Following a period of historic profitability and record capital of $785 billion at the end of 2025, the global reinsurance market is entering a pivotal transition. As rate momentum stabilizes, relying solely on broad hard-market pricing corrections to drive returns is no longer a viable long-term strategy. To defend technical underwriting margins and achieve sustainable growth, reinsurers must now shift their focus from riding market cycles to generating true operational alpha. 

For business and data leaders, the mandate is clear: The experimental phase of AI is over. The next competitive frontier demands a seamless alliance between deep underwriting expertise and enterprise-grade technological capabilities, transforming data assets into the ultimate strategic moat. This article outlines the blueprint for that transformation.

Chapter 1: The New Reality of Risk (“Why”)

For decades, reinsurers have mastered 'cycle management,' thriving in both hard and soft markets by intelligently deploying capital. However, today's connected risks are making historical cycles dangerously unpredictable. We are facing a perfect storm: climate change is intensifying natural catastrophes, state-sponsored cyber-attacks threaten global infrastructure, and geopolitical tensions are fracturing supply chains.

The new masters of the cycle will not be those who simply manage capital but those who leverage data and AI to anticipate, price, and mitigate risks before they materialize. These are no longer just "emerging risks"; they are immediate, systemic threats to underwriting profitability and operational resilience. Addressing them requires a paradigm shift in how we perceive, quantify, and aggregate exposure across the globe.

Chapter 2: The Strategic Response (“What”)

To survive and thrive in this volatile new reality, reinsurers must elevate their strategic response. This is not about making incremental operational improvements; it is about establishing robust business pillars necessary to navigate an unpredictable world.

• Underwriting Discipline: Reinsurers need to prioritize technical underwriting excellence and disciplined risk selection to ensure sustainable profitability after years of volatility. This means aligning underwriting with better data and enforcing pricing adequacy over volume. It requires a forward-looking discipline that prices in the cascading effects of modern perils. For instance, Swiss Re’s strategy emphasizes being “performance-driven, bottom-line focused.”
• “Value Added Services” for cedants: Reinsurers are increasingly focusing on client-centricity – going beyond transactional risk transfer to offer solutions and services that add value for cedants. This involves leveraging reinsurers’ data and expertise to help clients manage risks such as portfolio optimization and assessing exposure to climate risks.
• Operational Agility & New products: The ability to rapidly ingest new data streams, model novel products, and execute complex claims efficiently is now the baseline requirement for maintaining a competitive advantage. In an environment where reinsurance pricing is on the rise, parametric reinsurance for events such as severe convective storms (SCS) of a certain intensity may be an alternative for cedants for pre-determined payout. Similarly, Munich Re’s aiSure™ provides performance warranties and indemnifies clients of providers for their financial losses or legal liabilities directly related to AI errors.

Chapter 3: The Engine of Transformation (“Data & AI”)

While the strategic pillars define what must be done, data and AI determine how it will happen. They should no longer be treated as isolated IT efforts or experimental pilots; they are the core engine of the modern reinsurance enterprise.

To execute dynamic portfolio optimization and maintain underwriting discipline, reinsurers must transition from fragmented, siloed systems to an intelligent, interconnected ecosystem. Advanced predictive analytics and generative AI offer the unprecedented capability to synthesize vast amounts of structured and unstructured data—from dense legal contracts, submission in-take, and geospatial data to risk models - turning ambiguity into actionable, quantifiable foresight.

Chapter 4: “The Way Forward”

The underpinning fabric to realize the above priorities lies with data, analytics, and AI. Hence, leading reinsurers need to refresh their strategy to deliver a trusted, intelligent, and perpetually adaptable data and AI ecosystem (“cycle management”) for the enterprise. This involves building foundational capabilities rooted in principles such as domain-driven design, data product thinking, privacy by design, decision-grade data, and explainability to build trust.

• Decision-Grade Data (beyond data governance): Stop treating data governance as a compliance exercise or a cost center. The goal is to ensure that every underwriting and capital allocation decision is based on trusted, transparent, and auditable information.
• The Enterprise Context Fabric (The Digital Twin): To scale AI in an enterprise, frontier models today lack the capability to understand the business context. Hence, most AI implementations to date were limited to efficiency plays (such as contact center, Q&A, summarization) and hence have not delivered major business value in proportion to their investment. This is where ECF comes to play, a flexible semantic layer that serves as a glue to unify process and data context, and understand the complex relationships between policies, clients, risks, and capital. This "Digital Twin of the Business” enables the sophisticated, cross-portfolio analysis required to spot hidden risk accumulations.
• Agentic AI: Key processes such as underwriting, claims, and risk assessment need to be reimagined in entirety (with human-in-the-loop) using a systems-thinking approach to realize the value. For example: How might we augment an underwriter with a team of AI agents (i.e., multi-agents) that can instantly analyze a submission, research the client's risk profile, model the impact on the portfolio, and draft a set of recommended terms - for the underwriter’s review and decision making.

Final Chapter: What would you build first?

The time for isolated, disjointed pilots is over. If you were to start tomorrow, the critical step is not to build another predictive model or deploy a Q&A chatbot, but to establish an enterprise context fabric.

Why? Because without a unified understanding of your business, any AI initiative will remain a siloed, tactical solution. By first creating this semantic layer, you build the foundation to reimagine core processes like underwriting and claims from the ground up, transforming them from linear, manual workflows into dynamic, AI-augmented decision engines.

This is how you do not just adapt to the future of risk - you build it.

The insurance industry thought it understood hurricane risk in Florida. Then, in 1992, Hurricane Andrew made landfall and revealed how much the industry could not see. Twenty years later, the Insurance Information Institute reported in a white paper that Hurricane Andrew had produced $15.5 billion in claim payouts—more than triple the industry forecast—and left seven domestic insurers and one foreign insurer insolvent, while other carriers required parent-company support to pay claims.

Andrew was not just a catastrophe. It was also a knowledge failure. No one was blasé about hurricanes. No one believed coastal risk was imaginary. Yet the industry had written, priced and accumulated risk across a fast-growing market without the systems, models and institutional knowledge infrastructure it needed to see the full picture. What followed Andrew was one of the most important learning moments in modern insurance history. Andrew forced the industry to build better ways of seeing catastrophe risk. It turned loss history, exposure data, reinsurance strategy and modeling assumptions into a more disciplined knowledge infrastructure. For a time, the lesson seemed unmistakable: Memory needed to be part of the operating system.

Then came Hurricane Katrina less than 15 years later. Katrina was not a repeat of Andrew, but it tested many of the same institutional muscles: catastrophe preparedness, claims capacity, capital adequacy and the industry’s willingness to apply lessons from the past. Yet within a decade, underwriters were already warning that those memories were fading, creating the risk of “unintended complacency” about future catastrophic events. This is how insurance forgets: Hard-won knowledge becomes new practice, then old practice, then somebody else’s problem.

The Memory Paradox

Insurance may be the oldest continuously practiced risk management profession on earth. Lloyd’s traces its beginnings to a coffeehouse in 1688. The Philadelphia Contributionship, which Benjamin Franklin and his fellow firefighters founded in 1752, describes itself as the nation’s oldest successful property insurance company.

