparent, and smooth claims processing cycle would likely be a winner. Transparent claims handling can also help minimize customer complaints and fraud and optimize compliance and risk management.

Conclusion:

A healthcare emergency is already an emotional and physically exhausting experience for both the policyholder and their family. The last thing they want is a financial burden from an avoidable denied claim. The families shouldn't be chasing an insurance agent for a denied pre-authorization, especially when that policy covers the treatment costs. That said, incomplete/inaccurate patient information, healthcare plan changes, and submission errors can be among the other reasons for denied claims.

That's why a transparent, automated, and faster claims cycle can be the differentiator for businesses. Not only does it help boost operational efficiency by automating repetitive tasks, centralizing updated customer details, reducing data duplication, and increasing revenue streams, but most importantly, an AI-driven claims cycle helps an individual (the policyholder) in need. A smooth, caring, and empathetic claims handling means honoring the promise behind those sold policies when it counts.

The 2026 P&C insurance environment may be much easier to forecast compared with the last several years. Many experts have already said 2026 will be a year of profitable growth. Fitch suggests a combined ratio of 96% to 97%, and even the volatile homeowner market is anticipated to "stabilize." Similarly, it is obvious the new year will be filled with AI in insurance, building off widespread hype and numerous announcements of huge, multi-year investments by the likes of Travelers, Nationwide, GEICO, and Chubb. Just how much and deeply AI will affect insurance remains far less visible, with some areas of attention coming into focus, e.g., pre-binding, underwriting data, claims/FNOL analysis.

Looking back to "see" forward

We also know that each year can bring some unexpected and unwelcome surprises, such as the record-setting $40 billion-plus Palisades wildfires burning some 23,000 acres and everything in its path a year ago. On the flip side, not a single hurricane hit the U.S. Atlantic coastline in 2025. Global CAT losses still amounted to $107 billion in 2025, per SwissRe, but to put things in perspective, Hurricane Katrina in 2005 was around $105 billion alone. Severe CAT exposure has become more visible and thus accepted. In turn, risk models have presumably adjusted over the recent years, and, optimistically, the industry is better prepared.

Throughout 2025, M&A remained vibrant, with insurtech funding just over $1 billion per quarter and an increase in early-stage funding toward the end of year, per Gallagher. A closer look at some specific trends:

• Commercial rate softening, with much variation, e.g., property lines down, commercial auto up and workers' compensation flat
• New car sales slumping by 7.5% at year-end
• Car loan terms elongating beyond 60 months and car loan payments reaching a record of $760 monthly average, per JD Power
• Total loss auto rates rising, to 23%
• Overall auto claim repair volumes declining roughly 8%
• Deductibles for both auto and home climbing, shifting the financial burden from insurer to consumer. According to a study by MATIC, home deductibles are up 22%

As we look to 2026, the following trends help bring rationale for how we see things shaping P&C insurance into a year of progress and collaboration, including:

• Accelerating digitization
• Climate change
• Pressure on globalization
• Rising economic and social inequities
• Major demographic shifts
• Layoffs

AI in Insurance

Investments and practical insurance industry applications of AI will continue to expand even as a large number of AI startups fail and regulators try unsuccessfully to catch up to developments.

• AI will eliminate even higher numbers of less skilled employee positions, including transactional, customer service and document management. At the end of 2025, Chubb announced "radical" cuts of 20% over the next three years due to AI deployment. It is highly likely that other carriers will follow suit.
• A new breed of AI entrepreneurs will emerge to invent highly specialized consumer services delivering instant hyper-personalized gratification for information, retail therapy, mental wellness and unique "experiences."
• AI-enabled photo inspection will gain greater adoption across the insurance, automotive and transportation segments using computer vision and machine learning to automatically analyze images for defects, anomalies, damage, or authenticity, replacing or assisting manual visual operations. This technology will be applied across various industries including insurance to enhance efficiency, consistency, and fraud prevention.

Other Key Factors

Affordability will reverberate beyond a broad consumer/political cost-of-living issue, circling back to P&C insurance where unaffordability chants arguably began. Cost of coverage, availability, pricing and rating methodology will draw even greater attention from consumer groups and regulators. Protection gaps and total cost of home or vehicle ownership will become primary concerns replacing 2024/25 inflation and supply chain worries.

Sustainability will gain adoption across the insurance value chain, especially the North American ecosystem, influenced by global re/insurers. Areas of early focus will include risk and claims management such as auto physical damage and property. Sustainable insurance will reduce risk, develop innovative solutions, improve business performance, and contribute to environmental, social and economic sustainability. Lessons learned from consecutive catastrophic events may serve as a tipping point, taking holistic approaches to predict, harden and prevent.

