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Outdated Infrastructure Delays Insurance Claims

Research shows that, despite front-end digitization, outdated claims payment infrastructure undermines insurers' efficiency and customers' trust.

Pile of outdated television sets

Despite years of progress in digitizing the front end of insurance claims, the back-end infrastructure that supports these payments remains outdated and fragmented.

Our latest research, based on insights from over 200 senior insurance professionals in the U.S. and U.K., uncovered a clear pattern: The financial infrastructure that supports claims fund management remains fragmented. This disconnect not only delays payments but also creates operational risks and undermines trust.

The numbers tell the story. Nearly 80% of respondents cited internal process complexity as a key barrier to faster payments. 66% reported struggling to access readily available funds, a challenge that climbs to 74% in the U.S., where decentralized funding structures and manual approval flows persist. Only 1% of insurers said collaboration between claims and finance teams is "highly effective," underscoring how siloed operations remain.

All these responses represent a strategic challenge for insurers striving to stay competitive in a fast-evolving market.

The hidden costs of fragmentation

At the heart of the issue is a lack of real-time financial coordination. Claims, finance, and treasury teams often operate in silos, using separate systems that don't talk to each other. This makes it nearly impossible to track the status of funds, forecast liquidity needs, or reconcile payments without time-consuming manual intervention.

For U.S. insurers, the stakes are especially high. Our research shows that U.S.-based treasury teams are more focused on governance and compliance (37%) than their U.K. counterparts (22%), a reflection of heightened regulatory scrutiny and complex operating environments. Despite this focus, many still struggle with fragmented systems that leave them exposed to delays, errors, and compliance risks.

External coordination adds another layer of complexity. 78% of insurers reported delays associated with third-party involvement, whether from brokers, TPAs, or banking partners. 

While these partners play a critical role in the claims ecosystem, the lack of seamless integration and data-sharing slows payments, introduces errors, increases risk exposure and contributes to inefficient liquidity management.

Toward a new model: Real-time financial orchestration

The good news is that the industry is beginning to move beyond patchwork solutions. Insurers are increasingly recognizing that speed, accuracy, and control aren't just about operational efficiency; they're fundamental to customer trust and financial resilience.

This is where real-time financial orchestration comes in. By creating unified financial infrastructure that connects claims, finance, and treasury teams—and extends to external partners—insurers can gain complete visibility into fund flows, automate disbursement processes, and manage liquidity with confidence.

Such infrastructure isn't just about faster payments. It's about enabling insurers to:

  • Avoid overfunding and manual cash calls.
  • Enhance regulatory readiness through transparent, auditable fund tracking.
  • Strengthen resilience in the face of increasing market volatility and regulatory
  • scrutiny.

Our research also revealed that early adopters are already exploring smarter fund segregation models, integrated payment platforms, and automation tools that streamline reconciliation and compliance. These innovations are laying the foundation for a claims ecosystem where financial coordination isn't an afterthought, but a strategic advantage.

What's at stake

Insurers can't afford to let back-end inefficiencies stymie them. Rising customer expectations, increased regulatory oversight, and competitive pressure are forcing the industry to reimagine how money moves. The insurers that take steps to modernize financial operations by investing in real-time coordination, intelligent fund management, and integrated platforms will be the ones best positioned to lead.

This isn't just about paying claims faster. It's about creating an ecosystem where every participant, from insurers and TPAs to brokers and banks, operates from a shared source of truth, reducing errors and delays. It's about ensuring that liquidity is always available where and when it's needed, so insurers can deliver on their promises.

The call to action

The future of claims finance is about bold investments in real-time, data-driven infrastructure that connects internal teams and external partners alike. It's about making claims payments a driver of operational excellence and customer trust, not a back-office bottleneck.

As the landscape continues to evolve, insurers must ask themselves: Are we ready to break down the barriers that slow payments and erode trust? If not now, when?


Curt Hess

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Curt Hess

Curt Hess is the U.S. executive president at Vitesse.

He has over 25 years of experience across fintech and global banking, most recently as chief operating officer at 10x Banking. Prior to that, Hess held multiple C-level roles during a 12-year tenure at Barclays, including chief executive officer of the U.S. consumer bank and chief executive officer of Europe retail and business banking.  Earlier in his career, Hess held senior finance leadership positions at Citi, as well as with Bank of America in the U,S. 

AI in Claims: Act Now or Be Left Behind

AI transforms insurance operations from a future concept to a present-day imperative, with early adopters already seeing dramatic efficiency gains.

Light Bulb on White Panel

The commercial property and casualty (P&C) insurance industry stands at a pivotal moment that mirrors the dawn of the internet era. Just as companies that dismissed the internet in the mid-1990s found themselves scrambling to catch up years later, today's insurers face a similar inflection point with artificial intelligence. The message is clear: AI adoption in claims processing is no longer optional, it's inevitable.

For C-level executives in P&C organizations, the question has shifted from, "Should we implement AI?" to, "How quickly can we integrate AI effectively?" This urgency isn't driven by hype but by compelling market realities that demand immediate attention.

Why AI Is Now Inevitable in Claims

The evidence supporting AI's inevitability in claims processing is overwhelming. According to a 2024 Carrier Management study, approximately 66% of P&C carriers plan to use AI for operational decisions within the year, while current adoption hovers around 14%. This gap doesn't indicate hesitation, but it signals a massive wave of implementation about to break across the industry.

Executive sentiment confirms this trajectory. A 2025 survey by Roots Automation revealed that 90% of insurance executives express optimism about AI, with two-thirds being "strongly in favor" of its application within the insurance sector.

Early adopters are already demonstrating remarkable results. Sedgwick, a prominent claims management service provider, reduced claim review times from 15 minutes to just 2 minutes per claim using generative AI. Similarly, CURE Insurance reported productivity gains of 60–70% in their claims operations through AI deployment. These aren't marginal improvements; they represent transformative efficiency gains that translate directly to competitive advantage.

Capital investment patterns confirm AI's central role in insurance's future. According to Gallagher Re's Q2 2024 findings, over 63% of global insurtech funding was directed toward AI-first solutions.

The High Cost of Inaction

While the benefits of AI adoption are compelling, the consequences of delay are potentially devastating. Organizations that hesitate will find their competitors settling claims faster and more efficiently, damaging customer satisfaction and retention.

The financial implications are equally concerning. Without AI automation of routine tasks, loss adjustment expense (LAE) ratios will remain stubbornly high compared with AI-augmented competitors. This hurts profitability and the ability to offer competitive premiums.

The internal impact on workforce capabilities presents another risk. A 2025 study found that 58% of claims professionals using AI report low confidence in available technological tools, often linked to insufficient training. Organizations without comprehensive AI adoption plans will see existing technology gaps widen, leading to decreased employee morale and difficulty attracting talent.

Perhaps most critically, regulatory scrutiny of AI in insurance is intensifying. As of March 2025, 24 U.S. states had adopted the NAIC Model Bulletin on the Use of Artificial Intelligence Systems by insurers. Companies operating without formal AI policies are not only unprepared for regulatory inquiries but are at higher risk of non-compliance.

The Imperative of a Formal AI Policy

As organizations integrate AI tools into their core operations, establishing a comprehensive AI policy becomes foundational for responsible innovation and risk mitigation. This policy must address how AI technologies are developed, deployed, and managed within the claims organization.