And yet, for all that longevity, all too often the industry treats history as just a marketing and communication asset or, worse, merely decorative—for example, history may only appear in the form of portraits of long-forgotten industry captains adorning boardrooms. But while a strong heritage can and should be a brand and cultural asset, that’s just the tip of the iceberg when it comes to how the industry’s deep and well-documented history can serve today’s leaders. Properly structured, preserved and activated, insurers’ archives are a key source of memory that can improve judgment.

Insurance has an underwriting cycle. It also has a memory cycle. The Insurance Information Institute describes the property and casualty cycle as moving between soft markets, when premium rates are stable or falling and insurance is readily available, and hard markets, when rates rise and coverage becomes harder to find. Every CEO in the room knows the pattern. The question is why the industry keeps mistaking the next turn of the cycle for something new.

Part of the answer is that lessons learned in hard markets often lose force in soft ones. Discipline that feels obvious after a major loss can begin to look overly cautious when capital is abundant, competition intensifies and growth targets reassert themselves. What began as institutional learning slowly becomes institutional folklore: respected in theory but easy to dismiss in practice.

The industry doesn’t just forget but also does so predictably, cyclically and at great cost. Unlike many sectors, insurance has the receipts—documented in archives, actuarial records, claims files, underwriting manuals, catastrophe reviews and loss histories going back centuries. Often, the information it needs to challenge the cycle already exists. It just is not always accessible, connected or used.

The Cost of Forgotten History

The insurance profession is facing not merely a talent shortage but also a memory crisis. Insurance Thought Leadership recently reported, citing Bureau of Labor Statistics data, that by the end of this year, an estimated 400,000 insurance professionals will have retired in the U.S. since the beginning of 2021. Datos Insights puts the stakes even more bluntly, estimating that by 2036, that lost expertise could cost the industry up to $124 billion annually.

The danger is not only that there will be fewer people in the roles. It is also that the people leaving often carry tacit knowledge that has not been written down, indexed, connected to training or made usable to the next generation. Underwriting judgment, claims instincts, market memory, broker relationships, regulatory context, catastrophe assumptions, product lessons, pricing scars and more: These do not live only in systems. They live in patterns of experience.

The cost of forgetting is not theoretical. Consider asbestos: Milliman notes that asbestos and pollution continue to affect general liability books written decades ago. As of year-end 2019, the U.S. insurance industry recognized approximately $92 billion in asbestos liabilities. That is the brutal nature of legacy losses. Liabilities written in one era have financial repercussions in another.

The broader pattern should sound uncomfortably familiar: long-tail casualty exposures, long-term care pricing assumptions, catastrophe accumulation blind spots, social inflation and emerging risks that look manageable until they do not. Insurance is very good at modeling uncertainty. It is less consistently good at remembering how former certainties have aged.

AI Needs More Than Data. It Needs Memory.

A handful of carriers have begun connecting historical loss data directly to current pricing assumptions using AI. Berkshire Hathaway Homestate’s wildfire underwriting model, trained on 20 years of loss history, is one documented example. But external catastrophe data is not the same as institutional memory. The deeper archives—how your organization priced that risk, what your underwriters believed, what your claims teams learned and what your leadership decided—remain largely untapped.

AI can be a powerful level setter for lost institutional memory. It can help surface what new employees don’t know they don’t know. It can make archives searchable. It can connect decisions, losses, claims, correspondence, research, product history and oral histories in ways that were impractical even a few years ago. Used effectively, it can codify and reduce the risk of losing knowledge such as how to access relevant information and use archaic software or processes from decades past.

But AI is not the solution by itself. It learns from data. If the most valuable institutional knowledge is scattered across shared drives, paper files, retired employees’ memories, uncatalogued archives and disconnected systems, AI will not magically convert it into wisdom. It will produce generic intelligence, not competitive advantage. I made a related argument recently in Fortune: AI may transform how organizations operate, but it still needs records of how they have made decisions, navigated crises and earned trust over time. The age of AI requires infrastructure that preserves and activates institutional memory, including archives, internal documentation, oral histories and digital knowledge systems.

For insurance, that means archives are no longer simply a heritage function. They are also data infrastructure, training data and risk intelligence. They are the corporate DNA that can help AI understand not just what a company knows but also how it gained that knowledge.

From Archives to Operating System

So what should carriers do? Inventory the records that explain consequential decisions: underwriting guidelines, claims reviews, catastrophe response reports, product launch postmortems, board materials, market-entry analyses, regulatory correspondence, internal publications and more. Capture oral histories with retiring leaders and technical experts, especially those whose expertise is so fundamental it has become invisible. Then connect those materials to the systems where work actually takes place.

And finally, start treating institutional memory as an enterprise asset, not just a marketing and communications one. Corporate history and archival line items generally find a home in marketing and communications budgets because these activities are viewed as supporting brand storytelling. But these budgets rarely have the resources necessary to support business transformation projects that require year-over-year commitment.

More than 15 years ago, the CMO of a Fortune 100 insurance company said to me with no sense of irony, “We’re in a period of transformation: We’re trying to make the transition to the 21st century from the 19th.” The comment has stuck with me after all of these years, because I’ve seen firsthand how slow the industry can be to change.

AI raises the stakes, because knowledge infrastructure is now competitive infrastructure.

Humans forget. Risk management is supposed to make sure we remember. That’s why historical archives and institutional memory are not a soft asset in insurance—they’re part of the operating system. Lloyd’s has records dating back to at least 1734. What are you doing with yours?

Sometimes an industry paper comes along that simply confirms what many of us have known for years but were never willing to say loudly enough in the boardroom.

This is one of those moments.

Claims professionals, executives, reinsurers, brokers, regulators, and every person worried about the future of this industry — we need to talk.

Chantal Roberts recently released a white paper based on claim audits I did titled The ROI of Claims Staffing and Education. The message is neither subtle nor comfortable. We did not write another polite industry memo suggesting “operational improvements.” We held up a mirror to the insurance industry and forced leadership to confront a reality many organizations have spent years rationalizing away:

• Claims staffing is not an expense line item.
• Claims staff, and their ability to resolve claims objectively and efficiently, ARE the product.
• To quote Chantal, “The claims department is the only place where the product is produced.”

To quote another colleague, Heather Blevins: Claims “is the operational engine, the reputational foundation, and the financial heartbeat of every insurance organization.” 

Every premium dollar collected eventually encounters a moment of truth called a claim. That moment determines whether the insurer fulfills its promise or merely markets one.

And the data is becoming impossible to ignore.

“Understaffing claims departments does not save money. It merely delays the invoice until the consequences become exponentially more expensive. That invoice eventually arrives in the form of reserve instability, increased litigation, deteriorating customer retention, rising indemnity payments, regulatory scrutiny, employee burnout, and adverse development that keeps CFOs awake at night staring at reserve triangles,” as also recently stated by Ms. Blevins.

The industry has spent years treating claims departments as adjustable overhead rather than revenue protection systems. When financial pressure appears, staffing reductions often become the fastest route to improving short-term optics. But claims handling is not a static administrative process. It is a dynamic, human-driven environment requiring judgment, communication, investigation, negotiation, legal awareness, emotional intelligence, and technical expertise — often simultaneously.