Climate risk modeling will gain energy. Demand is high and growing for accurate, usable climate information, particularly data that can help assess risk more accurately and contextually. This will drive carriers and others to probe every level of risk, from neighborhoods exposed to more frequent flooding, and to test if proposed atmospheric cooling approaches can work safely, if at all. Interest and investments in climatetech that can benefit insurance will grow significantly.

Cyber threats and fraud losses will continue to expand as digitalization spreads around the world. Recent cyber claims frequency trends remained low while severity increased, presenting the insurance industry with a huge—yet hugely challenging—opportunity.

Insurtech consolidation will accelerate as partnerships and acquisitions become de rigueur and single-point, stand-alone solutions continue to lose favor. The future of insurtech is shifting from rapid disruption to sustainable, AI-driven integration, with the market projected to reach up to $254 billion by 2030. Key trends include AI-powered automation for claims and underwriting, hyper-personalization, embedded insurance, and a focus on profitability over pure growth. Agentic AI platforms will automate routine tasks, potentially cutting outsourced insurance roles in half by 2028. AI will also enhance risk assessment and, in some cases, replace traditional underwriting.

Embedded insurance will continue to emerge as a significant distribution channel as discreet insurance offerings are packaged with the related product/service purchase at the point-of-sale in a singular transaction. Auto insurance packaged with new and used cars will grow as OEMs and dealers seek new profitable revenues.

Open platforms and marketplaces will continue to proliferate, and closed systems will face greater headwinds. Core insurance system platforms (e.g., Guidewire, Duck Creek, Majesco) now host hundreds of popular third-party product and service providers supporting claims, policy administration, billing and payments. Even the leading auto claims and repair information providers (e.g., CCC Intelligent Solutions, Enlyte/Mitchell and Solera/Audatex) have begun to pivot from proprietary closed systems to partnerships with emerging physical damage solution providers.

Consulting firms will restructure for agility, such as moving toward "one firm" models to blend technology and consulting, exemplified by PwC's 2024 internal leadership changes. Major firms like McKinsey, Accenture, and the Big Four have implemented layoffs amid shifting demand for services. AI is cited as a major driver, with a growing percentage of McKinsey's work involving AI-related projects and ultimately affecting the need for traditional roles.

Tech talent will be at a premium. All companies will scramble to retrain, upskill and upgrade staff to fully leverage new and emerging technologies. Change management principles will be dusted off and applied to the numerous influences AI will bring to enterprises seeking to excel. A recognition of the importance of people leveraging AI as much as replacing work functions will continue.

Regulatory risk management will require agility in a fragmented landscape. Navigating a changing regulatory patchwork will require investments in legal expertise and compliance infrastructure, which can drive up operating costs. Some insurers will likely cut their losses and focus on less risky markets. That could increase return on investment for organizations that try to take a broader approach with a longer strategic horizon.

Caveats/The Future

If we have learned anything from recent history it is that the future is inherently uncertain. Not all of our predictions will materialize. Black swan events may occur, reshaping markets, nations and the insurance industry in dramatic, unpredictable ways. But we are confident that most of what we have forecast will become reality. Either way, our projection of optimism for a healthy and vibrant P&C insurance industry in 2026 tops the list.

We look forward to seeing the industry move forward, making progress through technology and collaboration. 2026 will be another exciting year for the industry in both expected and unexpected ways.

Moments after the Baltimore Ravens kicker missed a near-gimme field goal attempt with no time left and let my Pittsburgh Steelers escape with a two-point victory that won them the AFC North and got them into the NFL playoffs, ESPN sent out a notification. The headline read, "The North Belongs to Ravens."

Oops. 

I'm sorry I was too busy whooping and hollering at my TV to quickly click on the notification, and ESPN had fixed the story by the time I got to it, but I can imagine the tone it took about my Steelers. After all, I've read all the vitriol aimed at the Ravens. 

Ravens head coach John Harbaugh would have been a hero in Baltimore if he'd won the game against his archrivals and snuck into the playoffs, but now there is even speculation he will lose his job. He had his team in a position where it was so obviously going to win that I had my finger on the remote, ready to turn the TV off the moment the ball went through the uprights, so I could go out into the yard and scream. But his kicker missed the field goal, so, boom, let's get rid of the bum.  

The coverage is not only wrong-headed — as much as I'm happy to see people beat up on the Ravens and Harbaugh — but demonstrates the sort of mistake that makes executives, including in insurance, evaluate innovation efforts poorly.

The problem is that we don't think in percentages, or at least not well. We may know that the Baltimore kicker had a nearly 90% chance to make his 44-yard field goal try, but we don't quite get that the percentage means he'll miss one time in 10. We know it, but we don't really believe it. So when the kicker misses, we just see failure and look for someone to blame. 