A robust AI policy ensures alignment with regulatory requirements, establishes clear accountability for AI-driven decisions, and provides guidelines for responsible data usage. It also creates transparency for stakeholders, building trust in how AI is being leveraged.

Navigating Security and Confidentiality Concerns

For many executives, security and confidentiality concerns represent the most significant barrier to AI adoption, particularly regarding large language models (LLMs). These concerns are valid but manageable with proper governance.

The key is implementing appropriate safeguards, including data anonymization, secure API integration, and clear policies on what information can be processed through external AI systems. Many organizations are finding success with hybrid approaches that leverage both internal, secure AI systems for sensitive data and external tools for non-sensitive tasks.

A Familiar Turning Point

The current AI inflection point bears striking similarities to previous technological revolutions that transformed the insurance industry. In the 1980s, early PC adopters gained significant advantages in data processing. In the mid-1990s, companies that embraced the internet established digital presences that became crucial competitive differentiators.

Today's AI revolution represents a similar watershed moment. Early adopters are establishing capabilities and competitive advantages that will be increasingly difficult for laggards to overcome.

Your Next Move: Strategic AI Adoption

For P&C claims organizations, the path forward is clear: Strategic, governed AI adoption is now mission-critical. This doesn't mean reckless implementation but rather a thoughtful approach that balances innovation with appropriate governance.

Begin by assessing your claims processes to identify high-impact AI opportunities. Develop a formal AI policy that addresses governance, security, and ethical considerations. Invest in training to ensure your workforce can effectively leverage AI tools.

The time for tentative exploration has passed. The organizations that will thrive in the next era of insurance are those taking decisive action today to harness AI's transformative potential in claims.


Marc Lanzkowsky

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Marc Lanzkowsky

Marc Lanzkowsky, JD, is executive managing director at Lanzko Claims Consulting.

He has over 25 years of experience in insurance and risk management. After earning his Juris Doctorate from Pace University School of Law, he was a vice president of claim operations at Healthcare Risk Advisors and senior vice president at Arch Insurance. He has also held leadership positions at FTI Consulting and Zurich Insurance.

5 Strategies Reshaping Insurance in 2025

Insurers that position themselves as risk partners—not just risk protectors—will win market share and customer loyalty.

Floating Rainbow Iridescent Soap Bubbles Outdoors

The global insurance industry stands at a crossroads in 2025. Economic instability, climate disruption, evolving customer expectations, and fast-paced technology shifts have created a market defined by uncertainty and opportunity. For insurers, the question is no longer just about growth—but about resilience, relevance, and reinvention.

In this landscape, winning is not about being the biggest—it's about being agile, innovative, and customer-obsessed. 

Here are five forward-thinking strategies that can help insurers not only survive but thrive in 2025 and beyond.

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1. Build Resilience With Real-Time Risk Intelligence

The traditional approach to underwriting and risk assessment—largely historical and static—no longer cuts it in a hyper-dynamic environment. From climate events to cyber threats, risks are changing faster than insurers can traditionally model.

The winning strategy:

Leverage real-time data and AI-driven analytics to assess and price risk dynamically. Insurers must invest in IoT integrations, geospatial intelligence, and machine learning to evaluate risks as they evolve. For instance, property insurers can use satellite imagery and sensor data to adjust policies instantly during extreme weather.

This level of adaptability not only enhances profitability but also builds customer trust—especially when insurers proactively manage risk instead of just reacting to it.

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2. Redefine Customer Experience Through Personalization

Today's insurance customers—especially Gen Z and millennials—expect services that are fast, digital-first, and tailored to their needs. Yet many insurers still operate on legacy systems with generalized offerings and one-size-fits-all communication.

The winning strategy:

Use customer data to power hyper-personalized experiences, products, and pricing. AI-powered virtual assistants, predictive analytics, and digital onboarding tools can help offer customized policies in minutes. Beyond convenience, personalization also increases customer retention and boosts cross-selling opportunities.

Additionally, insurers must rethink customer engagement—shifting from a transactional model to one that builds relationships. Think wellness programs for health insurance or safe-driving rewards in auto coverage. The goal is to be present and valuable between claims, not just during them.

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3. Operational Agility: Automate, Streamline, Scale

Operational efficiency is no longer about cost-cutting—it's about being fast and flexible in responding to market changes. In 2025, the most successful insurers are those that can launch products quickly, resolve claims swiftly, and make real-time policy adjustments.

The winning strategy:

Adopt cloud-native platforms, invest in low-code/no-code tools, and automate everything from claims processing to compliance reporting. This isn't just about reducing manual work—it's about freeing human talent to focus on high-value, strategic tasks.

Modular product architecture can allow insurers to rapidly introduce micro-insurance products or temporary coverage for niche needs like gig workers, short-term travel, or cyber risk.

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4. Embed Sustainability and ESG at the Core

Regulators, investors, and customers are increasingly demanding accountability when it comes to environmental, social, and governance (ESG) factors. Insurers are uniquely positioned to influence responsible behavior—through both their underwriting policies and investment strategies.

The winning strategy:

Develop green insurance products (e.g., lower premiums for eco-friendly homes or EVs), and integrate climate risk assessments into underwriting. Align the investment portfolio with ESG goals, such as supporting renewable energy projects.

Insurers that demonstrate purpose-led values and environmental stewardship are more likely to build customer loyalty, meet compliance requirements, and attract ESG-conscious investors.

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5. Foster a Culture of Continuous Learning and Innovation

In an uncertain world, innovation is not a luxury—it's a survival skill. Insurers that cling to legacy thinking risk being left behind by insurtech disruptors and digital-native competitors.

The winning strategy:

Promote a culture of experimentation, invest in digital upskilling, and create innovation labs or partnerships with startups. Encourage teams to pilot new ideas, fail fast, and learn quickly.

Moreover, future-ready talent is critical. As underwriting, claims, and customer service become more tech-driven, reskilling the workforce and redefining roles will help insurers stay ahead of the curve.

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Looking Ahead: From Risk Manager to Risk Partner

2025 is not just a test of operational strength—it's a test of vision. Insurers that position themselves as risk partners—not just risk protectors—will win market share and customer loyalty.

By helping businesses and individuals prevent losses, recover faster, and plan ahead, insurers can transform from reactive providers to proactive enablers of resilience.

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Conclusion

The insurance industry in 2025 is being shaped by forces that demand transformation—not tweaks. The winning formula is clear:

  • Be dynamic in risk intelligence
  • Be personal in customer engagement
  • Be agile in operations
  • Be ethical and future-focused in values
  • Be bold in innovation

Those who embrace change will not only withstand the uncertainty—they'll define the future of insurance.

When Foreign Policy Becomes Economic Policy

Triple-I Chief Economist Michel Léonard says the confusion in the U.S. economy stems from an unlikely source: a radical shift in foreign policy.

Michel Leonard ITL quarterly interview

Paul Carroll

Keeping in mind that conditions are changing rapidly, I’ll go ahead and ask, What's happening with the economy right now, and why are we seeing such significant disruption? 

Michel Léonard

We have basically been rethinking 50 years of American foreign policy and domestic policy, but mostly foreign policy. The State Department's mission, U.S. foreign policy’s mission, is to protect American citizens abroad and America’s economic interests abroad. It’s not surprising that, as we're rethinking our foreign policy or alliances, it's also disrupting the economy.