Yet, increasingly, adjusters are expected to perform those functions while carrying caseloads that challenge the laws of physics.

And let us stop pretending this has no operational consequence.

When adjusters are assigned impossible workloads, the damage does not merely appear on spreadsheets. It appears psychologically, operationally, culturally, and financially. Every file represents a person in crisis. Every delay creates frustration. Every unanswered communication erodes trust. Every rushed investigation increases the probability of error. Every exhausted adjuster eventually reaches cognitive overload.

That is where the real cost begins.

Our research examined carriers, public entities, risk pools, TPAs, and reinsurers. The findings were remarkably consistent regardless of organization size or structure. When adjusters have manageable workloads, meaningful training, appropriate authority, and genuine supervision, measurable outcomes improve across virtually every operational metric.

Litigation frequency declines.

Cycle times improve.

Reserve accuracy stabilizes.

Loss costs decrease.

Customer satisfaction improves.

Employee retention strengthens.

Financial predictability increases.

Conversely, when organizations understaff claims operations, the opposite occurs with almost mathematical certainty.

This should not surprise anyone. Claims professionals directly influence claim severity. A properly trained and adequately supported adjuster can identify fraud earlier, de-escalate emotional disputes, recognize settlement opportunities before litigation expenses explode, manage defense counsel more effectively, and communicate clearly enough to preserve trust even during difficult claim outcomes.

An overwhelmed adjuster often cannot.

That distinction matters because claims leakage rarely announces itself dramatically. It accumulates silently through delayed investigations, unnecessary attorney involvement, poor documentation, inconsistent reserving, prolonged cycle times, avoidable bad faith allegations, and deteriorating customer relationships. Eventually those operational failures migrate into financial instability.

Then the industry acts surprised when combined ratios worsen.

But perhaps the most dangerous consequence of chronic understaffing is what it does to the people themselves.

The insurance industry frequently discusses “talent acquisition” and “talent retention” as though these are mysterious external forces. They are not. Young professionals are not avoiding claims careers because they lack work ethic. Many are avoiding the profession because they see exhausted adjusters carrying crushing workloads with insufficient mentorship, limited support, and little organizational empathy.

People do not stay where survival becomes the daily objective.

Experienced adjusters — the institutional backbone of every successful claims organization — are retiring or leaving faster than they are being replaced. The resulting expertise gap places even greater pressure on those who remain. Supervisors become overloaded. Training compresses. File quality deteriorates. Escalations increase. Litigation follows.

And this creates another issue the industry has not fully confronted: the dangerous belief that technology alone will solve the problem.

Artificial intelligence, predictive analytics, automation, and workflow systems absolutely have value. They can improve efficiency, identify patterns, accelerate administrative processes, and enhance data analysis. But they cannot replace seasoned judgment. They cannot calm an angry insured. They cannot read emotional nuance during a recorded statement. They cannot evaluate witness credibility in the field. They cannot instinctively recognize when a claim is about to spiral into nuclear litigation exposure.

Technology amplifies expertise; it does not replace it.

An inexperienced adjuster armed with sophisticated software is still inexperienced.

In fact, one of the more troubling developments in the industry is the assumption that automation justifies reducing staffing even further. That is precisely backwards. Technology works best when paired with trained professionals who understand both the limitations and the implications of the information being generated.

Claims handling remains fundamentally a human enterprise.

And none of this discussion should be interpreted as criticism of claims professionals themselves. Quite the opposite. Many claims departments continue functioning at all only because experienced adjusters perform daily organizational heroics under increasingly impossible conditions. They absorb emotional trauma, catastrophe response obligations, public hostility, regulatory pressure, and unrealistic productivity demands while still attempting to protect both insureds and their organizations.

That model is not sustainable.

At some point, the industry must decide whether it truly believes claims handling matters. Because if claims truly represents the product insurers sell — and it does — then starving the claims function while investing elsewhere becomes strategically irrational.

No airline would intentionally understaff pilots.

No hospital would intentionally overload emergency physicians beyond safety thresholds.

No law firm would intentionally assign trial attorneys hundreds of active files without support and still expect excellence.

Yet the insurance industry has normalized precisely this behavior inside claims operations for years.

Then we wonder why social inflation worsens.

We wonder why bad faith allegations increase.

We wonder why customer satisfaction deteriorates.

We wonder why severity trends continue climbing.

The answer is sitting directly in front of us.

Understaffed claims operations create operational instability that eventually becomes financial instability.

The message to executive leadership and boards of directors is therefore remarkably simple.

If you want stable reserves, predictable earnings, stronger retention, reduced litigation exposure, and improved operational performance, stop starving your claims department.

Invest in people.

Invest in training.

Invest in supervision.

Invest in manageable workloads.

Because every dollar “saved” through inadequate staffing eventually returns as multiplied loss cost.

The organizations that understand this first will likely become the industry leaders of the next decade. Those that continue treating claims staffing as expendable overhead may eventually discover that understaffing is not cost containment at all.

It is delayed financial detonation.

And it is about time we said so out loud. Because at the end of the day, most never interact with anyone other than the claims representative. What the consumer remembers from experience is every insurer's legacy.

No matter which side of the argument you land on over AI job creation or destruction, an AI image crisis looms. Phrases like, "AI job apocalypse" say it all. Growing negative sentiments about data centers have found their way into political campaigns with concerted efforts to halt or divert construction. To the surprise of many, the mere raising of the AI topic drew jeers by young graduates at several recent commencement ceremonies. According to Pew research, just 10% of Americans say they are more excited than concerned about AI, down from 37% when first asked in 2021. 

Of late, however, the tenor of the AI job destruction conversation is softening to creation of capacity. In other words, using new capacity for people to do other work instead of merely cutting jobs. 

Capacity Creation

Capacity creation happens when AI, especially agentic AI, unlocks productivity by performing all sorts of tasks around the clock with no days-off.  More than just AI productivity gains, repurposing people work so they can do more. For instance, both underwriting and claim handling include large portions of routine, manual work. Gathering, validating, summarizing and sharing information for decision making are prime areas for AI. Once AI does all of this heavy lifting, employees will be freed to shift to new and higher-grade work – at least in concept.

Instead of mass layoffs, companies must contemplate what to do with new capacity by redirecting employees to focus on more meaningful tasks. “More meaningful,” higher-value work is loosely defined, but, either way, the precept of shifting resources to higher importance is well-suited to fit the P&C insurance industry, which runs on people and prides itself on doing business through people and relationships.

Aside from the constant chatter about huge AI productivity gains reducing insurance workforces, reality shows little evidence of overall job loss so far. However, even with the emerging mindset to repurpose work, there is expected to be considerable job disruption. This is important to distinguish from net job losses considering negative AI sentiment comes from real people, whether based on perception or reality. Job disruption should not be taken lightly even if the net amounts remain modest. It is also worth contrasting industries because some job types outside of insurance, such as coding, factory work, taxi driving and administrative tasks, are already being hit.