The Yahoo Sports writer acted as though the Ravens failure to make the playoffs was foreordained, even though they began the season as one of the top-ranked teams in the league. His article began: 

"From the beginning of the Baltimore Ravens’ season, when they had an epic collapse and lost to the Buffalo Bills, right to the end when their season ended with a loss to the Pittsburgh Steelers, nothing was good enough." He added that the Ravens "will be searching for answers after a season that went horribly wrong. There will be immediate questions about John Harbaugh’s future as the team’s coach."

The Steelers and head coach Mike Tomlin would have come in for exactly this sort of treatment if the field goal had been good, even though the Steelers had even a stronger gripe about the kicking gods being against them. The Steelers were only two points ahead, and thus in danger of losing to a field goal, because their kicker had missed an extra point with 55 seconds to go in the game, after not missing one all season. The pressure on Tomlin isn't entirely gone, given that he hasn't won a playoff game in eight years, but there will be a lot less of it in the off-season, especially if we beat the Texans in Pittsburgh on Monday night. 

The Steelers even came in for misconceived criticism despite winning the game. I always liked Trey Wingo when he was an analyst on ESPN, but he posted a Tweet that was downright silly. He said Tomlin made a terrible decision by going for a field goal at the end of the first half rather than trying for a touchdown from the Ravens one-yard line. He tried to back up his claim, but he was clearly just Monday morning quarterbacking — because the Steelers failed to score a touchdown, they shouldn't have tried.

The truly goofy part of his argument was his statement that, had the Steelers converted a chip-shot field goal, they would have been five points ahead of the Ravens at the end of the game, not two, and wouldn't have had to worry about a field goal. But come on. If the Steelers had scored a field goal, the dynamics of the game would have changed. The coaches would have made different decisions, and the players would have done different things in the different circumstance. You can't just take the three points and add them to their score for the rest of the night. 

The other part was nearly as bad. He offered an adage: "You always take the points." But the NFL has moved beyond a lot of adages and started to apply real, live math to questions such as when to go for it on fourth down, as I wrote a year ago. And here's the math:

A field goal would have been almost a 100% chance, so the Steelers could have counted on almost three points. The attempt at a touchdown had a slightly better than 70% chance, based on league averages, and an extra point is almost a lock. 70%-plus of seven points is roughly five points. Yes, the Steelers' attempt was embarrassingly incompetent, but I'm still going to take a likely outcome of five points over one of three points in almost every circumstance. 

This is the Ravens we're talking about. I need every edge I can get.

What does this mean for insurers?

Insurers need to think more like venture capitalists and less like Yahoo Sports or Trey Wingo. Venture capitalists not only know that 90% of the startups they invest in will fail but act on that knowledge. If they think an area is promising, they make a number of bets rather than assuming they're going to pick the one winner. They don't label their entrepreneurs as losers just because they lose — if you had a 90% chance to win but lost, they count that as a win and blame the circumstance, not you. As a result, VCs often invest in serial entrepreneurs, who have either previously failed or only had modest successes. 

A line from the mother of a girl who ski raced with my younger daughter has stuck with me. Stephanie, who build a financial software business she sold for $2.5 billion, told me: "I like people with scars."

Insurers also need to think of early innovation efforts as opportunities to learn, rather than trying to immediately shape them into pilot projects designed to scale and go to market. As I've been saying for almost 30 years now, the key is to Think Big, Start Small and Learn Fast — the latter two points meaning that you need to do lots of inexpensive projects, even though they're in the service of a grand vision, and move on quickly to the next set of tests once you've learned what you can. The vast majority of these projects won't get anywhere near the market, but they aren't failures if you've learned something important.

I'm hardly arguing for no accountability. There are still bad ideas, and they may be executed poorly by people who should no longer be part of your organization. I'm just arguing that we all take a more sophisticated view of success and don't decide that failing to convert a 90% chance merits firing — even though I'd love to see Harbaugh out of Baltimore.

Go, Steelers! Beat those Texans!

Cheers,

Paul

Paul Carroll

You’ve written for us about the need for hyper-personalization in life insurance. Would you start us off by describing what that looks like in practice, as well as how it differs from how life insurance has historically been handled?

Brooke Vemuri

I've been in the life insurance business for 23 years, working in both technology and operations, so I've seen firsthand the evolution from moving physical forms around the building—we had folders, and we put them in carts, and we drove them around to our underwriters—to where we are today. We've crossed a lot of hurdles over the last 20 years.

Now we're in a place where we have intelligent automation and a digital application process. Over the past few years, this has meant having an event-driven, rules-based system that allows us to capture the right application questions, then run rule sets underneath, call third-party data, and do all the things we need to amalgamate and come together on a decision. That change was hard to get across the line, but we've arrived and are doing very well as a result.