Institutions such as NATO [North Atlantic Treaty Organization], the IMF [International Monetary Fund], and the World Bank have been significant contributors to supporting American hegemony. For instance, the NATO charter says its supreme commander must be American. What does that mean in practice? It means that all NATO members' armed forces are, arguably, directly under the direction of the U.S. general in charge of NATO. And it’s been a practice that NATO members buy American weapons, American planes, and so forth.

Traditionally, the World Bank has an American as its head and the IMF a European. The World Bank was created in part to facilitate decolonization and the IMF to facilitate transition to market economics. In the second half of the 20th century, both organizations contributed to expanding U.S. influence and to reducing, among others, France's and the U.K.’s influence in their pre-war spheres of influence.

It's somewhat challenging to see the lack of understanding that these institutions contribute to U.S. strength, not U.S. weakness. We're transforming these institutional tools, part of a system that served us very well, that put us and kept us at the pinnacle of growth, wealth, consumption, and quality of life. And I don't think folks, whether on the left or the right, Democrats or Republicans, completely understand what’s happening and what that means for U.S. economic strength.

I’ll stop there so we don’t head into politics. 

Paul Carroll

Some of the projections about the impact of tariffs haven’t shown up in the numbers yet, but consumer confidence is way down. What impacts might we see in the coming months? 

Michel Léonard

The polarization in the U.S. has led to significantly different expectations of the economy and, as you're pointing out, the numbers (such as GDP and CPI showing tariffs’ impacts to date) haven't turned out to be what the consensus among economists was when tariffs were announced. Indeed, five months into the year, the U.S. economy remains more resilient than originally expected. There are some reasons for that: Inventories, for example, have been cushioning companies and softening or delaying increases in prices. Tariffs themselves have been implemented, suspended, reimplemented, and so forth. 

But we are indeed starting to see an effect with prices and CPI. And remember, price and inflation data are survey-based, and that's month to month. So, the data we are seeing now in May is already a month or a month and a half behind. On GDP, it's basically half a year. I think when the data is revised, it will show that consumers have it right, that the low confidence we’re seeing now will ultimately be justified by a higher CPI – they are living this inflation on a daily basis. Prices have increased significantly after several years of consecutive increases. 

And this time, unlike during COVID, people don’t have the option to stay home instead of commuting, which absorbed some of the inflation. Commuting is very expensive, as is eating out in downtown areas while working. I believe the economic numbers are actually worse than what we're currently seeing in the growth data, and inventories are being depleted. We're approaching that critical point now. 

The job market has tightened. Jobs are still growing, but at a much slower pace. This means the competition for employment is about to change, with job seekers losing bargaining power.

As you're pointing out, consumer confidence is at a low. What really matters most here is not the specific number. I always say it's better to be generally correct than precisely wrong. What matters here is that the current confidence numbers are comparable to during the financial crisis, the pandemic and the oil crisis.

There are significant differences between the University of Michigan and the Conference Board surveys, but they both point to consumers expecting prices to increase significantly as we go into Q3. And that has started: for example, the announcements from Amazon and Walmart and others, because inventories are being depleted. Both surveys also state that consumers expect further weakening in the job market, especially as inflation picks up.

Paul Carroll

How does tariff-related inflation differ from traditional inflation, and why don't the Federal Reserve's typical tools work as effectively against it?

Michel Léonard

I'm going to reference [former Fed Chairman Alan] Greenspan here. He used to say when it came to interest rates and monetary policy, "The medication must fit the disease." What he meant is that interest rate increases are the right medication for demand-driven inflation. When people are buying more and you want them to buy less, you raise interest rates. You're effectively making it more expensive for everyone to purchase, especially those who do so with credit cards or when buying homes and other big-ticket items like cars. 

What we're experiencing now is fundamentally different. We don't have more people or money competing for a stable supply of goods. Instead, we have decreasing supply, while consumer demand is also decreasing due to weakening sentiment and confidence. The issue is that supply is decreasing faster than demand, and that’s driving prices up. This inflation is supply-side driven. Therefore, increasing interest rates won't have the intended impact to decrease inflation. Consumer demand is already contracting, and rate hikes will have little effect on the supply constraints. We need to increase supply, and the answer there lies in trade policy. That's a political decision. The administration has stated its goals, though some aspects remain uncertain. 

Going back to Greenspan's principle, increasing interest rates would be the wrong medication for supply-driven inflation caused by tariffs. Technically, we would still decrease consumption if we increased interest rates. But to achieve meaningful impact, the Federal Reserve would be unlikely to succeed with modest 25-basis-point increases. The Federal Reserve would need bigger increases and a faster pace over several consecutive hikes. The Fed would need to telegraph to markets, companies, capital, banks, mortgage lenders, and auto financing companies that they'll keep raising rates. 

This approach would likely bring the economy to a complete standstill. For the property and casualty insurance industry, the goods traditionally most affected by interest rates increases are big-ticket items: homes, home improvements, cars, and major appliances – especially those typically purchased with financing. The P&C industry is fundamentally about replacing, rebuilding, and repairing. 

When looking at our forecast for 2025 and 2026, we expect P&C underlying growth, which is still above U.S. GDP growth in May 2025, to reverse over the next four or five quarters and start growing more slowly than overall U.S. GDP. We've always anticipated this shift. But what we're seeing now is that tariffs' impact, which would be worsened by interest rates increases, is shortening that period. If we previously expected four months of performance where P&C growth exceeded overall GDP, we now see perhaps three to four months. If conditions worsen further, that might shrink to just two to three months. 

Paul Carroll

What is your assessment of where the economy and markets are headed given the current political climate and protectionist policies? 

Michel Léonard

The way I've been thinking about this is through the lens of our international institutions. These multilateral organizations were designed first to create and then maintain a world with the U.S. at the center—militarily, economically, and diplomatically. We effectively had the world as our playground. Right now, we are pursuing decoupling. We haven't fully decoupled yet, but that's the goal.

If we continue decoupling, we would essentially reframe our economy from global to national— our market from 7 billion to 350 million people. I was speaking yesterday with financial professionals in Canada who noted that they are not seeing the same tariffs inflation as the U.S., and that while bilateral trade uncertainty with the U.S. is damaging GDP growth, the Canadian economy is seeing increases in investments from other countries that are now reluctant to invest in the U.S. Governments within the European Union and Canada have been eager to create other free trade zones and facilitate trade among themselves.

The implications are significant. For equity markets, U.S. companies are currently at the top of the global system with significant market access and the benefits of the dollar as the reserve currency. Equity valuations for the likes of Apple, Microsoft, our energy companies, and our banks are based on those companies being global. But if U.S. protectionism is met with retaliatory protectionism, American companies may suddenly lose access to a significant share of that global market. At the extreme, they may be left only with the U.S. market – say 75% fewer consumers and opportunities. When people ask me where's the floor for equity, I think of that 75% market loss scenario. I'm not saying it will happen tomorrow, but certainly in the longer term, there's no floor. The floor is being moved downward. 

In terms of fixed income, the curve is steepening, getting closer to what, I would argue, it should be. One should receive more money for committing funds for longer periods—at least enough to cover inflation. Otherwise, you're losing money. We haven't had a significant steepening curve for a long time, so I think that can be construed as positive, especially because bonds and CDs are the backbone of how middle-class households build wealth. 