The insurance industry also takes great pride in resilience which has proven helpful in attracting and retaining talent offering “job security” in good times and bad.  At the same time carriers are eager to automate a wide-range of manual tasks while already outsourcing others. So, what should the insurance industry do with all of this expected future capacity?

Where to Deploy New Capacity

Nearly all functions of insurance could make a case for greater resources – essentially having more hours in a day. Some of the sentiments expressed include:

• CEO’s are certainly eyeing how to reduce both expense and loss ratios to boost profitability with AI, trying to gain first-mover advantages to take market share and outpace competitors across the value chain
• Stakeholders are considering how fraud may be reduced and better contained
• Insurance insiders are enthusiastic about avoiding or mitigating losses to accelerate Predict & Prevent initiatives
• Customers are wondering how AI efficiencies translate to lowering the cost of insurance

Here are some of the top contenders for more people resources:

Customer Service

True customer service has become a rare commodity despite digital self-service adoption and better communication tools. Because of inherent insurance complexities, customers still demand human touch and often have more conversational needs. Whether point-of-sale, renewal, billing or claims, there are elements of consumer distrust and lacking confidence to make the right decisions without talking with an expert. Shortcomings in service often revolve around communication breakdowns and difficulty in reaching the right person. Meanwhile digital tools and work habits have distanced human interaction. Customers vent about repeating the same information and navigating the onerous insurance process and just want help.  Improved customer service and touch would be a top contender for any new capacity. 

Lower Expenses

For every dollar of premium, about 25 cents is spent on expenses. While this amount is generally accepted in today’s environment, new capacity to absorb growth-related work, gap filling for the retiring insurance workforce and enhanced management of expenses are prime areas for focus. AI can also play a direct role to advance underwriting and claim automation and vendor management and, in more specific ways, such as litigation expense control. Simply having deeper insights to control and better manage expense is also on top of this new capacity list.

Loss Avoidance and Mitigation

A Predict & Prevent mantra has gained in popularity with the advent of sensor technology and obvious demand for resilience from evolving climate exposures. Loss control has long served the upper insurance markets well, where resources, experts and actions invested can support effective ROI expectations. Such efforts have made some inroads in personal lines through telematics, water and fire detection. Yet, adoption remains a struggle, as does customer engagement. Similarly, loss mitigation efforts are inconsistent and limited, with some bright spots during CAT events to emulate and expand. However, prevent and mitigating losses is widely underserved and screaming for more attention and resources.

New Insurance Products/Services

The core principle of insurance, commercial risk transfer, has been heavily tested over the last decade. Catastrophes, soaring premiums, restrictive policy language and higher deductibles are reshaping the degree of risk transfer; policyholders are absorbing more risk, particularly in homeowner lines. New requirements such as fire prevention, resilient roofs and new construction standards increase these burdens. In several scenarios, such costly measures are required just to be insurable. An older roof can be uninsurable altogether and most definitely will be on a predetermined actual cash value (ACV) schedule, paired with a huge wind/hail deductible. Translation, the homeowner bears all or most of the risk, which begs for new solutions.

New insurance and financial solutions must be in the forefront to address homeowners' resiliency and prevention investments. The healthcare industry addressed high deductible and out-of-pocket issues through Health Spending Accounts (HSA). Perhaps some sort of home spending account would be similarly beneficial. Because exploring and developing new products require time and resources, these also make the list for new capacity. 

Another way to prepare for capacity shift is to look at underserved areas in which there currently are not enough resources. Although insurers work hard on these areas, most are far from optimized. Interestingly, most are highly important. Here’s a partial list:

• Training and Development
• Upskilling for AI with attendant Change Management
• Auditing and Quality Control
• Legal and Regulatory Compliance
• Vendor Management
• Subro/Salvage Recovery
• Fraud investigations and deterrence
• Working with Communities on Resiliency
• IT Project backlogs
• System Integration waiting list

There are numerous and exciting possibilities for deploying new capacity, but it will take some significant alignment and rethinking. Visionaries see a future of abundance, with some extreme views that depict little to no time spent working and living lives of fulfillment in other ways. Such majestic predictions only fuel AI skepticism and outright rejection of what feels like turning society completely upside down. It is daunting enough for businesses to get started with AI and even more ambitious to prepare for capacity redeployment. 

At present state, there has been marginal readiness to retool roles, and perhaps timing is premature. Consider how claim adjusters and underwriters are anticipated to operate in the future when all or most of the administrative portions are solved, with AI accounting for 70% or greater of the work. It’s a stretch to suggest claim adjusters and underwriters will readily concentrate on “approving” AI decisions and naturally spend much more time interacting with customers and agents without significant change management. 

Any plans to deploy AI in ways that preserve human jobs by reallocating work must apply equal effort to thoughtfully address the many people and structural barriers. The scope is wide and will include new requirements around; hiring/selection, differing skill needs, role redefinition, rewards/incentives alignment, workload expectations, workflow and process reengineering, to cite just a few. As the use of AI expands and solves problems, there will be unintended byproducts that are likely to be as difficult if not harder to solve.

The good news is the continuing discussion to shift future people capacity upward – inspiring for all stakeholders, especially employees (and not to mention the whole value chain and economy built around them). 

Time will tell if this budding attitude sustains or is simply more AI washing. 

Political risks and violence have climbed to number seven in the annual Allianz Risk Barometer 2026, its highest position ever, highlighting the fact that such perils have joined mainstream business risks in a world of tumult. According to the new Political violence and civil unrest trends 2026 report, war has overtaken civil unrest as the political violence exposure companies fear most (53% of all respondents globally), as conflicts in Europe and the Middle East disrupt global trade flows, strain political alliances, embolden adversarial powers and heighten risks to business assets. Civil unrest ranks at number two globally (49%), terrorism/sabotage is at number three (46%).

The U.S./Iran conflict is currently dominating news cycles, having disrupted the global economy significantly. Businesses affected by armed conflict face significant challenges, including supply chain disruptions, loss of market access, and the risk of cyber-attacks and sabotage, the report says. Even before the Iran war, it is estimated that business assets had experienced a 20%+ increase in exposure to conflict in the last five years. For the insurance industry, and especially the political violence and terrorism (PVT) business, the war in the Middle East may lead to significant losses in some areas and new risk assessments for selected key industries and regions. Based on current estimates, the financial loss quantum has the potential to result in a costlier event than PVT claims resulting from the war in Ukraine.

Civil unrest and sabotage remain significant concerns for companies

Allianz Research has tracked around 250 reported strikes, riots, and civil commotion (SRCC) events over the last five years with active participation exceeding 1,000 people and lasting for more than one day. Pakistan experienced the most SRCC events with 11, followed by Indonesia. Other countries that experienced a high number of events include the U.S., Greece, Tunisia, Hungary, Iran, and India. Economic pressures, including cost-of-living issues, are fueling protests and strikes worldwide, with citizens demanding better governance and economic reforms. Most public protests around the world are peaceful, but significant insured losses occurred as a result of major unrest events in 2025. The Indonesian riots in August incurred over $50 million in insured losses, while Nepal’s September protests could see insured losses higher than those caused by the catastrophic earthquake of 2015, which were more than $200 million. Depending on the duration of the conflict in the Middle East, a heightened risk of SRCC activity is also to be expected, particularly in countries heavily reliant on Middle Eastern oil and gas or fertilizers.

