What if your car insurance reflected how you actually drive, not just who you are? That question is no longer hypothetical. In 2024, more than 21 million U.S. policyholders shared telematics data with their insurer, according to IoT Insurance Observatory research. That reflects a 28% compound annual growth rate since 2018. Usage-based insurance (UBI) is no longer a niche; it’s a mainstream strategy reshaping our industry.  

For years, competitive pricing drove adoption. But today, something deeper is at play: Trust and perceived value are fueling the next wave of growth. This isn’t just about saving money; it’s about believing the insurer will use sensitive driving data responsibly and deliver tangible benefits in return. 

Source: IoT Insurance Observatory field customer surveys

According to a recent consumer survey by Arity and the IoT Insurance Observatory - sampling 2,059 personal auto policyholders representative of the U.S. market - 82% of policyholders would recommend a telematics app that rewards safe driving, offers feedback, provides crash assistance, and delivers other valued services. Among drivers under the age of 53, that number exceeds 90%. Positive sentiment toward telematics has steadily increased over the past decade, as shown in the chart above.

Trust isn’t a buzzword here, it’s the foundation of adoption. Consumers share data only when they believe insurers will protect it and use it to create real value. In fact, 53% of respondents expressed high trust in insurers’ handling of personal data, ranking insurers second only to banks. That trust translates into action: willingness to switch plans, share driving scores, and pay for connected services. 

The willingness to adopt UBI is strong: 60% of policyholders are open to switching, rising to 72% among younger drivers. This level is consistent with the evidence from recent TransUnion surveys showing that 60% of people reported being offered telematics opted-in.

When consumers see clear benefits, privacy concerns fade. They want pricing that reflects lifestyle, rewards for safe driving, and features like automatic crash assistance. Three-fourths are open to sharing their driving score for a personalized quote. More than half of those willing to switch prefer pricing models that offer bigger potential savings, even if it means some risk a surcharge. 

This is where telematics shines. Insurers can deliver compelling offers because telematics unlocks incremental economic value by sensing events, transmitting data in real time, and applying AI-driven analytics to understand, decide, and act. This enables smarter underwriting, faster claims processing, and more proactive risk management. By sharing part of this value with policyholders, insurers create a win-win scenario that makes UBI not just viable - but mainstream.

Source: IoT Insurance Observatory

Over the past decade, insurers have proven the power of telematics data to transform core functions: 

• Continuous Underwriting: Telematics data enables more accurate risk assessments and selection, allowing insurers to better match rates to actual risks. This leads to more sophisticated pricing, improved retention and effective acquisition of good risks, and reduced premium leakage from riskier drivers. Insurers can also use telematics-based data to make portfolio-level decisions regarding risk appetite and reinsurance.
• Enhanced Claims Management: Real time crash detection is a game-changer for claims management. Insurers can trigger proactive responses, notify emergency services, and initiate the claims process. Insights about crash events support timely claim handling and help minimize potential fraudulent or inflated requests.
• Connect and Protect: International telematics-based experiences demonstrate effectiveness in mitigating risks by identifying risky situations in real time and intervening before accidents occur.  Behavioral change programs promote safer driving, leading to fewer accidents and lower loss ratios. 

Policyholders are willing to reconsider their insurer when pricing reflects how they actually drive and live: More than half of policyholders would switch for a product with the premium defined by telematics data. 

Consumers aren’t just looking for lower premiums; they want features that matter. Rewards for safe driving and automatic emergency assistance in severe crashes rank among the top preferences across all generations - from Gen Z to Traditionalists. And the appetite for innovation doesn’t stop there: More than half of policyholders would pay $4.99 per month for a connected dashcam service that offers emergency assistance, video recording for protection against unfair complaints, and real-time safety feedback.

The impact goes beyond individual policies. When the usage of telematics data is holistically adopted across the entire insurance organization, this improves pricing accuracy, reduces losses, and makes insurance more affordable, all while promoting safer roads. Fewer accidents mean fewer injuries and lives saved. That’s why telematics is more than a business strategy, it’s a social good.

The time to invest in telematics mastery is now. Insurers that fully embrace the connected paradigm in all their core processes and responsibly use data with customer consent can unlock greater value—delivering fairer pricing, personalized experiences, and safer roads. This broader usage of data enables higher value creation and sharing, benefiting policyholders and society as a whole.