In terms of the deficit, I don't think the deficit will significantly affect this dynamic. Economists have been warning for 50 years that we'll have a problem and the dollar will weaken because of growing deficits, but it hasn't happened. There are other intermediary variables at play. I'm less concerned about the deficit and a weaker dollar, but fixed income yields in the short term will probably increase. 

The concern [among rating agencies on the quality of U.S. debt] isn't really about debt but about policy uncertainty. This uncertainty could drastically affect the cost of money, potentially contributing to a recession. Ironically, protectionism would also act to depress prices, creating conflicting dynamics. 

For employment, the same thinking applies. Many American jobs at companies like Microsoft are supported by international operations. Similarly, in the insurance industry, while many carriers are domestic, we also have brokerage firms that operate internationally, as do many banks. 

Have we fully decoupled? No. Is it done? No. Is there a way back? I don't think we've gone that far in decoupling yet. But if the rest of the world sees this as a fundamental shift in what Americans expect rather than just a temporary change in administration, then we'll start seeing more permanent changes. Such a transformation would take 10 to 20 years, but in that scenario (a full decoupling, as unlikely as this can be at this point), our equity markets could potentially shrink by 75%. 

Paul Carroll

Wow.

What lessons can we draw from economic confidence crises like the one I witnessed while running the Wall Street Journal bureau in Mexico City in the mid-‘90s, and how might they apply to our current economic situation? 

Michel Léonard

This is a great example. Mexico, like Argentina, demonstrates economic resilience despite pessimistic predictions. After each emerging markets bust, industry and finance people often claim, "It's over, investors won't return,” but they always do. Bondholders keep going back to Argentina. We remain a huge market, and that's not going to change.

Free trade advocates have always acknowledged that open trade borders lead to jobs redistribution—lower-skilled jobs move to countries with cheaper labor. Under U.S. leadership, the world economy has grown incredibly. The Chinese couldn't have lifted hundreds of millions out of poverty and created a middle class without global trade. The same applies to India and Pakistan. This historic transformation deserves applause. 

The flip side is that substantial wealth transferred from the U.S. abroad, disrupting communities throughout America. Those good union and public sector jobs—many are gone. Unionization rates have declined. Pro-free trade economists and policymakers always said we need to retrain our people. Montreal, where I'm originally from, did this exceptionally well. It was like the Rust Belt—losing its harbor and entering a structural recession. But all levels of government there invested heavily in retraining, for example creating thriving IT, aerospace, and hospitality sectors.

When a significant portion of society feels disenfranchised and unrepresented—this is Political Economics 101, not politics—they disconnect. They turn to third parties or, in emerging markets, fundamentalism. For our democracy to survive, we need an economic system where everyone feels their prospects can improve. We're not aiming for France's economic model — I don't think Americans want that—but we need a system where everyone believes their situation can get better. That's a laudable goal this administration is trying to achieve, and I hope there's a middle ground. All of that said, we can contribute to people understanding the impact of policy choices regarding prices and inflation.

Paul Carroll

Thanks, Michel. I feel smarter than I was 20 minutes ago. I hope our readers and listeners feel the same way.


Insurance Thought Leadership

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Insurance Thought Leadership

Insurance Thought Leadership (ITL) delivers engaging, informative articles from our global network of thought leaders and decision makers. Their insights are transforming the insurance and risk management marketplace through knowledge sharing, big ideas on a wide variety of topics, and lessons learned through real-life applications of innovative technology.

We also connect our network of authors and readers in ways that help them uncover opportunities and that lead to innovation and strategic advantage.

What's Going on With FEMA?

Amid a shifting cast of characters and conflicting statements about plans and funding for FEMA, two things are finally becoming clear.

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FEMA

David Richardson, the acting head of the Federal Emergency Management Agency (FEMA), told staffers two weeks ago he was surprised to learn there is an annual hurricane season. The Trump administration put out a statement saying Richardson was joking, but Reuters quoted staffers as saying he seemed serious. And the Wall Street Journal separately reported that Richardson, who had no experience in disaster management when he was named to the job in May, has been surprised to learn of the breadth of FEMA's responsibilities.

Richardson's confusion comes on top of a whole lot of other confusion at the agency — an announcement promising a new disaster management plan, then an announcement that there will be no new plan this year; disaster recovery grants delayed, then provided, but only on an ad hoc basis; and a whole lot of mutating policy statements about scaling back or even eliminating the agency.

How do we make sense of all that?

I've been waiting and watching to try to understand what's going on and how it might affect insurers that provide coverage for disasters, and I think two things have finally become clear. 

One is good for insurers. One is bad for them. Both are bad for homeowners and other policyholders.

Richardson was named acting administrator at FEMA after his predecessor was fired, apparently, for telling Congress FEMA should continue to exist. That suggests strongly that the Trump administration's sometimes conflicting statements do reflect a plan to drastically scale back or even eliminate FEMA and the assistance it provides following natural disasters. 

Richardson said back in May that states would have to bear 50% of the cost of disaster recovery, up from the previous 25%. More recently, Trump has said he will mostly wind down FEMA, although not until after hurricane season.

Under the Constitution, only Congress can abolish an agency such as FEMA, and the executive branch is required by law to spend the funds that Congress allocates, but the Republican-controlled Congress has shown no inclination to push back against Trump's assertions of authority. Even if Congress suddenly reclaimed its authority, Trump has considerable executive power to deny or at least delay grants to states and to fire FEMA officials who stand in his way.

So he is making the federal government an unreliable backstop for people and communities facing calamities. That uncertainty will hang over FEMA even if Democrats retake control of one or both houses of Congress in the mid-term elections or if the next president takes a more traditional view of FEMA's role in disaster recovery. (This article in Slate does a great job of showing what the FEMA chaos already means on the ground in areas hit by disasters and how individuals and states are having to scramble.)

The step backward by the federal government will hurt property holders — and my heart is always with those suffering from natural disasters — but will, in fact, help insurers. Property holders now carry more risk than they did pre-Trump, and they're going to want to lay off some — maybe even a lot — of that risk with insurers. 

Even state governments, strapped for funds, may turn to private insurers for help with the risk the federal government is handing to them. (The Trump administration line is that states are being "empowered" to do more about disaster recovery, but states don't seem to see things quite the same way.)

The second thing that has become clear about Trump's FEMA is rough both for policyholders and for insurers. It is that Trump has little or no interest in a program called Building Resilient Infrastructure and Communities (BRIC). 

The program has long been used to help areas hit by disasters make themselves less vulnerable to future catastrophes, and it meshes — or meshed — with the Predict & Prevent movement in the insurance industry. 

Groups such as the Insurance Institute for Business & Home Safety have been promoting standards such as FORTIFIED roofs, and insurers have been working with communities to help prepare them for wildfires, hurricanes and other natural disasters. The expectation has been that the federal government would at least be some sort of partner, providing expertise and a fair amount of funding. Not any more. 

This post by the Triple-I quotes one expert as saying, “Eliminating [BRIC] entirely — mid-award cycle, no less — defies common sense,” and details how the decision pushes responsibility to state and local governments and to private interests, including policyholders and their insurers.