At the same time, acts of sabotage, including state-sponsored ones, have increased sharply in the last 18 months. On the global stage, the last four years have seen a surge in targeted and malicious attacks on critical infrastructure, such as undersea cables by advanced persistent threat (APT) actors. These are usually sponsored by organizations or rogue states including Russia, which is very active in this gray area. Such attacks don’t necessarily cause widespread damage, but they can disrupt daily life and business activities, resulting in the allocation of valuable resources to policing and monitoring critical infrastructure.

Adaptation and resilience more important than ever

With geopolitical upheaval, economic pressures, and social media all amplifying the threat of political violence, the potential fallout can lead to substantial economic and insured losses, challenging businesses and their insurers. The pattern of protests and violence in recent years has clearly shown that some industries and occupancies are much more vulnerable to the full spectrum of political violence perils, but any organization can be affected. One of the most severe PVT risks is the threat of business interruption (BI), which could lead to substantial economic and insured losses, challenging businesses, and their insurers. Adaptation and building resilience are therefore crucial for businesses of all sizes.

The U.S. / Iran conflict is likely to have a significant impact on risk mitigation moving forward. According to the Allianz Risk Barometer, prior to the conflict just over a third (35%) of companies were already exploring nearshoring and evaluating domestic manufacturing options, 32% were looking to improve inventory management, including storing inventory in free trade zones, and almost half (49%) were looking at renegotiating and diversifying supply chains, as strategies to adapt to shifting geopolitical risks. Such trends will likely be accelerated by the conflict.

As we navigate this era of heightened uncertainty, understanding the implications of these risks and mitigating them in our interconnected business ecosystems has never been more critical. Insurance has a key role to play in this regard, and demand for political violence insurance continues to grow. We see an elevated level of interest and more buyers than ever in this space. Clients are broadening their coverage to better fit their risk footprint. This is a big shift from the market and buyer behavior we saw before the war in Ukraine, and which is now amplified by subsequent events.

To read the full report, please visit Political violence and civil unrest trends 2026.

Insurance organizations are spending billions modernizing systems without fixing the operating model underneath them. 

For years, the industry has treated modernization as a technology initiative—replace the policy system, upgrade claims, improve workflow automation. But despite massive investment, most insurers still operate through fragmented architectures stitched together across policy, billing, claims, reinsurance, and finance. The result is inefficiency and operational drag embedded into the economics of the business. 

This is why so many organizations still rely on spreadsheets, manual reconciliation, delayed reporting, and disconnected financial visibility despite years of digital transformation. The issue is not that insurers lack technology. The issue is that most insurance operations were never designed to function as a unified operational system. And nowhere is that more visible than in reinsurance.

A recent field study, commissioned by INTX and grounded in independent research conducted by RSM, surveyed more than 250 property and casualty insurance professionals. The findings point to an industry operating under structural strain—where inefficiency is not episodic but systemic.

The financial impact of these challenges is significant and continues to grow. Across the industry, insurers are investing millions in implementing and maintaining multiple core systems, while also absorbing continuing costs tied to support, downtime, and manual work. These expenses extend well beyond initial implementation and compound over time, creating sustained multimillion-dollar pressure on operating budgets. 

As these costs scale across systems and business units, they limit the ability to invest in innovation, slow responsiveness to market demands, and weaken overall business performance. These cost pressures are reflected in how insurers actually operate on a day-to-day basis. 72% of respondents reported using Excel or homegrown tools to manage critical workflows. Further, most organizations operate multiple core systems at once, supported by spreadsheets and manual processes. This fragmented environment creates complexity, reduces visibility, and slows execution across the business.

The study identified four persistent pain points that continue to shape performance across the industry. These are symptoms of a broader issue: operating model debt.

Cost Distortion

Implementation remains a major barrier to modernization. Organizations report spending an average of up to $1 million to deploy a single system. With most insurers operating two to three systems on average, total implementation costs can reach $3 million or more.

These costs are driven in part by reliance on third-party system integrators. More than half of system users depend on integrators for training, and 40% rely on them for project management and implementation. This dependency introduces additional expense and complexity. Core systems often require external support to deliver functionality that should be standard.

Every dollar spent on implementation limits the ability to invest in innovation. As costs rise, organizations navigate these difficult trade-offs that affect their long-term growth and sustainability.

Time Distortion

Legacy systems limit the ability to adapt. 45% of organizations report implementation cycles of 18 months or longer. Even targeted initiatives, such as adding a new product line, can take more than six months.

These delays represent missed opportunities. Organizations are unable to respond quickly to market shifts or regulatory changes. Competitiveness declines as faster-moving peers gain ground.

Even after long timelines, outcomes often fall short. In fact, average satisfaction with implementations remains below 71.8%. Many projects fail to deliver expected value, reinforcing frustration and limiting confidence in future investments.

These operational challenges can be reflected in industry performance. Over the past 15 years, U.S. property and casualty insurers have operated at an underwriting loss when measured without investment income. A combined ratio of 102.1% shows that claims and expenses exceed premium revenue. This pattern highlights the structural inefficiencies within core operations.

Visibility Failure

Support costs extend far beyond licensing and maintenance fees. Insurers report spending from $100,000 to nearly $5 million annually on recurring system costs. These expenses are only part of the picture.

Nearly half of organizations report significant additional costs tied to internal IT support. Teams spend valuable time maintaining outdated systems, resolving issues, and supporting users. Organizations report up to 888 hours of lost productivity each year due to system issues, with financial impact reaching as high as $450,000 annually. Delays in resolving tickets disrupt operations and slow critical workflows.

These costs are often hidden, but they have a direct effect on profitability and planning. Over time, they create operational fragility and limit the ability to scale.

Financial Leakage

Manual processes remain deeply embedded in core system workflows. 52% of policy administration tasks require human intervention. Many insurers rely on spreadsheets alongside their core systems, often working across multiple vendors and tools.

This reliance introduces risk and slows operations. Employees must move between systems, reenter data, and reconcile information. Data latency increases, and errors become more likely.

The financial impact is significant. Organizations spend between $475,000 and $1,125,000 each year on manual work. 36% of respondents identify quoting, policy issuance, and claims processing as the areas most affected.

Manual workarounds reduce efficiency and limit scalability. Time and talent are diverted away from strategic priorities. These inefficiencies weaken performance and make it harder to respond to change.

The Missing Layer: Reinsurance Outside the System

Nowhere is this fragmentation more visible, or more consequential, than in reinsurance.

In most organizations, reinsurance is still managed as a downstream process. Risk is written first. Reinsurance is applied later. Recoverables are calculated separately. Financial impact is understood only after multiple systems are reconciled.

This creates a structural disconnect between underwriting, claims, and capital.

The result is delayed recoverables, incomplete exposure visibility, and inefficiencies in capital deployment. What should function as a strategic lever for growth instead operates as an administrative process.