Fraud is hardly a new problem, but it is a serious issue, and recent fundamental changes in societal norms are exacerbating fraudulent conduct and making detection and deterrence less of a priority than warranted. The scope and scale of fraud are truly shocking, especially among government-funded medical and social programs currently under scrutiny, where enormous costs are somehow tolerated.

Fraud not only creates significant economic loss but also undermines confidence in its public and financial institutions, including insurance. Yet preventing and combatting fraud is seemingly episodic and random. 

All of this serves to bring renewed attention to the long-standing concerns about ever-expanding fraud in general – and specifically insurance fraud. Insurers need to heed the wake-up call.

COST OF INSURANCE FRAUD

Quantifying insurance fraud's impact is difficult and spans from premium fraud to claims fraud, whether opportunistic or through deliberate scheme. According to the Coalition Against Insurance Fraud (CAIF), insurance fraud costs American consumers more than $300 billion a year. This amounts to an individual policyholder $900 annual “tax,” as insurer costs are passed on in form of premiums. Claims fraud is said to occur in about 10% of property-casualty insurance losses. Medicare fraud alone is estimated to cost $60 billion every year.

There are also several limitations when it comes to detecting fraud. According to the National Association of Insurance Commissioners (NAIC), there are key differences between “hard” and “soft” types. Soft forms of fraud are widespread and can be a common exaggeration of a legitimate claim. Hard types are described as intentional acts to create or fabricate “damages” and claims. Still, these general headers fall short of telling the whole story. 

Claim fraud can be perpetrated by an individual or involve others including organized crime rings recognizing there are entire ecosystems designed to inflate, embellish and even fake an accident. Billing for unperformed medical procedures pales in comparison to fake “victims” being paid to undergo surgery. A single case in New York uncovered a $31 million scheme between a doctor and lawyer in trip-and-fall “accidents,” paying "victims" to endure surgery, simply to initiate a claim, justify damages or both. So-called runners are paid finder-fees to produce participants.

Further, many frauds go undetected for long periods or are missed altogether because there is much reliance on the “honor system,” whether at point of sale in which premiums are based or when making a claim. Although any healthy system checks and verifies, it is impractical, unnecessary and risky to deeply investigate a large percentages of cases. Insurers balance customer service, state regulatory requirements involving timeliness and potential complaints that can escalate to lawsuits. 

Meanwhile, internal special investigative units (SIUs) likewise have finite resources and bandwidth, only concentrating on the most actionable cases. Law enforcement agencies have similar constraints, and insurance fraud is a lesser priority than other crimes. Altogether, this dilutes the efficacy of combatting fraud, leading to uncaptured and under-reported figures.

Instead, anecdotal case examples tend to do the best job of illustrating the magnitude of fraud. Phony medical clinics, staged auto accidents, even faked deaths demonstrate the amounts at stake and the lengths fraudsters will go. More frustrating is how obvious some of the schemes are, revealed as in the infamous empty day care center stories. 

But what happens when technology pushes the boundaries beyond such traditional fraud methods?

The Yin and Yang of AI and Insurance

The rapid emergence of artificial intelligence has brought greater business risks, and the financial services industries are among the largest victims of related fraud. Ironically, business is quickly learning to harness the power of AI to fight fraud more effectively – but so are the fraudsters. 

The potential of AI in claims fraud detection is among the most powerful applications, and particularly so in life & health and accident, according to a February 2026 report from Gallagher Re and CB insights: "Global InsurTech Report."

AI has many benefits. It can improve efficiency, help make better decisions, and encourage innovation across different industries. But these advantages also come with serious risks – especially the potential for misuse in fraud or deception.

Like any powerful technology, AI can be used for both helpful and harmful purposes. This makes strong and thoughtful governance essential to maximize its benefits and protect against misuse.

Hackers and other criminals can easily commandeer computers operating open-source large language models (LLMs) outside the guardrails and constraints of the major artificial-intelligence platforms, creating security risks and vulnerabilities, researchers said.

Hackers could target the computers running the LLMs and direct them to carry out spam operations, phishing content creation or disinformation campaigns, evading platform security protocols, the researchers said. Roberto Copia, director at IVASS Inspectorate Service, spoke about this issue at the 4th National Congress of the CODICI Association in 2025. He pointed out a growing concern: While AI can improve the efficiency of the insurance industry, it can also give fraudsters more advanced tools to commit fraud.