The next evolution is moving away from considering the application as one static process or one way to get to an outcome. Instead, we're moving toward tailoring the experience by distribution, by agent, by customer. Personalization means accommodating the way our agents and agencies like to do business.

Just to give you a small example: We have some agents who say, "Don't waste my time collecting beneficiary information. I'm going to get you everything I need to get a decision on the case. Then once I have a decision and the customer accepts, I'll gather that beneficiary information—it's more of an administrative piece of work because it's not influencing the decision." I have another distribution partner that says, "We start with beneficiaries. We connect the sale to the reason why you're making the purchase."

That's a high-level example on the easier side of personalization—how do we tailor or reorder the journey? 

Next comes determining how many questions we ask based on the product, and how we tailor that experience based on the product, the partner, and the customer. You start to get all these different variations of how you would flow a digital application process to collect the right information at the right time, make the right decision, and end up with a case you can place in force—in a way that works with every agent and partner you have.

You can start to see that there are going to be lots of permutations and combinations of how all of that evolves and comes together. From a technology perspective, that's all about creating the right, flexible architecture to make that happen, to allow that configuration, and to support our agents in the way they want to sell the business.

Paul Carroll 

Is this personalization extending beyond the sales process to policy offerings and features, as well?

Brooke Vemuri

Yes, that's a good question. Over the last 20 years or so, you typically entered the journey when you already knew what product you wanted. For example, "I know I want a term 20 policy. It's for a 50-year-old male with two children.” Now we're heading toward a different approach—someone starts the journey as that same person, but they really don't know what product or combination of features they want until they move through the journey.

Whether that means recommendations based on how many beneficiaries you have, what stage of life you're in, what your income levels are, or what job you have—as the system starts to understand who the customer is in the journey, we can start to make the right recommendations for a product.

I think you're going to see both tailoring of products and features. One of the things we're working on is how to come out with an offer for every applicant. Because in a lot of cases, there are still declines in our environment, even on term business. So how do you enter the process saying, "I have a desire to have life insurance" and be sure to end up with something—whether that's an accidental death product or a final expense product?

So yes, you're going to start to see tailoring of offers, cross-sell, counter-offers—that's what we're calling it. How do we come up with another product that might be viable for both the customer and the life insurance company so that, at the end of the journey, you still get some product that is available to you.

Paul Carroll

How is AI being incorporated into this process, particularly in terms of gathering customer information and providing real-time recommendations to agents during customer interactions?

Brooke Vemuri

Intelligence is coming into play primarily in our agent-facing capabilities. Our journey encompasses both customer-agent interactions and internal, employee-facing systems. As people interface with the system, it makes certain recommendations to the agent based on how the journey is progressing. 

You might not do that directly with a customer, though. If a customer is going through the process unassisted, it's a little bit more complex for them to navigate some of those things. So our thought process is a lot like TurboTax—in the upper right-hand corner as you progress, it shows what you're accruing, what we know now, and where we might go in this journey, and starts to forecast and give options for next best steps.

For now, we’ll just focus on agent-facing capabilities, because that feels more like advisory-type activities. That's at least our early thinking.

Paul Carroll

What innovation can we expect to see over the next few years?

Brooke Vemuri

I think we'll be very much focused on what we're talking about now, which is tailoring our apply processes—for both customer and agent. We're looking at getting some cues or indicators into the journey about how a case is tracking and how agents might handle what's going to happen in terms of that sale, whether it's going to be a decline or whether we can pivot to another product.

I think you're going to see all of that in the next two to three years—tailoring the counter offers or the alternate offers, tailoring the journey to align with how that agent wants to sell their business.

Beyond that, we're imagining a lot more variation on the product. We're going to see more things in terms of what we call table ratings today: How do we get more price variability in the product so we can accommodate more and more applicants? I think that's probably on the three- to five-year horizon.

Paul Carroll

What trends are you seeing around younger generations being more interested in living benefits rather than death benefits?

Brooke Vemuri

That's a good point. Things are being added on and tailored into the product that allow more utility out of the product. It's not a policy that you print and put on the shelf and wait till you die, and then someone gets the benefits of it. It's about how the policy can give you benefits while you're living—tapping into that face amount for critical illness or accidents and things like that.

I agree that living benefits are giving more value to the term product. I mean, there's still a need for your basic term product out there that has no bells and whistles. But to reach some of the younger generations, we're finding that living benefits have been valuable for them.

Paul Carroll

Life insurance policies represent long-term commitments—20 years for term policies, often longer for permanent ones—making it challenging to validate underwriting assumptions quickly. How is the recent industry shift toward fluidless evaluation and fewer application questions working in practice?