A Shift Toward Modern Systems

Addressing these challenges requires replacing legacy platforms and rethinking how insurance operations are structured. Modern systems are beginning to reflect this shift by improving and unifying individual functions. Policy, claims, billing, reinsurance, and financial reporting operate within a single system, with a shared data model and real-time processing. In this model, reinsurance is embedded at the moment of transaction. Financial impact is visible immediately. Recoverables are tracked continuously, not reconstructed after the fact. New platforms reduce reliance on multiple systems and eliminate the need for extensive third-party integration. By providing direct support and more efficient implementation models, they lower costs and accelerate time to value.

Transparent pricing improves cost predictability and reduces hidden expenses. These improvements help organizations operate with greater stability and confidence.

Automation is central to modern platforms. Advanced workflows streamline quoting, policy issuance, and claims processing. Real-time data validation improves accuracy and removes the need for many manual workarounds. Integrated functionality reduces duplication and improves consistency.

Speed is a defining advantage. Implementation timelines that once extended beyond a year can now be reduced to months. New product lines can be introduced within three to six months, and expansion into new states can occur in days. This agility allows organizations to respond quickly to changing conditions.

Moving Forward

The insurance industry does not have a technology problem alone, but also an operating model problem that has been compounded over decades of system layering and process workarounds. Modernization, therefore, is about eliminating fragmentation. The future winners in insurance will be the organizations with the fewest operational gaps—not the most systems.

Recent headlines around the hiring landscape have been daunting. Amazon, Meta and Oracle announced significant layoffs in recent months, and many other firms appear to be following. The more telling story is what happened next: thousands of capable, experienced people entered the job market at once, and many of them are still looking. The people losing jobs aren't struggling to apply. They're struggling to be seen.

Much of this reflects how far AI has shifted the workplace, raising what an individual can produce while leaving the way that capability gets recognized largely unchanged. And the investment pouring into the space is not new: last year investors put $4.93 billion into HR technology, a 20% year-over-year increase, according to HR Executive. Yet for all that capital, many would argue the industry has only become more complicated. Employers have the tools to hire faster, but something has been lost: connection, individuality, and a clear path for capable candidates to secure meaningful careers.

The candidate experience bears the weight of it: endless application portals, automated rejection emails, AI screening systems, and interviews that feel transactional. People spend hours on the perfect resume and cover letter only to receive an impersonal response, or nothing at all. Meanwhile, employers struggle with their own inefficiency, from overwhelmed hiring teams to high turnover to a flood of applications, and the same persistent question of how to identify the right people.

The hiring paradox

It's worth understanding where the discrepancy lies. Hiring has become faster than ever, so why is finding the right people more difficult?

The instinct in the market has been to add another layer of software to the employer's side of the equation, whether that means more sourcing tools, more screening tools, or more AI-assisted outreach. But the imbalance the funding is trying to solve doesn't sit on the employer side. It sits on the candidate side. Companies have always had infrastructure: applicant tracking systems, recruiters, sourcing teams, agencies, and the entire HR tech stack. The candidate has had a resume and a job board login.

That gap is what makes the current moment different. As AI compresses the cost and time required to do knowledge work, the distance between what a worker can produce and what their resume can communicate has widened sharply. A two-page document submitted to a portal was never a great representation of capability, and it is a worse one now. The result is a market where the people most able to do the work are often the least visible inside the systems built to find them. That is exactly why a wave of skilled professionals can hit the market after a layoff and still go unseen. So while optimizing for efficiency, hiring has lost the very qualities that make recruitment work: trust, timing, and human understanding.

There's a useful parallel in how other industries solved a version of this problem. Professional athletes don't apply for teams; agents place them. Actors don't apply for films; agencies represent them. In finance and law, the senior end of the talent market has run on introductions and trusted intermediaries for decades. Each of these industries reached a point where the value of an individual's work was high enough, and the cost of a bad match was high enough, that a representation layer became standard. In the knowledge economy, however, that infrastructure simply does not exist.

The result is a labor market where qualified candidates disengage from traditional application funnels altogether. Many people are not applying to jobs anymore, not because they aren’t ambitious, but because the process itself feels exhausting, repetitive, and deeply inhuman. They are not motivated enough to tailor resumes, rewrite hundreds of cover letters, or coordinate multiple rounds of screenings for opportunities that may never result in a real conversation.

At the same time, on the employer side, businesses are running on thin margins as more workers leave on a consistent basis. According to a recent report from LinkedIn Talent Solutions, hiring teams are prioritizing quality-of-hire and retention over sheer recruiting volume, indicating a deeper shift in how companies evaluate talent.

The paradox is crucially clear: the actual experience of hiring is more detached than it has ever been. This is where a new kind of recruitment tool must fall into place.

A new kind of AI bridges the gap

The companies that endure will be the ones future-proofing their strategies. Instead of automating tasks, the most promising AI tools are turning toward relationship-building, personalization, and long-term career alignment, away from processing applications at scale and toward intentional connections between employers and candidates.

In many ways, this transformation reflects how hiring has always worked at its best. Historically, the strongest career opportunities have always come through genuine introduction, referrals, and direct conversations. By putting people back into the mix, it creates a much more connected dynamic: technology to surface opportunities and remove administrative friction, people to weigh leadership potential, skill, and personality.

An AI agent that works

The idea of an introductory economy is where HR funding has a significant effect, and it’s an approach being directly accomplished through platforms that run on a simple premise: recruiting cannot run on automation alone, but requires direct introductions that put each candidate into the hiring conversations they deserve. The agent meets candidates on the messaging apps they already use, including WhatsApp and iMessage, helping qualified talent express their goals, find the right opportunities, and connect directly with hiring managers. The goal is making high-context relationships that would otherwise take years to build.

That is the difference this model makes for modern-day recruitment. It advocates for the candidate so they can get careers that actually last. In a market where most tools are designed to serve the employer side, this rebalancing creates a more equitable and ultimately more effective hiring process.

The future as we know it

As AI continues to reshape the workforce, and as more funding is prioritized in this space, recruitment is quickly becoming one of the most urgent challenges of the next decade.

If jobs keep disappearing, how can people access the next roles that matter? These are the questions hiring managers and candidates still cope with every day.

Hiring can no longer afford to be a standardized solution. It is due for change, to get individuals into the worthwhile roles they have worked long and hard for. The companies shaping the future of hiring are the ones putting their money in the right kinds of tools. It is the companies emphasizing a candidate-first model like Clera, where no machine can say where a person lands a job next.

For years, conversations around wealth management and personal risk planning often centered on a traditional household structure, where financial and insurance discussions were directed to one primary decision-maker. That reality has changed.

Women are projected to control an estimated $34 trillion in assets by 2030, according to recent McKinsey research, representing one of the largest wealth shifts the advisory industry has seen in decades. At the same time, women are increasingly leading businesses, managing multigenerational wealth, overseeing family offices and making independent decisions around long-term financial protection.

Despite these changes, many affluent women still say they feel underserved or misunderstood by traditional advisory models.

The challenge isn’t simply about offering more products or creating “women-focused” messaging. Expectations around advice, communication, trust and long-term planning are evolving, and for insurance and risk advisors, this shift carries important implications.

Wealth Conversations Are Becoming More Personal

Today’s high-net-worth households are more complex than ever. Widowhood, entrepreneurship, second marriages, blended families, caregiving responsibilities, independent wealth creation and longer life expectancy are all reshaping how affluent clients think about financial security and risk.