AI and Insurance: An inseparable alliance

AI is cautiously becoming an indispensable tool in the insurance sector. Its applications range from risk assessment to customer services, claims processing and fraud detection. Predictive algorithms, neural networks, and machine learning models allow the processing of vast datasets, improving underwriting accuracy, accelerating claim settlements and strengthening insurers' anti-fraud capabilities.

But these very tools – powerful, scalable and increasingly accessible – are also being weaponized by fraudsters. “Those who seek to commit fraud are often skilled innovators – frequently one step ahead of those tasked with stopping them,” Copia has said.   

A quantum leap in criminal sophistication

Insurance fraud has always been a structural problem in the sector. Yet today, it’s undergoing a qualitative shift. We’re no longer dealing solely with fraudulent damage to property or fictitious claims. Modern fraud is digital, automated and highly sophisticated. AI has become a powerful enabler for those seeking to manipulate data and images, forge documents or create false digital identities.

A March 2026 report, Verisk State of Insurance Fraud Study, finds, based on surveys of 1,000 U.S. consumers and 300 insurance claims professionals:

• 55% of Gen Z say they would consider editing a claim photo or document
• 98% of insurers say AI editing tools are fueling digital fraud
• Only 32% of insurers feel very confident about detecting deepfakes
• 69% of consumers believe fraud will raise premiums for all policyholders

A paradigmatic example is the Ghost Broker scam: insurance websites that appear legitimate, often employing advanced social engineering techniques, real logos, and data stolen from unwitting intermediaries. AI allows these fraudulent portals to appear increasingly credible, complete with chatbots simulating customer service, AI-driven profiling of potential victims, and the delivery of highly personalized fake offers. The result is a seemingly flawless customer journey. But the buyer is left uninsured and unknowingly defrauded until subsequent inspection reveals the deception.

Another example involves "synthetic" identity fraud, in which fraudsters create an identity with a mix of fabricated credentials. According to Lexis Nexis, fraudsters may create synthetic identities using potentially valid Social Security Numbers (SSNs), with accompanying false personally identifiable information (PII). This newer challenge raises the bar for insurers to validate identity at point of sale and other policy lifecycle stages.

THE FRAUD FIGHTING IMPERATIVE

We believe that insurers have an obligation to prioritize fraud detection and avoidance in this growing, too-big-to-ignore dynamic. This obligation is moral, economic and legal. An insurer’s duty to its policyholders includes protecting their investment while managing fair and accurate premiums alike.

Paul Carroll 

With AI transforming underwriting, some say the function is entering a new phase. Do you agree? 

Katie Klutts Wysor 

When you look at what property and casualty carriers are saying and what brokers are starting to say, there’s broad recognition that generative AI could reshape risk assessment and underwriting in meaningful ways. It’s becoming an enabler. It can help underwriters make better decisions and work more effectively. 

Carriers are focusing on underwriting as a function and investing heavily in it. They’re talking about that focus in investor days and earnings calls, and they’re doing significant work internally to organize data and update processes—improving speed, increasing automation, accelerating turnaround times and supporting more informed decisions with better data. 

What’s even more significant than the process improvements is what AI could do more broadly. Think of the underwriter as managing capital and trying to direct it to the ideal place. How can AI help define the parameters around what the carrier wants to write so it can deploy capital where it is targeting stronger returns? From there, you can align appetite and business mix with the underwriting process. 

For example, if I’m a regional workers’ comp player and I want to expand into other lines of business or other states, how can I use AI to support that portfolio management decision and direct capital more effectively? Then, how do I identify the necessary distribution partners to find the business I want? How do I create the proper incentives for distribution partners to bring forward that business, so I have a submission to underwrite? And then, how do I make sure the underwriting process, and decision-making aligns with my appetite? 

I think that’s where a great deal of value could be created from an underwriting perspective, looking at how AI can help inform research on the front end, and how you then use something like a GenAI-enabled underwriting platform to begin systematically embedding strategic capital decisions into appetite, process and guidelines at scale, so underwriters evaluating risks are working from more relevant information. 

Then you can use AI to respond to new business decisions more quickly, respond to renewal decisions more effectively, and potentially take certain actions during a policy’s term to support risk mitigation conversations. 

If you can start mastering that link—how you’re deploying capital and setting appetite, all the way down to those micro process decisions—that represents a new level of maturity. 