Brooke Vemuri

It does take years to know if your assumptions are right. I think the biggest value of the data right now is being able to look at historical data and model it in a way that makes us more comfortable with innovations—whether that's leveraging other third-party evidence sources or forgoing exams altogether, like with fluidless underwriting.

We're using a combination of historical data—looking at how it would perform based on our back book of hundreds of thousands of cases at this point—and other third-party evidence that we can leverage, like medical claims data and labs data. We're leaning heavily into claims data specifically.

It's really about putting those pieces of the puzzle together alongside the self-declarations from the application so that you have a complete picture to underwrite from and make a decision. We're going after alternative evidence sources to eliminate some exams while also looking retrospectively at the data to see what it's telling us. Through our modeling, we can apply new assumptions to that data, see what it might look like, and price accordingly.

Paul Carroll

What other trends should our readers be aware of in life insurance?

Brooke Vemuri

I think you've probably captured a lot of them. There's going to be a lot of tailoring—tailoring on the journey and tailoring on the products, tailoring on the prices, tailoring on the variation of features and capabilities that exist in the term product space.

How do we alternate out of term if we need to? How do we counter out of that if we need to? I think that's the real progress on the horizon.

Paul Carroll

Thanks, Brooke.
 

About Brooke Vemuri

Brooke Vemuri is vice president, IT and innovation at Banner Life and William Penn. She leads people and cross-functional teams to reimagine the future of life insurance from lead generation, through apply and underwriting, to offer, pay, and in-force. Her team drives transformation and change to the business and distribution through the development and execution of business propositions focused on growth and cost reduction through a digital business strategy.

As an industry, insurance no longer views AI as a novelty. But with this embrace comes higher standards. Insurers have become more discerning buyers and plan to invest large percentages of their own in-house budgets into technology products and teams. Insurance IT spending hit $185 billion in 2024 and is forecast to grow 8.5% each year, reaching $420 billion by 2033. At the same time, insurance companies are still looking for insurtech partners to drive innovation and bring new ideas into the industry. The increase in investment and expectations is poised to shift the insurance technology landscape in 2026 in three main ways:

#1: Good data, not pretty colors

For years, insurers have relied on third‑party vendors to drive their technology adoption. Now, after sustained investment in AI talent, engineering teams, and modernized infrastructure, they can build polished interfaces and baseline AI models themselves. This shift means insurers no longer need vendors for surface‑level innovation, like simple LLM wrappers.

While they'll still rely on their insurtech partners, insurers' expectations will start to change. Flashy chatbots, attractive dashboards, and feature‑heavy UIs won't move the needle unless they solve a real business problem. Insurers are looking for differentiated capabilities like proprietary data pipelines and advanced AI models that meaningfully outperform what they can create internally. Anything else is lipstick on a pig, and insurers aren't falling for it.

#2: Integrated technology stacks

When insurers do find the innovative technology they need, the systems they're already running can often stand in the way of implementation. Over time, insurers have added tools and platforms piece by piece, creating a maze of legacy systems that rarely communicate cleanly with each other. This fragmentation creates a chain reaction. Siloed systems lead to integration bottlenecks, which slow the adoption of new technology. In an industry where compliance and operational efficiency matter, this drag has become a liability. Moving into 2026, insurers will prioritize connected technology stacks to accelerate the adoption of best-in-class solutions, reduce security risks, and future-proof IT resources.

One of the major priorities for technology integration in 2026 will be adopting MCP (Model Context Protocol). MCP is an open, standardized protocol that allows AI models to securely and consistently interact with external tools, data sources, and internal systems. For a highly regulated industry like insurance, where data privacy, auditability, and controlled access are critical, MCP provides a framework that enables AI-driven capabilities without compromising customer data or operational safeguards. Insurers leveraging AI in-house will demand that their technology partners meet these standards, allowing them to securely build and innovate.

#3: The death of noisy alerts

Insurers are masters at identifying, measuring, and mitigating risks, making sure no stone goes unturned. Fraud alerts are a common practice, giving claims teams a heads-up when anomalies are detected. But when helpful notifications turn into an avalanche of meaningless signals, alerts quickly turn from necessary to completely ignored. Constant pings, red flags, and false positives not only overwhelm adjusters but also erode trust in the tools themselves, leading professionals to either tune out alerts or fall back on manual investigation.

In the coming year, insurers will be looking for evidence-based, noise-free insights that directly inform decision-making. By reducing false positives and standardizing how online evidence is used in claims workflows, insurers can finally strike the balance between automation and accuracy, protecting their reserves while preventing burnout across adjuster, SIU, and litigation teams.

Looking Ahead

As insurers move into 2026, their expectations for technology are sharper, pragmatic, and strategically aligned with long-term business goals. The increase in standards will push the industry as a whole forward, setting the stage for major innovations in the year to come.