Here's hoping this year is kinder than recent ones in terms of hurricanes, tornados, severe convective storms and wildfires, but I'm not counting on any relief. And even FEMA says it is "not ready."

Cheers,

Paul

 

 

Mary Meeker Weighs in on AI

The high-profile analyst shows that AI has been improving far faster even than we realize and that progress is accelerating, with no end in sight. 

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ai brain

Mary Meeker, a high-profile analyst known as "the Queen of the Internet" because of her early, bullish calls on the prospects for Amazon, Google and Apple and then for the massive yearly reports she issued on the state of the internet, has turned her attention to AI. 

In her first major report in five years, Meeker makes the case that AI has been improving far faster even than we realize and that progress is accelerating, with no end in sight. 

Her forecast — and she's generally right — should be very good news for insurers.

Meeker's report, all 339 data-and-graphics-packed pages of it, uses the word "unprecedented" dozens and dozens of times. I'll spare you the detail, but here are a few nuggets you might want to include in any presentation arguing for investment in AI:

  • ChatGPT hit one billion searches per day in less than two years. Google needed 11 years to reach that mark.
  • While developing and training generative AI models is wildly expensive, the cost of using AI has declined 99% just in the past two years.
  • The “Big Six” U.S. tech giants (Apple, NVIDIA, Microsoft, Alphabet, Amazon and Meta) are going to keep spending unfathomable sums to improve capabilities and drive costs down. They spent $212 billion on capital expenditures in 2024, up 63% from 2023, largely on AI chips, data centers, and cloud infrastructure. They are investing 13% of revenue on R&D, up from 9% a decade ago, and they have the resources to keep going: Their annual free cash flow is nearly $400 billion.
  • While there is a lot of concern about the energy consumption of AI, Nvidia's latest 2024 Blackwell GPU achieves 105,000 times greater energy-efficiency than its 2014 predecessor. 

Meeker also offers many useful examples of how generative AI is, and will, find its way into the real world: 

  • More than 10,000 doctors at Kaiser Permanente use an AI assistant to automatically document patient visits, freeing three hours a week for 25,000 clinicians.
  • Stripe pushed one important fraud-pattern catch rate from 57% to 97% overnight.
  • 27% of ride-hailing trips in San Francisco are handled by autonomous vehicles. (That's much higher than I would have guessed.)
  • By early 2025,, evaluators thought 73% of the output from a GPT was written by humans.

Meeker's projections for five and 10 years out may be even more startling. By 2030, she expects that AI will:

  • Generate human-level text, code and logic, in any number of languages.
  • Run autonomous customer service and sales.
  • Collaborate like a creative partner.

By 2035, she says AI will:

  • Conduct scientific research
  • Design advanced technologies
  • Simulate human-like minds
  • Operate autonomous companies
  • Perform complex physical tasks in real-world environments

Any projections for technology that reach out a decade often verge on science fiction or at least fuzzy optimism -- a lot of projections about AI, for instance, were "coming in 10 years" for decades. But Meeker paints a picture of intriguing possibilities that we should all explore.

A lengthy analysis of her report on Substack offers an even rosier outlook for insurers. It says that, while many companies and jobs will be overtaken by the growing power of AI, it won't threaten businesses that have these three levers:

  • "Data gravity – proprietary or regulated corpuses (medical imaging, trade documents, tele-metrics) that outsiders cannot legally pull into pre-training.
  • "Reward ambiguity – industries where you can underwrite the outcome (fraud risk, quality-of-care scores, turbine uptime) and price on financial exposure.... Risk pays!
  • "Compliance bottlenecks – any workflow where passing the audit is the moat."

That sure sounds like insurance to me: proprietary data, underwriting of risk, and compliance bottlenecks. So insurers can take advantage of the huge amounts of horsepower that the gen AI model companies are providing, while secure in the knowledge of the health of the underlying business.

Insurers can now start to raise their sights. At the Instech conference in New York City last week, where I had the pleasure of speaking, I heard about remarkable improvements in data intake from Concirrus, Cytora, and Federato, based on AI engines. By next year, I'd expect to hear about similar progress in the assistants that companies are building for underwriters, claims representatives, and agents and brokers so they can process more information faster and make better, more consistent decisions. The year after that, I'll bet we're hearing about whole streams of work being automated through AI.

In time, I suspect we'll stop even talking about chatbots because the capabilities will be built into everything, making AI essentially the user interface for companies. We'll just go to a website or make a call and ask a question. AI will then provide a summary answer, much as Google and other search engines are now doing, and offer next steps. 

Eventually, the arms race by gen AI companies may slow, as losers drop out of the competition. At that point, prices for us users could rise, or at least stop plummeting, following much the same dynamic that saw Uber and Lyft raise prices after years of subsidizing rides to lock in interest among riders and drivers.

But Meeker makes clear that any slowing won't come any time soon. For the next few years, at least, it's full speed ahead.

Cheers,

Paul

Generating Underwriting Capacity Via Agentic AI

Agentic AI is emerging as insurance carriers' solution to operational underwriting constraints in a talent-starved market.

Symbolic Graphic Representation of AI

When insurance personnel speak about underwriting capacity, they usually are referring to underwriting compliance, risk-based capital (RBC) models, or reinsurance. Less commonly, carriers think about operational underwriting capacity. In this context, operational underwriting capacity refers to a carrier's ability to balance speed, risk, and resources to meet the demands of sales and distribution. Key considerations in evaluating operational underwriting capacity include:

  • Agent and Customer Expectations – Agents and customers expect policies that can be issued quickly and accurately. For agents, this means strong quoting capabilities and fast cycle times. Policyholders are increasingly seeking instant decisions, wishing to avoid more invasive measures to underwrite policies (e.g., medical exams).
  • Talent Considerations – Carrier struggles for underwriting talent are pervasive within the industry, fostering underwriting "hubs" within the U.S. to ensure the ability to attract talent (e.g., Charlotte, N.C.). But access to underwriters is only one part of the equation. Complicated risk also requires specialized skillsets, and all carriers are competing for the best underwriters.
  • Risk Assessment Framework – Underwriting has often relied on guidelines and rules-based processing in risk assessment. But much of that framework relies on historical data that leads to inefficient pricing – either overpricing, harming the customer, or underpricing, putting the carrier at increased risk.

The need to address underwriting capacity is not new. Carriers have already pursued rigorous investment in underwriting. In 2024, property & casualty carriers reported a $22.9 billion underwriting gain and industry combined ratio of 96.6%, per AMBest. For life insurers, 2024 saw 3% growth in premium but flat growth in policies. Increased sales in indexed universal life and variable life policies drove premium growth - both products requiring more sophisticated underwriting skillsets.

For carriers, this means pressure to manage expenses, as well as innovative underwriting capabilities, to compete in the market.

One avenue for innovation to create greater operational capacity? Agentic AI. With agentic AI, carriers have the capability to tackle several underwriting challenges.

Leveraging Data to Create More Dynamic Underwriting

Agentic AI can be used to conduct real-time data analysis across a myriad of data sources to better underwrite risk. Behavioral data (e.g., telematics) and IoT sensor insights (e.g., home sensory equipment) have fundamentally changed how carriers can price risk in a dynamic way. For instance, Nationwide has reported that customers enrolled in its usage-based insurance programs tend to pay 20% less than those enrolled in traditional policies. Hippo Insurance has used sensors to detect smoke, carbon, and water leaks, resulting in discounted and customizable products for customers. Agentic AI performs this data analysis to create much more tailored customer segments for pricing purposes.