Paul Carroll 

Speed has become a significant competitive factor in insurance underwriting. If you’re slow to quote, even with a slightly better price, you may lose the business. What’s happening in terms of speed in the underwriting process, and how do processes need to change—not just the technology—to take advantage of the speed AI can offer? 

Katie Klutts Wysor 

Speed to quote and bind means something very different across varying lines of business. In auto insurance, you need to be able to deliver a quote in seconds. So, you see many personal lines players focusing on quote simplification. In auto, it is close to a mature problem, and many carriers are following established market patterns to stay competitive. 

But the speed question gets harder as you move into more specialized or complex lines in personal insurance and small commercial, middle market and large commercial. 

What we’re seeing there is impressive. Capabilities are now emerging to triage submission intake. From a technology perspective, carriers increasingly can take a submission no matter how it comes in—via email, a platform or another channel—and combine it with what they already know about that risk, along with relevant third-party data. 

At that point, it becomes an execution challenge. How can you more systematically get to a quick yes, no, or maybe on appetite, and then move effectively  toward a quote? 

How fast that can happen depends on the line of business. But that point is right: Carriers should move as quickly as their competitors. If you’re slow, distribution may not be willing to wait. 

And the benchmark will continue to move. 

Paul Carroll 

How far along is the insurance industry in using technology to allocate capital more intelligently, and what needs to happen to reach the next level? 

Katie Klutts Wysor 

It’s less a technology challenge and more a business decision-making challenge. Some players in the market are especially strong at this, and you can see that by looking at underwriting returns over time. Those companies have consistently used technology and data to manage their portfolios and allocate capital with greater precision, and they will likely continue to adopt new approaches as the tech capability improves. The timeline for broader adoption of newer technology, including generative AI, is harder to predict because it comes back to how quickly "the pack" of carriers can  evolve how they manage the profit and loss across the portfolios. 

Paul Carroll 

What is an example of how insurers can improve their capital allocation? 

Katie Klutts Wysor 

The fundamental approach is to look at your underwriting returns against the capital you’re deploying to the business, map that out, compare outcomes, and decide where you want to grow and where you may need to pull back. Improvements in technology may allow carriers to do that analysis more frequently. Instead of doing it once a year as part of strategic planning, you could be looking at a refreshed view every month using more current data. 

Many carriers may be able to move from annual reviews toward monthly or weekly review cycles, depending on how they make decisions. They may also be able to do the analysis in a more automated way and make decisions more intentionally on micro-segments of the business (by geography, class, line, etc.) that would have been too time-consuming to identify and react to previously. 

Maybe a competitor enters the restaurant space aggressively and undercuts on price. You may decide not to follow them down that path because you believe the pricing is unattractive, so you slow growth in restaurants. 

Or take the opposite scenario: A trend affects restaurants and causes the market to become more cautious. You may conclude that the market reaction has gone too far and decide if this is the right time to pursue restaurant business. 

Today's leadership reviews may only look at class code-level data monthly or quarterly, and at frequency and severity trends in a backward-looking way. But if you can automate how, you assess that information at a portfolio level, then you can decide whether to lean in or lean out of a class like restaurants more quickly.  

Paul Carroll 

What about the execution side of this—how do insurers actually act on these faster insights once they’ve identified an opportunity? 

Katie Klutts Wysor 

The second half of the equation is exactly that. Say you’ve been able to automate and generate more timely underwriting data, so you can make portfolio decisions weekly or monthly instead of quarterly or annually. That’s a meaningful shift. The next step is execution. 

Say you decided to lean into restaurants. You want the market to know. You want your agents and distribution partners to know you’re interested in that business, particularly if another carrier has started to pull back or take rates. That’s the business you want to enter the pipeline. 

Then you want to set up your underwriting process so you can pivot quickly. Maybe you were not prioritizing that business to get it to an underwriter’s desk and streamline escalation paths to support faster turnaround. 

Of course, once submissions are flowing in, and the process is in place to evaluate and price the business you want competitively, you also need the proper governance and controls, so you don’t end up writing risks that fall outside appetite. 

The big difference this year versus a year ago is the ability to put agentic AI workflows in place and that support faster transaction-level decisions. Humans are still in the loop, but they are not necessarily slowing down the process in the same way they did when carriers relied more heavily on manual referral and escalation processes to respond to market changes. The next frontier I expect to see in the coming year is using agentic AI workflows to help improve portfolio-level decisions.