Sedgwick's 2026 Global Risk Study warns that resilience will be the defining challenge for businesses in the year ahead. Leaders at every level are being urged to anticipate risks that extend beyond today's headlines, sharpen foresight and embed strategies that can withstand uncertainty. In that same vein, they also need to clearly define a practical road map for claims and litigation management, crisis response, and workforce adaptation, which homes in on the report's central message: Success in 2026 will rely on readiness for the unknown.

The 2026 Global Risk Study offers a forward-looking perspective on the forces reshaping the industry. The report draws on perspectives of Fortune 500 executives and is bolstered by expert commentary from clients and industry leaders. These combined perspectives provide a comprehensive view of emerging trends, challenges, and lessons across sectors.

Through an exploration of key cross-sector data and trends, the report examines both the challenges and opportunities that are shaping the future of risk, claims, and workplace resilience. In turn, the findings and insights can be used to equip organizations to better understand emerging risks, adapt to evolving conditions, and innovate with confidence. By embedding these insights into strategic planning, organizations can lead effectively, even in a landscape defined by change.

Redefining risk and claims, AI drives both opportunity and challenge

AI is a powerful force that's rapidly reshaping society and the business world, and sits at the intersection of opportunity and risk. While organizations recognize its transformative potential, they also identify the fast pace of technological evolution and regulatory uncertainty as major implementation hurdles. Our survey of Fortune 500 executives shows that while 70% of Fortune 500 firms have established AI risk committees, only 14% feel fully prepared for deployment, and 31% report struggling to keep pace or falling behind. This demonstrates that to capitalize on AI while mitigating risk, companies must actively invest in governance, workforce training and change management, and developing a strategic deployment roadmap, or risk falling so far behind that it's impossible to catch up.

Rising costs, labor strains, and insurance pressure fuel catastrophe risk

Extreme weather, property exposure, and labor shortages are intensifying catastrophe risks and recovery challenges, driving higher costs, longer timelines, and greater complexity across industries. Organizations increasingly see agility, planning, and technology as essential to navigating these shifting risk profiles. In fact, other data from the report highlights the scale of the issue, with 75% reporting labor friction tied to immigration-related access hurdles, 11% facing severe labor access challenges, and 76% anticipating moderate to severe insurance pressures stemming from catastrophe risks. The takeaway here is that companies that embrace resilience strategies and technology-enabled claim solutions are, or will be, better positioned to respond efficiently and limit operational disruptions.

Mounting geopolitical, trade, and cyber risks strain supply chains

Supply chain disruption is also intensifying as geopolitical instability, shifting trade policies, regulatory upheaval, and global events amplify existing vulnerabilities. Organizations continue to face persistent challenges from supplier concentration, logistics breakdowns, and rising cyber threats. With 66% of Fortune 500 companies reporting negative impacts from U.S. trade policies, 65% identify economic and geopolitical volatility as their primary supply chain concern, and 38% highlight cybersecurity as a structural vulnerability within their networks. This goes to show that to remain competitive, companies must actively assess and manage supply chains, implement robust cybersecurity measures, and develop agile contingency plans.

Accelerated workforce change drives new leadership and skills priorities

As AI continues to reshape the workplace, organizations are reimagining leadership and prioritizing people skills, team-building, career mobility, and purposeful mentorship to balance emerging challenges with new opportunities. Data within the report provides evidence of the shifting landscape with 32% of organizations citing changing employee expectations as their top talent challenge, 47% struggling with transferring leadership skills effectively, and 20% viewing VR/AR technologies as a priority tool for reducing workplace injuries. The evidence says that forward-thinking organizations are investing in leadership development, continuous learning programs, and technology-enabled training to equip employees for the evolving workforce. These investments also create opportunities to enhance safety, reduce injuries, and maximize employee productivity.

Cyber and geopolitical threats outpace organizational readiness

Global risk strategies are increasingly defined by persistent instability, with organizations confronting relentless volatility from multiple sources simultaneously. As exposures outpace preparedness, agile scenario planning and staged interventions are becoming essential for resilience. While only 3% of Fortune 500 companies feel fully prepared for all global risks, 56% identify geopolitical instability as their top concern, the highest across sectors and regions, and 50% cite cyber threats as a critical risk exposure. The data underscores the urgency for organizations to strengthen governance, enhance monitoring, and integrate cross-functional risk strategies.

To view the full study, visit Sedgwick.

The insurance industry is investing heavily in AI, primarily to automate functions across underwriting, claims, fraud detection, and customer experience. However, despite the increase in investment, few carriers have extracted outsize value from AI, largely because enterprise rewiring has been focused solely on speed instead of context. 