Although property & casualty is leading the way in this space, expect life and health insurers to follow suit. The opportunity to promote healthier living and improve longevity risk for carriers, using behavioral data and sensors, will improve underwriting. John Hancock is using data from connected devices like FitBit and Apple Watch to provide customers with the opportunity to reduce premium payments while derisking their life insurance business.

Improved Fraud Detection

Agentic AI is capable of identifying fraud before the policy is ever issued. Specifically, it can be trained initially on known fraud practices, freeing existing personnel to focus on more nuanced cases. Over time, carriers can train agentic AI to recognized more sophisticated fraud scenarios. As carriers seek to increase sales, both through premium and policies sold, well-developed agentic AI will be critical to scalability. For example, agentic AI can recognize fraudulent or digitally altered data to either automatically flag or reject an application. This can be particularly valuable in situations where AI can identify fraud more accurately than its human counterpart.

One property & casualty insurer developed an agentic AI PoC focused on identifying policies written by "ghost brokers," individuals who were not authorized to sell policies. In addition, the carrier improved their model's capability to detect misrepresentation, particularly during the "free look" period, to further attack fraud in the underwriting process.

Underwriting Copilot and Training Opportunities

Underwriting triage is a foundation of risk management. This is especially pronounced in complex claims situations, where more experienced underwriters are needed, creating process bottlenecks.

Carriers should consider using agentic AI as both a copilot and triaging tool. As a copilot, agentic AI can accelerate the training process for underwriting trainees, providing real world scenarios and the opportunity to "grade" the underwriter in real time for accuracy. But as a triage tool, an agent can bypass inefficient workflow processes and better manage capacity within the organization. Many underwriter teams are regional – for example, an underwriting team for a captive life insurer may be based in the Southeast to directly support agents within the region. Or there may be a property & casualty commercial insurer that is responsible for a given territory. While that model may continue to be necessary, agents can be used to prioritize and redistribute cases based on need. For example, a commercial property & casualty carrier can use an agent to identify the complexity of a given renewal, assign it to the next capable underwriter, and prioritize them based on the urgency and estimated time to complete them.

Where Do Carriers Go From Here?

While the exact results will be carrier-dependent, a commitment to agentic AI within underwriting will position carriers to be better prepared to meet both financial obligations and consumer sentiment.

As carriers design their underwriting strategy, they should consider if they have the requirements to execute an AI strategy in the space:

  1. Data Quality and Data Sources – Without the right data, agentic AI is bound to fail. Carriers need to consider what internal and external data sources they want to use, how to remediate their internal data, and how to integrate external data sources into their underwriting platforms.
  2. AI Governance Structure – At least 30% of AI use cases are abandoned after proof of concept, per Gartner. This is due to companies rushing to do something with AI without any plan. A proper governance structure not only provides a method for evaluating AI use cases at an enterprise level but also provides clear metrics and considerations that will be necessary to address regulatory scrutiny as AI regulation continues to develop.
  3. Rethinking the Underwriter of Tomorrow – At the core of operational underwriting capacity are underwriters. Carriers need to rethink the entire underwriting function and decide what an underwriter will need to do in the not-too-distant future. For example, will underwriters still need to perform data entry functions or play a more collaborative role with agents or brokers in sales? This exercise typically highlights a key challenge – that there is significant upskilling required within the existing workforce to address underwriting change.
  4. End-User Experience – As insurance carriers consider the future of underwriting, there must be a recognition that this is not happening in a vacuum. Competitors are also reevaluating their own underwriting processes. As carriers rethink underwriting, they should reconsider the experience with three lenses: agent, customer, and employee. A winning strategy will optimize the experience for all three stakeholders as a means of capturing and retaining all three.
  5. IT and Process Transformation – Fundamentally, carriers need to reassess their underwriting function and engage in a potential core system modernization. Many carriers have not made investments into their underwriting platforms or modernized processes. For example, a lack of application programming interface (API) connectivity with underwriting platforms may limit the ability to integrate data necessary for agentic AI use cases.

The ability (or inability) of carriers to supplement their underwriting capabilities with agentic AI will affect their profitability and sustainability long-term. Customers, agents, underwriters, and financial investors will demand agentic AI. This cannot be achieved overnight and will require forward-thinking leaders.


Chris Taylor

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Chris Taylor

Chris Taylor is a director within Alvarez & Marsal’s insurance practice.

He focuses on M&A, performance improvement, and restructuring/turnaround. He brings over a decade of experience in the insurance industry, both as a consultant and in-house with carriers.

AI and IoT Redefine Risk Management

AI and IoT transform insurance risk management from reactive pricing to loss prevention.

Person Standing While Using Phone

Despite the buzz around digital transformation, a staggering 74% of insurance companies still use legacy systems to carry out their daily operations.

Hindsight has been the guiding light of risk management. Underwriters have used backward-looking data to evaluate risks, and loss events have been the driver of policy adjustments.

However, this method is now rapidly losing ground, thanks to the revolution of AI and IoT.

The current climate of volatility, looming cyber threats and supply chain fragility have created a world of escalating risks requiring more than a basic reactive model. Insurer needs something smarter, a more forward-looking approach that deeply involves tech in risk strategy.

That's where the Internet of Things and artificial intelligence are driving change. Forming the heart of the future of insurance risk management, their combined powers transform static risk profiles into dynamic systems that are capable of predicting, detecting and even preventing losses in real time. Powered by machine-learning algorithms, these systems don't just flag risks -- they consistently learn and adapt so insurers can be one step ahead of the risk lifecycle.

In this article, we will explore how AI and IoT are redefining the insurance landscape with advanced technologies like real-time risk scoring and hyper-personalized coverage.

We'll also discuss practical uses, hurdles to implementation and what insurers like you need to stay ahead of the curve.

The Evolution of Risk Management in the Insurance Industry

Once upon a time, risk management was a manual process, heavily dependent on spreadsheets, static questionnaires, and actuarial tables. But there's been a dynamic shift since then, ushering in brand new processes of real-time data analysis and algorithmic decision-making.

On the surface, it might seem like an optional shift. It is anything but.

As regulatory bodies demand greater transparency and faster reporting, customers are seeking more personalized support and responsive coverage. This massive shift makes the proverbial one-size-fits-all policies obsolete.

Armed with data-driven risk models instead, insurers can now easily leverage the insights that structured and unstructured data provide to make swift and accurate underwriting decisions. This data can come from anywhere -- be it financials and claims history or telematics and weather feed.

With predictive analysis, you can now keep an eagle eye on trends before they escalate. You can also adjust policies on the basis of real-time exposure and behavior with dynamic underwriting.

Calling these innovations revolutionary will be no understatement. The convergence of AI and IoT has transformed risk from something to price and transfer to a process that involves anticipation, monitoring and management.

The era of the retrospective stance is over as the age of forward-leaning approach takes over with AI and IoT in the driver's seat.

Role of IoT in Real-Time Risk Detection and Prevention

The Internet of Things or IoT might be best-known for connecting devices. But its not-so-glamorous role of helping insurers detect, assess and mitigate risks in real time is just as critical.