Paul Carroll 

Would you talk a bit about some of the process efficiencies from generative AI as underwriters make their decisions? While those efficiencies aren’t as strategic as the portfolio-level decisions you’ve described, they still seem substantial.  

Katie Klutts Wysor 

Underwriters face a series of yes, no, and maybe decisions, and much of the friction sits in the maybes. You can automate obvious yes-or-no decisions. The maybes are the gray area where you bring in a person. 

Over time, we may be able to bring in a person less often because of agentic AI and other decision-support tools, while maintaining appropriate human oversight. 

Some maybes exist simply because a piece of information is missing. A file gets routed to an underwriter to obtain one additional data point. Once that data point is available, a rule can be applied, and the case can become a yes or a no. In many cases, that is increasingly solvable today.  

You should identify those cases in your portfolio, then use AI to obtain the data point and apply the rule. 

There are also maybes that are more judgment-based, where you’ve created a manual review because you want someone to look at it who has seen this kind of case many times before. Maybe they’ve seen a six-figure loss in a similar situation, so you ask, “Would you write this again knowing what you know now?” 

Agentic AI workflows can help by bringing more context to the situation and supporting more informed underwriting judgment.  

Paul Carroll 

Based on what you’re seeing, how much are underwriters working with brokers and clients to provide guidance on risk reduction—essentially telling them, “you’re getting dinged for this, why don’t you fix it to reduce your risk?” 

Katie Klutts Wysor 

Right now, it’s predominantly brokers and distribution partners that are providing that first line of risk management advice. But there’s also a meaningful role for underwriters and carriers. 

The concept is there, the question is how consistently it can be translated into actionable guidance. 

Paul Carroll 

What final advice would you offer to readers? 

Katie Klutts Wysor 

What my clients care about is taking some of the bigger ideas and translating them into what to do right now and how to respond in practical terms. So, what I’d leave readers with is this: Keep thinking about the art of the possible but also focus on what you can implement now to strengthen performance this year, and start bringing those two together. 

Understand what technology, data, and AI capabilities are available to you. But more importantly, identify which ones you can deploy quickly while you continue building toward the more complex architecture and data challenges you should address over time. 

Paul Carroll 

So you can create a cycle: make targeted investments that create near-term efficiencies, then use those gains to support the next wave of investment. 

Katie Klutts Wysor 

Exactly. Don’t spend three years trying to build the perfect solution. There’s a lot you can do right now. Deploy something practical that can create value, then use those gains to support larger investments over time. 

Paul Carroll 

Thanks, Katie. 

Here's a thought experiment for you: What if Elon Musk ran an insurance company?

Just imagine how regulators would react to his brash, visionary ideas wrapped in disdain for tradition and a belief that rules don't apply to him. 

But what if you could bottle the good parts of his iconoclasm and leave out the parts that would scare policyholders about the reliability of their insurance carriers? A former president of Tesla just published a book that might allow for that. It describes the five-part "algorithm" that he and Musk used to manage the company during a transformative stretch in the mid-2010s. 

I don't think insurers should go full force, a la Musk's "hardcore" mode--you could wind up with an embarrassment like DOGE and never recover--but his algorithm does offer a playbook for radical simplicity and for what I think is the right way to approach automation. 

Jon McNeill, author of "The Algorithm: The Hypergrowth Formula That Transformed Tesla, Lululemon, General Motors, and SpaceX," says the method has five steps:

• Question every requirement.
• Delete every possible step in the process.
• Simplify and optimize.
• Accelerate cycle time.
• Automate.

Question Every Requirement

McNeill writes about how Tesla, for instance, questioned China's requirement that it own a piece of any company operating in the country and eventually negotiated a deal that let Tesla own 100% of its Chinese subsidiary. He also writes about deciding that cars didn't need to be assembled out of so many parts, even though they had been since the days of Henry Ford. Instead, Tesla began experimenting with casting bigger and bigger pieces of the car and eventually succeeding, greatly reducing the need for assembly.