Automation is undeniably on the rise, but automation that prioritizes speed without understanding runs the risk of misfires. And in insurance, misfires are costly. Humans remain essential for nuanced decisions, especially in complex or highly emotional scenarios. Scaling AI responsibly is possible, but it requires a shift from a speed-first to a context-first approach. Models must understand not only the patterns they're analyzing, but the underlying reasons behind them.

The Accuracy Gap

Insurers have made significant progress in developing predictive models that identify probabilities with increasing precision. However, these models are constrained by the need for explainability. Regulatory requirements mean actuarial and underwriting models rely on limited, well-understood variables, creating a gap between what advanced machine learning could predict and what insurers can reasonably deploy. As a result, models often struggle to capture the full nuance behind risk, reinforcing the need for human judgment in complex or exceptional cases.

Generative AI and machine learning systems can support everything from claims triage to fraud detection, yet their decision-making abilities quickly erode when context is missing. Because of this, underwriters and claims professionals continue to shoulder the burden of work, as most cases need manual intervention as "exception" cases from a standard predictive model. For example, adjusting perils for unusual environmental factors or managing emotionally charged claims where customer history, tone, and circumstances matter.

Underwriters are often presented with the promise of "black box outputs" that could materially improve decision quality, even though such models are rarely fully incorporated into production due to transparency requirements. The potential performance gains are compelling, but the lack of explainability introduces governance, compliance, and audit challenges that prevent widespread adoption.

As a result, AI and automation are often used to accelerate modeled decisions, such as automating data gathering processes, rather than to enhance the quality of those decisions. Speed becomes the default measure of progress, while insurers still rely on humans to interpret context and manage high-stakes judgments. In a regulated industry where defensibility is paramount, this focus on efficiency over insight limits the potential value of AI.

Why Context Matters

Context is the cornerstone of trustworthy outputs. Contextual intelligence allows insurers to model the nuances behind decision-making that traditional analytical approaches often miss. Decisions aren't made solely on isolated transactions or entities; they depend on how these elements relate to one another.

Many critical factors remain hidden: loss performance reflects both human behavior and environmental conditions, and while the latter are difficult to influence, the former, like insureds' relationships with other individuals or businesses, are frequently under-modeled. Most insurers evaluate behavior in the context of a specific line of business, but miss signals such as commercial directors' histories at prior firms, social relationships with other stakeholders, or patterns spanning personal and commercial lines. Accessing this kind of data quickly and reliably is challenging, which is where context is often lacking from many decisions.

While horizontal uses of AI, such as summarization or transcription, provide operational support, they rarely deliver transformational change. Vertical, context-aware AI, by contrast, enables true next-best-action guidance. It can prioritize claims based on severity and customer value, evaluate broker behavior over time, or surface hidden relationships across complex books of business. According to Deloitte's 2025 Insurance Technology Trends Report, even as AI systems progress, they will still rely heavily on connected datasets to provide "actionable insights" to both regulators and customers.

Transformative Applications

Insurers are already beginning to explore how contextual AI can reshape core operations. In underwriting, context-aware models can evaluate risk holistically, incorporating nuanced exposures, historical performance, customer behavior patterns, and third-party datasets. These systems support more fair, accurate, and consistent decisions. According to a 2025 CEFPro study, insurers that are already applying contextual AI to underwriting have reduced processing time by 31% and enhanced risk assessment more than 40% while improving overall efficiency.

In claims, AI models paired with human oversight are accelerating adjudication while preserving transparency and auditability. Context helps claims teams identify which cases can move quickly and which require deeper review, improving both cycle time and customer satisfaction without compromising regulatory scrutiny. It also helps more accurately model claim severity and find aggregate recovery opportunities due to recurring third-party fault, something which often goes undetected in claim-centric analyses.

In fraud detection, contextual AI can identify coordinated fraud rings, reduce false positives, and improve straight-through processing rates. Building contextual relationships before flagging anomalies leads to far more accurate decisions, confidently catching the largest threats and letting good customers flow through frictionlessly, improving customer experience while reducing leakage and supporting revenue growth.

Governance and Readiness

Strong data governance is no longer optional; it is a prerequisite for responsible AI adoption. No model, no matter how technically sophisticated, can compensate for weak data foundations. Insurers leading the market are actively investing in robust governance frameworks that include model monitoring, explainability standards, auditable decision trails, and continuing regulatory alignment.

The Evident AI Insurance Index shows that the top-performing insurers prioritize transparency and ethical AI development, linking performance to strong governance and leadership accountability. This demonstrates that AI success is not just about technology; it's about embedding trust and oversight into every stage of deployment.