Devices such as telematics in vehicles, wearables, smart home sensors and industrial IoT create a continuous loop of feedback between insurers and insured assets.

How do these devices and their always-on data stream change the game? Take smart thermostats. These can spot a frozen pipe before it bursts. Meanwhile, telematics can identify high-risk patterns from a person's driving behavior even before an accident takes place. Industrial sensors can prevent workplace accidents by flagging faulty machines. Each data point can prevent critical loss.

That's why insurers now rely on IoT for multiple tasks, including sharing more alerts and building nuanced risk profiles so premiums can be adjusted in a dynamic fashion. In fact, IoT has also been instrumental in lowering costs associated with insurance claims processing by up to 30%, per Mordor Intelligence.

However, there's a technical issue, and that involves figuring out how to leverage large datasets. Sensor data can be unstructured - not to mention high-volume.

This is where custom-built software platforms can help. These solutions are capable of ingesting large amounts of data from diverse sources -- both processing and integrating them with legacy systems in real time to save you a ton of time, money and hassle.

With custom software in place, you can tap into the full potential of IoT, thus turning reactive claims into proactive risk management.

AI-Powered Risk Scoring and Underwriting

There's no doubt that IoT has revolutionized risk management -- but so has AI. With AI, you can make sense of the massive, fragmented data streams that keep pouring in from internal systems, connected devices and third-party sources. Plus, fast and smart underwriting is possible with AI.

While underwriting traditionally depended on backward-looking data, AI shifts it to the present by processing real-time data -- contextual, environmental and behavioral -- to generate dynamic risk scores unique to each profile.

As a result, pricing is now not probability-based, relying on historical cohorts. Instead, it reflects real exposure.

Take the recent insights released by McKinsey which show that insurers that use AI in underwriting have witnessed loss ratio improvements of up to 5%. That's not all. They have also seen expense reductions of 10%–15%. And this is just the beginning.

Personalization is another major advantage when it comes to AI insurance models. You can use AI to gather lifestyle factors and wearable data of your customers to craft personalized plans for them with dynamic premiums that adjust to their real-time behavior.

Property insurers can use AI to determine occupancy trends and environmental risks when drafting plans -- a granularity level that was impossible in the days of manual underwriting.

Consequently, with the advent of AI, insurance policies are now not only highly customizable but also very adaptive to changes. Moreover, AI can trigger early interventions, adjust coverages and flag anomalies before the commencement of a renewal cycle.

The presence of AI in insurance might seem futuristic, but incumbents and startups are already leveraging AI-based underwriting engines to prevent fraud and improve accuracy while keeping personalization as the basis for all liaison.

Lemonade uses AI bots and behavioral data to assess risk in real time, settling some claims instantly while reducing loss ratios and operational costs.

Lemonade uses AI bots and behavioral data to assess risk in real-time, settling claims instantly while reducing loss ratios and operational costs.
https://d3.harvard.edu/platform-rctom/wp-content/uploads/sites/4/2018/11/Example-of-claim.png
Addressing Ethical and Operational Risks in AI Integration

As with anything new, challenges abound with the integration of AI and IoT into the mechanisms of the insurance sector. But none of the threats arise from policyholders. Rather, it's the system itself that poses risks, ranging from algorithmic bias to data privacy and regulatory scrutiny.

You see, IoT devices are responsible for collecting data -- location, behavior, even biometric information -- that can be classified as strictly personal. Use of such information without clear boundaries can be considered a breach of trust and a liability. Thus, for insurers, it is critical to protect the data and have stringent rules for using that data in underwriting, pricing, and claims decisions.

However, that's not the only AI hurdle.

Most AI models can be likened to black boxes -- which means they often make decisions that cannot easily be backed by an explanation. This can put the fairness and accountability of such decisions into question, especially when it comes to sensitive tasks like claims automation, where transparency and equity are a must.

As for regulation, jurisdictions around AI are getting tougher. Auditability and model governance are now standard practices. As an insurer, you must guarantee your system can be monitored, tested and documented for any inherent biases.

The message is clear: Without the ethical use of AI, insurers and agencies can land in hot water.

While having a well-governed AI system can boost compliance, it can also serve as a competitive differentiator -- helping insurers build trust in a world where speed with fairness are paramount.

Building a Future-Ready Risk Management Infrastructure

By the year 2027, the global insurance market is expected to reach $9.8 trillion. That's a CAGR of 12% between 2022 and 2027.

With such rapid growth in store, retrofitted tools or patchwork systems for risk management just won't do. The shift from a reactive strategy to a proactive one requires a solid infrastructure that is equally agile, intelligent and purpose-built.

The first step is to rethink your existing tech stack. Your legacy system can likely process only batches of data instead of the continuous loops that emanate from AI engines or IoT devices.

The result?

Data silos, stalled workflows and zero opportunities for intervention. Either you need to modernize these systems or build custom integrations around them to stay viable. It's the only way.

Going down the custom software solution route will offer you the flexibility to centralize disparate data sources without ripping out your core system. As a consequence, you can automate decision-making and enable modular upgrades that help your company with underwriting, claims, and compliance workflows.

That said, infrastructure isn't simply about redefining your tech stack. It's also about ensuring seamless collaboration.

As a forward-looking insurer, your aim should be to track specialized vendors you can partner with to co-develop tools that suit the operational model of your company. Such strategic alliances can benefit your business by bringing domain expertise, speed and long-term innovation to the table.

To be ready for the future, you must choose wisely. You want your risk strategy to lead in the years ahead, not lag.

Final Thoughts and Strategic Recommendations

Don't treat AI and IoT as just another tech tool that comes and goes. Instead, think of them as the switch that lets you alter your entire risk management strategy from the ground up. Both are key to accurate forecasting, faster response times and loss prevention.

With AI and IoT working together for you, you get an insurance model that benefits carriers and policyholders alike.

However, if you want a competitive advantage, being an early adopter is the only way to go. You need to be willing to address ethical risks that arise as you modernize your infrastructure by investing in the right partnerships.

The path ahead for senior insurance executives is dotted with specific tasks:

  • A thorough assessment of where and how your reactive models are falling short
  • Prioritization of AI and IoT use cases that offer long-term scalability and near-term wins
  • Modernization of legacy systems with custom platforms that enable real-time integration in a seamless fashion
  • Formation of strict governance frameworks that foster a culture of fairness, auditability, and compliance

The future of risk management is here.

But the real question for CXOs and underwriting leaders like you is: Are you ready to evolve your risk infrastructure, or are you willing to lose your competitive edge?

The choice is yours.


Dhruv Mehta

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Dhruv Mehta

Dhruv Mehta is a content marketing consultant. 

He has been sharing insights on DevOps and Software Development. 

Lessons in Managing Transformation in Insurance

Effective transformation requires focusing on change management fundamentals rather than seeking technological silver bullets.

White Arrow on a Road Surface

Change is inevitable; managing it effectively is where the challenge lies. Many transformation initiatives fail, not because of technology itself, but because of how change is managed.

Recently, I had the opportunity to participate in Send's INFUSE webinar on Managing Change, alongside industry experts, where we explored what makes transformation efforts successful and the common pitfalls that organizations face. It was a great discussion, and I wanted to share some of the insights we covered.