For insurers, though, I'm thinking the real benefit would come in more modest ways that track more closely with an anecdote McNeill told in a podcast with the Wall Street Journal. He talked about how much trouble Tesla had designing and manufacturing a part that was supposed to sit between a battery and the chassis. The problem became so important that Musk got personally involved and haunted the factory for weeks. Eventually, Musk and McNeill asked if the part was really necessary, and the battery people told them it had been mandated by the folks responsible for damping noise. When McNeill went to them, he was told that, no, the battery folks had mandated the part to minimize danger in the case of a battery fire. McNeill decided to track down the engineer who had signed the order requiring the part -- and learned he couldn't reach the person because he was a summer intern who no longer was at Tesla.

Insurers already question what they believe to be undue regulation, but I think they could benefit more broadly from asking employees across the business to question everything they're told to do, whether by someone inside the organization or outside it. Even if you just do this as a one- or two-month exercise, I'd bet you'll find you're doing lots of things just because they've always been done that way, not because they deliver any value.

Delete Every Possible Step in the Process

At Tesla, McNeill said in the podcast, he deleted several steps, and Musk asked whether he'd broken the process as a result and received some severe pushback. When McNeill said he hadn't, Musk told him he hadn't gone far enough. He needed to keep pushing until he not only got close to the bone but cut into the bone -- at which point, he should back off and find a sustainable approach.

McNeill said the rule of thumb was to only deliver what the customer directly paid for: the car. Customers didn't pay directly for manuals, for documentation, and so on, so Tesla would spend as little effort as possible in those areas.

Again, I don't think that approach would survive at an insurance company. Cut-until-you-break-something can happen in a manufacturing process, behind the scenes, but it didn't even work at the Department of Government Efficiency (DOGE), which Musk ran in the early days of the second Trump administration. Even with the slash-and-burn ethos of Trump 2.0 a year ago, Musk cut too deeply and caused problems both for those receiving government services and for Trump. 

Still, insurers can suffer from a sort of data and process bloat. Given the industry's abundance of caution, it's easy to ask for more questions, to gather more data, and to require another guardrail in the process. Life insurers have shown that it's possible to do the same with less, given the success of fluidless underwriting, and other lines could surely scale back some requirements, too -- becoming more efficient while making customers happier.

Simplify, Accelerate, and Automate

I'm combining the last three parts of the Tesla algorithm because, at least for insurers, they all fit under one mandate: "Automate last."

McNeill said Tesla learned the value of these three steps when it was having so much trouble manufacturing the Model 3 that it was running out of cash and was in danger of bankruptcy. The company stopped running its highly automated manufacturing line, set up a big tent outside the factory and started making the cars by hand. Once management figured out the best process, it began speeding up. Only once they saw that they could run the process at speed did they start bringing in the machines that would automate it -- scrapping the entire production line that they'd set up before fully understanding what was needed.

"Automate last" fits with the approach the computer industry has taken for decades: Once a manual process is fully mapped out, it can move into software and then, when you're sure you have everything nailed down, you can hard-wire the work by moving it into the silicon. 

That approach makes sense for insurers, too. When you see the possibilities of AI, for instance, you should map out a potential new process, implement it manually, speed it up -- and only then let the machines take over.

There are plenty of things about Musk's approach to business that I wouldn't recommend. For more than a decade now, I've been mocking his annual claims that he'll have millions of Teslas functioning as robotaxis, that he's going to colonize Mars (we won't even land someone on Mars in his lifetime), that he's about to unleash an army of humanoid robots, and so on. Those of us without his massive wealth would lose all credibility overnight if we pushed a similar sort of sci-fi dream. Insurance, as an industry built on trust, can't afford anything close to the wild claims that Musk makes routinely.

But I do think it's worth giving his algorithm serious consideration because it can reduce complexity and lead to effective automation. If nothing else, reading about the bold moves at Tesla might inspire some new thinking and resolve in the insurance industry. 

Cheers,

Paul

P.S. "The Algorithm" reminds me of one of my favorite geek jokes:

Q. How do we know that Al Gore actually invented the internet?

A. It runs on Al-Gore-ithms. 

The insurance claims industry sits at an inflection point. Medical records are more complex, nuclear verdicts are rising, and the workforce is changing faster than most organizations can adapt. AI promises to help — but most implementations have fallen short. We sat down with Mark Tainton, senior vice president of data solutions at Wisedocs, to talk about what's actually working, what isn't, and why the industry needs to move from document management to true decision intelligence.

Paul Carroll

The insurance claims industry has been talking about digital transformation for years. What's actually changed in the last 18 to 24 months, and what's still stuck?