Meanwhile, the IAIS Mid-Year Insurance Report emphasizes that resilience in AI deployment depends on trustworthy data foundations and consistent oversight. Together, these findings highlight a critical lesson: insurers that invest in robust governance and high-quality data are better positioned to scale AI effectively, reduce risk, and generate measurable business value.

Defining the Next Chapter of Insurance AI

The insurance industry has reached an inflection point. AI's success will not be measured by how many processes it automates, but by how effectively it helps humans make better, more defensible decisions. Insurers that prioritize integrated contextual data, invest in human-AI collaboration, and build systems that are explainable and auditable will be the ones who unlock AI's full potential.

As the industry moves into 2026 and beyond, context will be the competitive currency that determines which insurers lead the market, and which are left trying to explain decisions they can't fully understand.

Flood risk is no longer a peripheral climate concern. It is fast becoming one of the most underestimated balance-sheet threats facing businesses and insurers globally. Over the last five years alone, flooding has caused an estimated $325 billion in economic losses worldwide, yet only $70 billion was insured (source: Munich Re), exposing a widening protection gap that the industry can no longer ignore.

This is not merely a story of rising water levels. It is a story of outdated assumptions.

Traditional flood models, rooted in historical event catalogues, are increasingly unfit for purpose in a world of volatile weather patterns, rapid urbanization, and climate-driven extremes. As Hamid Khandahari of Descartes Underwriting says, historical data "cannot fully account for events beyond anything previously recorded." The implications for underwriting, pricing, and capital allocation are profound.

The new reality: unpredictable, underinsured, unprepared

The scale of the challenge is stark. In the U.K. alone, surface-water flood risk could affect 6.1 million properties by 2050—a 30% increase compared to previous projections (source: NaFRA). In the U.S., flood events jumped nearly 30% year-on-year between 2022 and 2023, with several states seeing events quadruple (source: Lending Tree).

Yet, despite mounting evidence, risk perception remains dangerously muted. Many organizations still operate under a flawed logic: "We haven't flooded before, so we probably won't." This mindset is actively reinforced by commercial insurance dynamics. When losses do occur, the response is typically capacity withdrawal, higher deductibles, exclusions, or outright non-renewal—exactly when resilience is most needed.

This has created a vicious cycle: low perceived risk leads to underinsurance; the first major loss triggers rate shock and restricted coverage; risk then becomes both more expensive and harder to transfer.

Technology is changing what's possible

The industry now has the tools to break this cycle—but only if it evolves how it uses them.

Advanced flood forecasting, hydrodynamic modeling, and IoT sensor networks are changing the economics of risk. Leading platforms such as Previsico's can now provide 36-48 hours of warning, allowing businesses to move assets, shut down operations safely, and materially reduce losses.

The Balfour Beatty Vinci HS2 case illustrates this shift in practice. After suffering multimillion-pound flood losses, the company used predictive flood intelligence and sensors to protect sites, relocate critical equipment, and avoid repeat losses when the next event occurred.

Crucially, parametric solutions are not constrained by the same capital bottlenecks that plague traditional catastrophe underwriting. They can also be structured to cover deductibles, gaps, or even function as primary protection where conventional policies fail.

Yet adoption remains strikingly low. Despite 43% of U.K. organizations reporting flood impact, only 7% currently use parametric insurance in their flood risk financing strategy. That disconnect represents both a risk and an opportunity for the market. 

The strategic role of captives

This is where captives emerge as the industry's most underused strategic asset.

Captives are no longer simply about premium arbitrage or tax efficiency. They are fast becoming risk laboratories—vehicles for innovation, structured retention, and long-term resilience.

More than 1,700 new captives have been formed since 2020, bringing the global total above 7,000. Many are now absorbing flood risk by necessity, not choice—particularly in the U.S., where obtaining flood risk coverage is often incredibly difficult. These captives are then highly motivated to encourage operating divisions to manage flood risk effectively.

When combined with parametric structures, captives unlock a powerful model:

• The captive retains frequency risk.
• Parametric reinsurance absorbs severity risk.
• The business benefits from faster liquidity and reduced earnings volatility.

This architecture also helps address "basis risk"—the mismatch between actual loss and parametric payout—by allowing the captive to smooth inconsistencies and manage retained exposures.

In practice, this makes flood risk more insurable, more predictable, and more strategically manageable.

From risk transfer to risk resilience

The industry stands at an inflection point.

Flood is no longer just a peril to be transferred; it is a systemic risk that must be actively managed, predicted, and financed in new ways. The combination of advanced forecasting, real-time data, parametric triggers, and captive-backed structures represents a shift from exposure to resilience.

The winners in this market will not be those who wait for traditional models to catch up. They will be the insurers, reinsurers, brokers, and risk managers who accept that the future of flood insurance is not about pricing the past—but engineering resilience for a climate-altered future.