The Foundation of Successful Change

One of the biggest problems is poor planning. Too often, organizations become enamored with technology without considering its practical effect at the operational level. A shiny new tool means nothing if it doesn't address real pain points for employees on the ground.

A well-structured change program should include:

  • Clear Planning and Defined Success Metrics - Organizations must ask themselves, "What does success look like? What does failure look like?" Without a clear road map, businesses risk implementing solutions that fail to deliver tangible benefits.
  • Engaging People Early - The people who use the technology daily should be actively involved in planning and implementation. Their input ensures that the solution is solving real problems.
  • Focus on Outcomes, Not Just Processes - Change programs can quickly become overly detailed, leading to loss of sight of the bigger picture. Keeping the end goal in mind helps teams stay aligned and motivated.
Biggest Barrier to Change: The Human Element

While legacy systems and regulatory frameworks are common hurdles in insurance, the biggest barriers are human-centric. Underwriters, IT teams, and change managers often speak different "languages," making it difficult to align on goals. Bridging this gap requires creating a common understanding across all stakeholders.

Another major obstacle is clarity of purpose - many transformation initiatives attempt to solve too many problems at once. Instead of creating a solution that excels in one or two areas, they end up with something that doesn't really hit the mark.

Technology's Role in Change Management

Technology is a critical component of transformation, but it should never be the starting point. The biggest mistake companies make is assuming technology alone will fix broken processes. Instead, organizations should:

  • Obsess Over the Business Challenge First - Start with understanding the core problem before selecting a tool.
  • View Technology as an Ecosystem - No solution exists in isolation; successful adoption depends on integration with existing processes.
  • Avoid the "Silver Bullet" Mindset - No single piece of technology will resolve every issue. Instead, incremental improvements and phased adoption drive the best results.

A key trend emerging is custom-built AI solutions that adapt to individual user needs. In the future, organizations will move away from large, off-the-shelf systems in favor of more tailored, intelligent solutions.

Lessons from Experience

Throughout my career, I've seen many businesses invest heavily in technology, only to struggle with adoption. One of the most effective strategies I've used is implementing a "soul-sucking task list"—asking employees to list their most frustrating daily tasks. If a technology investment doesn't directly address one of these pain points, it's unlikely to gain traction.

In another case, a company assumed it had a standardized process for policy cancellations. However, when we examined it, we found three different workflows being used simultaneously. The lesson? You can't please everyone, but you can focus on outcomes. Once the desired outcome is clear, the process will follow naturally.

Final Thoughts: Embracing a Culture of Change

The key takeaway from our discussion? Diminish fear within your organization. Fear of failure, fear of job loss, fear of the unknown—these are the real barriers to change. By fostering an environment where employees feel safe to adapt and innovate, organizations can bridge the gap between technology and transformation.

As Emma Cullum, head of operational strategy change and excellence at QBE, put it:

"A child born today will experience a year's worth of change in just 11 days by the time they turn 60. The companies that succeed will be the ones that embrace this pace of change, continuously modernizing instead of waiting for a perfect solution."


Ryan Deeds

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Ryan Deeds

Ryan Deeds is an analytics leader at Alkeme Insurance.

Previously, he led customer success at ennabl, held roles in technology and data management at Assurex Global and was IT director at Crichton Group.

Lessons in Managing Transformation in Insurance

Effective transformation requires focusing on change management fundamentals rather than seeking technological silver bullets.

Change is inevitable, but managing it effectively is where the challenge lies. Technological advancements are moving at an incredible pace, creating numerous opportunities. Many transformation initiatives fail - not because of technology itself, but because of how change is managed.

Recently, I had the opportunity to participate in the INFUSE webinar on Managing Change, alongside industry experts, where we explored what makes transformation efforts successful and the common pitfalls that organizations face. It was a great discussion, and I wanted to share some of the insights we covered.

The Foundation of Successful Change

One of the biggest challenges in implementing change is poor planning. Too often, organizations become enamored with technology without considering its practical effect at the operational level. A shiny new tool means nothing if it doesn't address real pain points for employees on the ground.

A well-structured change program should include:

• Clear Planning & Defined Success Metrics - Organizations must ask themselves, "What does success look like? What does failure look like?" Without a clear roadmap, businesses risk implementing solutions that fail to deliver tangible benefits.

• Engaging People Early - The people who use the technology daily should be actively involved in planning and implementation. Their input ensures that the solution is solving real problems.

• Focus on Outcomes, Not Just Processes - Change programs can quickly become overly detailed, leading to loss of sight of the bigger picture. Keeping the end goal in mind helps teams stay aligned and motivated.

As Matt Carter, practice director at Altus Consulting, put it during the webinar:

"You have to keep an abstracted view of the prize you're going after. Programs evolve quickly, and people lose sight of the bigger picture. Keeping them focused on where they're headed ensures success."

Biggest Barriers to Change: The Human Element

While legacy systems and regulatory frameworks are common hurdles in insurance, the biggest barriers are human-centric. Underwriters, IT teams, and change managers often speak different "languages," making it difficult to align on goals. Bridging this gap requires creating a common understanding across all stakeholders.

Another major obstacle is clarity of purpose - many transformation initiatives attempt to solve too many problems at once. Instead of spreading efforts too thin, organizations should focus on one or two key areas where they can create meaningful effect.

Emma Cullum, head of operational strategy change and excellence at QBE, emphasized this during the discussion:

"One of the biggest challenges I've seen is organizations trying to do too much at once. Instead of creating a solution that excels in one or two areas, they end up with something that doesn't really hit the mark."

Technology's Role in Change Management

Technology is a critical component of transformation, but it should never be the starting point. The biggest mistake companies make is assuming technology alone will fix broken processes. Instead, organizations should:

• Obsess Over the Business Challenge First - Start with understanding the core problem before selecting a tool.

• View Technology as a Connected Ecosystem - No solution exists in isolation, successful adoption depends on integration with existing processes.

• Avoid the 'Silver Bullet' Mindset - No single piece of technology will solve every issue. Instead, incremental improvements and phased adoption drive the best results.

A key trend emerging is custom-built AI solutions that adapt to individual user needs. In the future, organizations will move away from large, off-the-shelf systems in favor of more tailored, intelligent solutions.

Lessons from Experience

Throughout my career, I've seen many businesses invest heavily in technology, only to struggle with adoption. One of the most effective strategies I've used is implementing a "soul-sucking task list"—asking employees to list their most frustrating daily tasks. If a technology investment doesn't directly address one of these pain points, it's unlikely to gain traction.

In another case, a company assumed it had a standardized process for policy cancellations. However, when we examined it, we found three different workflows being used simultaneously. The lesson? You can't please everyone, but you can focus on outcomes. Once the desired outcome is clear, the process will naturally follow.

Final Thoughts: Embracing a Culture of Change

The key takeaway from our discussion? Diminish fear within your organization. Fear of failure, fear of job loss, fear of the unknown—these are the real barriers to change. By fostering an environment where employees feel safe to adapt and innovate, organizations can bridge the gap between technology and transformation.

As Emma Cullum, head of operational strategy change and excellence at QBE, put it:

"A child born today will experience a year's worth of change in just 11 days by the time they turn 60. The companies that succeed will be the ones that embrace this pace of change, continuously modernizing instead of waiting for a perfect solution."