Many women are no longer participating in these conversations as secondaries. They are leading them. That changes how advisors must approach discussions around property protection, liability planning, collections, business exposures, trusts, succession planning and lifestyle continuity.

It also changes how relationships are built.

Research highlighted during PRMA’s Women & Wealth discussions emphasized that many women value advisors who prioritize listening, context, education and long-term partnership over transactional conversations.

In many cases, trust is shaped less by technical expertise alone and more by whether the advisor understands the client’s priorities, family dynamics and long-term concerns.

Where Advisors Sometimes Get It Wrong

Many of the disconnects women describe are not apparent. They are subtle behaviors that unintentionally create distance in the relationship.

There are several recurring themes, including:

• Defaulting attention to the person the advisor has historically worked with
• Making assumptions about financial roles within a household
• Over-explaining without understanding the client’s baseline knowledge
• And jumping to solutions before understanding priorities

These moments may seem minor, but they shape whether clients feel heard and respected.

The broader issue is that women are not a single client type. Recent research introducing multiple behavioral profiles of women investors reinforces that affluent women approach financial decisions differently based on life stage, personality, experience, family structure and personal priorities.

Some clients want detailed education and collaboration. Others expect concise strategic guidance. Some are highly involved in every decision. Others prioritize delegation and efficiency. The strongest advisors recognize the importance of adapting their communication style rather than relying on assumptions.

Insurance Is Increasingly Part of the Broader Wealth Conversation

As wealth becomes more complex, insurance conversations are becoming more integrated into broader financial planning discussions.

For affluent women navigating major life transitions, agents should be prepared to ask questions that extend beyond premiums and policies.

They include:

• How will lifestyle continuity be maintained after a loss?
• Are trusts, LLCs and ownership structures properly aligned?
• Is valuable property documented correctly?
• Are liability protections sufficient as wealth grows?
• Are family members adequately protected across multiple residences, vehicles or recreational assets?

Even in situations involving divorce or estate restructuring, insurance frequently becomes a foundational piece of rebuilding financial independence and protecting future stability. The growing importance of reviewing ownership structures, umbrella liability protection, valuables coverage, trusts and long-term lifestyle considerations during transitional life stages requires technical expertise, but it also requires emotional intelligence.

The Advisors Who Will Win in This Market

Much of this evolving conversation around women and wealth has also been explored by Steph Wagner, National Director of Women & Wealth at Northern Trust and author of Fly! A Woman’s Guide to Financial Freedom and Building a Life You Love, which examines how financial confidence, life transitions and long-term planning intersect for many women today. While written primarily for women, Wagner’s work also offers a valuable perspective for advisors who want to better understand the emotional and practical realities that shape many client conversations.

The growing influence of women in wealth creation, wealth transfer and financial decision-making is one of the defining shifts changing the advisory landscape.

Advisors who invest the time to understand individual priorities, communication preferences and life circumstances will be better equipped to build lasting relationships. Those relying on assumptions about who makes decisions, how trust is built or what clients value risk overlooking an important segment of the market.

Ultimately, this is a conversation about serving women more thoughtfully. The advisors who embrace that mindset will be the ones best positioned for the next generation of wealth.

The models are ready. The infrastructure often isn't. And the gap between the two is where most AI investments go quiet.

Not long ago, I was in a discussion where everyone agreed the AI was working. The model accuracy was where we expected it to be. The pilot results looked promising. Yet nobody wanted to expand the program. 

In insurance, AI rarely fails because the technology doesn't work. It fails because the organization around it isn't ready for what the technology requires.

The issue wasn't the algorithm. It was everything around it.

The Operational Intelligence Gap

The insurance industry has spent the last several years building AI competence at the model level. Carriers have invested in underwriting algorithms, fraud detection engines, claims triage tools, and customer-facing automation. Much of that work is technically sound.

But technical soundness isn't the same as operational readiness. And in insurance, where decisions carry regulatory weight, customer relationships, and actuarial accountability, the operational layer is where value is either realized or lost.

The failure mode I keep seeing isn't model accuracy. Over the years, I've started thinking about this as an operational intelligence gap: the difference between a model that performs well in testing and a business capability that can be trusted, governed, and sustained in production. That gap has four dimensions. In my experience, most organizations focus on only a few of them.

Places Where AI Loses Its Value

Part of that gap is data.

Insurance organizations rarely operate from a single source of truth. Policy systems, claims platforms, CRM environments, external feeds—each evolves at its own pace. AI doesn't break when those systems disagree. That's the problem. It continues to produce answers. Some of them are even convincing.

But data isn't where most projects get stuck. More often, the problem appears later, when AI has to fit into an existing workflow.

A claims adjuster receives a recommendation. An underwriter receives a score.

The model may be right. Yet if the recommendation arrives too late, or in a format nobody actually uses, the value evaporates surprisingly fast.

What This Looks Like in Practice

A few years ago, I led the technology architecture for a communications platform at a major insurance enterprise. The system eventually handled roughly 80 million customer communications annually across digital and traditional channels. The core AI components performed well in testing. The models did what models do.

What nearly derailed the program had nothing to do with the models. It was the integration layer between real-time AI routing decisions and a legacy policy system that updated on a 24-hour batch cycle. The AI was making decisions based on customer state data that was, by definition, always a day old.

Fixing that required infrastructure investment that wasn't in the original scope. What surprised me was how little of that conversation involved the AI itself:

Looking back, I don't remember many conversations about model accuracy. I remember conversations about budgets. Ownership. Which team would take responsibility when something went wrong.

When we addressed those layers, not just the algorithm, the results shifted materially. Complaint volume dropped by 90%. Delivery speed improved by 96%. The AI wasn't different. The operational architecture around it was.

Insurance Has Always Been a Trust Business

Insurance was a trust business long before it became a technology business. Customers trust insurers with their financial security. Regulators expect decisions they can explain and defend. Employees have a different concern altogether: they want technology to support their judgment, not quietly replace it.

Consider a claims environment. An AI model may identify potentially fraudulent claims with impressive accuracy. Yet if investigators cannot understand why a claim was flagged, or if the escalation process is unclear, the organization faces a difficult choice: trust a recommendation it cannot explain or ignore a recommendation it cannot defend. In insurance, that tension often matters more than the model's accuracy score.

Trust Changes Everything

Regulatory expectations are also changing. Across major markets, insurers are facing growing pressure to explain how automated decisions are made, monitored, and governed. Regulators are asking harder questions than they were two years ago. That shift is real, and it's not slowing down.

Some of the most mature programs I've seen weren't built by organizations with the most advanced models. They were built by organizations that invested early in governance.

When I look at AI programs that struggle, the same questions keep coming up.

The first question is, “Can we trace any AI-influenced decision back to the data that produced it, the model version that processed it, and the human who is accountable for it?” If the answer is no, the program has a governance gap that will surface at the worst possible moment.

Another question worth asking is, “Have the people closest to AI outputs been involved in designing how those outputs reach them?” Not briefed after the fact — involved in design. The distance between those two things is usually the distance between adoption and shelf life.

And the third one is, “Is the integration layer between AI systems and core operational data treated seriously, or as a technical detail to be handled later?” In every AI program I've seen fail quietly, the integration layer was a detail. In the ones that scaled, it was a strategic decision.

Beyond the Algorithm

I've stopped being surprised when a technically successful model struggles to create business value. I've stopped being surprised when a solid model fails to move the needle. By now I know where to look, and it's never the algorithm.

Most insurance organizations don’t need more AI pilots. They need the discipline to turn what they’ve already built into something that actually gets used by real teams, in real decisions, under real pressure.

Most of these organizations already have AI. What they're missing isn't access. It's the organizational work that makes AI usable, and that work doesn't show up in a vendor demo.

The auto insurance industry today is facing many new challenges, including elevated repair costs, evolving fraud risk, and policyholders still expecting fast, almost immediate answers when a vehicle claim disrupts their lives.

In fact, the CCC Intelligent Solutions reported that total-loss claim share reached a record, with vehicles seven years or older accounting for more than 72% of total-loss valuations as aging vehicles and rising repair costs continue putting additional pressure on claims operations. Average total repair costs were above $4,730 in 2024, a 3.8% increase year-over-year, with costs rising a further 1.4% during the first half of 2025.

That said, the real problem extends beyond just claims volume or repair inflation. Many teams still rely on fragmented data across multiple sources when verifying basic claim information. And every delay in that process causes additional expenses and friction. This means the claims process can only modernize if adjusters can access verified data early enough to make better decisions before those costs escalate.

Digital Claims Tools Need Stronger Data Beneath Them

Forrester expects U.S. insurance technology budgets to reach $173 billion in 2026. So, it’s safe to assume that carriers have spent millions, if not more, investing in front-end claims technology. FNOL automation to mobile photo uploads, AI-assisted triage, and digital communications have all undoubtedly improved speed and customer access. But, like many other technological advancements, those tools only perform as well as the data that feeds them.

Many bottlenecks appear after intake, when adjusters need to confirm whether a claimant has clear ownership or whether title activity creates settlement risk. A claim can move through digital intake quickly and still stall once a team needs verified vehicle data from these disconnected systems.

In total-loss workflows, a missing lien record can hold up payment, a title discrepancy can force late-stage review, and a VIN inconsistency can trigger additional investigation after the carrier has already invested time in valuation and settlement coordination. Digital claims systems create speed at the front of the process, but it’s verified data that protects that speed through resolution.

Claims Leakage Often Starts With Small Data Failures

It’s very rare that claims leakage happens due to just one dramatic error. It usually builds through repeated friction across thousands of files. For example, a delayed lienholder confirmation adds handling time, and a late title issue creates settlement rework. Each of these issues may look manageable individually, but across the total claims book, those small failures add up to real cost.

Claims leaders already track macro severity drivers such as repair inflation and litigation exposure. Operational leakage deserves the same attention because it sits closer to the daily work of claims teams. It affects cycle time, adjuster capacity, policyholder satisfaction, and payment accuracy.

The cost environment makes those small breakdowns harder to absorb. But it’s better data that gives carriers a direct way to reduce friction inside the claim, rather than only reacting to severity after it shows up in the file.

Total-Loss Claims Need Earlier Verification

Total-loss claims place a heavier burden on data quality because they require coordination across multiple parties. The carrier may need to confirm ownership, communicate with a lienholder, validate title status, process documentation, and resolve payment expectations within a very compressed timeline.

When adjusters can access verified title, lien and ownership information in real time rather than relying on fragmented lookups across disconnected systems, they can identify title issues before any valuation discussions advance. Earlier visibility helps claims teams spend less time handling administrative issues late in the process and more time focused on claim resolution, policyholder communication, and overall exposure management.

That can help improve control over claim outcomes, because adjusters spend less time resolving administrative issues late in the file and more time managing exposure, documentation quality, and policyholder communication.

Stronger Data Also Strengthens Fraud Detection

Fraud risk has also increased the importance of connected claims intelligence. Modern fraud schemes often exploit gaps in vehicle records, ownership data, title activity, and identity verification.

NICB projected a 49% rise in insurance crime involving identity theft by the end of 2025. Its analysis also found that nearly one quarter of identity-theft referrals involved synthetic identity activity. And with insurers in the U.S. losing roughly $300 billion to fraud per year, nearly 25% of the industry’s total value, it’s a costly issue to have.

Auto claims teams need to see these risks earlier in their workflow to stop schemes in their tracks. Title manipulation, VIN inconsistencies, suspicious transfer activity, irregular lien documentation, and undisclosed prior vehicle events can all indicate exposure. When adjusters or SIU teams see those indicators late, it’s the carriers that face higher investigative costs and weaker recovery options.

Connected verification data helps claims organizations identify suspicious patterns before payments even move forward. It also helps SIU teams prioritize the files with the highest risk, rather than forcing adjusters to chase disconnected data across every claim.

Data Security Has Become Part of Claims Performance

Claims data carries high security value because it often combines personally identifiable information, vehicle identifiers, ownership records, payment information, and lienholder details. As claims operations become more digital and increasingly dependent on outside data providers, carriers are placing greater scrutiny on how sensitive information moves across third-party systems and whether those systems meet modern security expectations.

That makes claims operations an attractive target for fraud actors and cybercriminals, and it is also why claims leaders need strong data governance and clearer visibility into the vendors supporting critical claims workflows. Teams need to know who accessed sensitive claim data, how systems use it, and whether third-party workflows protect it with the same discipline expected inside the carrier’s environment. As more carriers rely on outside data partners to support total-loss, fraud, and settlement workflows, security can no longer sit apart from claims performance. For data partners operating in this environment, SOC 2 compliance is not optional. It is the baseline signal that security controls have been independently verified, not just self-reported.

Better Claims Data Improves Adjuster Productivity

Claims organizations also continue to face staffing pressure and heavier file complexity. Experienced adjusters should spend their time evaluating exposure and guiding claim outcomes. Many still spend too much time searching for records, confirming basic facts, and resolving data inconsistencies that technology should surface earlier. Earlier verification can help reduce that burden.

When claims teams can trust core vehicle and ownership data, adjusters can move files with greater confidence. They can reduce manual follow-up, improve documentation quality, and focus attention on claims that require judgment rather than administrative tracking.

This also improves consistency across claims teams. Fragmented workflows create uneven outcomes because different adjusters may use different sources, ask different questions, or catch problems at different points in the file. Connected operational data provides teams with a shared foundation for decision making.

Why the Next Phase of Claims Modernization Should be Operational

Carriers need infrastructure that enhances data integrity across verification-intensive workflows, especially in total-loss processing and settlement coordination. Stronger claims data helps reduce leakage, improve cycle time, strengthen fraud detection, and protect adjuster capacity. Security also needs to sit at the center of that infrastructure. Claims data has become too valuable, too sensitive, and too operationally important for carriers to treat governance as a secondary concern.

The insurance industry has already improved customer-facing claims technology in abundance. Therefore, the next phase of modernization should naturally focus on the quality of the underlying data, especially the verified title, lien, and ownership layer that total-loss and fraud workflows rely on most.