Mark Tainton

Having worked in the insurance industry for over 30 years at the intersection of technology and claims operations, I've certainly seen infrastructure change. But the bigger question now is the operating model that can actually leverage that infrastructure. And the operating model is not so much around storing documents in claims management systems or document management systems—it's about how we take advantage of that data asset. We’re essentially moving from document storage into effective decision-making.

Over the last five years, there has been an acceleration in the technology, in particular with large language models. Technology is not the problem.

It's really about taking advantage of the individual pieces of information in the world of unstructured data. That's the next wave we should be focusing on: How do we operationalize the assets so they’re part of the DNA of insurance processes?

Paul Carroll

Medical record review is at the heart of so many claims decisions, yet it still appears remarkably manual at most organizations.

Mark Tainton

I’ve certainly seen large carriers that have introduced AI but haven't introduced the process changes or changed how people can take advantage of the insights as the claim goes through its lifecycle. Carriers are using ineffective decision making approaches that continue to mirror what we saw 10, 15, 20 years ago. 

There needs to be a conversation around how adjusters work, especially because of the change in their age demographic. New people coming into the claims industry consume data completely differently. We have to adjust. 

You have to also understand the psychosocial aspects of the workforce, where COVID accelerated change. You need to cut across multiple claims at any given time and look for triggers that are prevalent by a treatment provider, or at risk indicators that suggest psychosocial issues—they are top of mind for a lot of claims teams right now.

Paul Carroll

There's always a tension between speed and defensibility in claims, especially given the high stakes. How do insurers resolve that tension?

Mark Tainton

Claims are getting more complex, and we've seen a lot of legislation that makes it very clear that if someone's making a decision solely based off AI output with no human in the loop, that's going to be a problem.

When you tie that concern into the expansion of traditional fraud and increases in nuclear verdicts, the defensibility question becomes critical. There needs to be a human in the loop.

Several states are already drawing that line legislatively. California's SB 574 and a growing number of AI governance frameworks now require that AI-assisted decisions in insurance and legal contexts be documented, auditable, and explainable. That is not a future concern; it is a present operating requirement for carriers doing business in those jurisdictions. The organizations that build defensibility infrastructure now will not be scrambling to retrofit it later.

Paul Carroll

There are a lot of solutions out there these days, but they seem to largely be point solutions—summarization tools, triage tools, document processors, and so forth. What's missing from the point solution approach?

Mark Tainton

First, they don't fit into the ecosystems of clients and large carriers. They don't work alongside platforms like Guidewire where they can function as a module and help make those decisions effective.

The point solutions also aren’t really end-to-end. They're focusing on a point in time on a particular claim. That produces what I call a silent failure. The AI processes the document and returns a summary, and the claim moves forward. But the anomaly that should have triggered a flag, the treatment pattern that does not match the diagnosis, the billing inconsistency that signals a problem: None of that surface because the tool was never designed to look across the lifecycle. The claim does not fail loudly. It just quietly travels in the wrong direction for months. 

Think about first notice of injury as a claim goes through the life cycle, and all of a sudden you get a demand package or a treatment package coming in. What are the decisions you want the adjuster to make?

You need intelligence that cuts across the full lifecycle of the claim in terms of other claims with certain characteristics. And I think that's where point solutions really come up short.

Paul Carroll

I assume that thinking is why you took a platform approach with WiseShare.

Mark Tainton

Very much so. We have the sorting and summarization solution that we just renamed WisePrep. It includes WiseChat, where users can save all the insights they generate from a large language model. We've introduced WiseInsights looking at litigation trends, looking at treatment patterns and how they develop, looking across claims that an adjuster who's got a workload of 200 or 300 claims cannot identify on their own. These insights reveal similar characteristics across claims. For example, we looked at one portfolio and identified that a particular treatment provider, over a 12-week program, consistently prescribed a higher and more severe medication at the four-week timeline. 

WiseShare is important, too.  Far too often, a summarized document gets passed from the adjuster to inside counsel, then to external counsel, and eventually to an IME [independent medical examiner]. A lot of the time, we see slip-ups—documents go missing, misinterpretations occur, different versions of the truth emerge. WiseShare brings everything together into one consolidated environment where all of those entities can actually share, review, and export the claim file. 

From a legal defensibility standpoint, that consolidation is not a convenience; it is a chain-of-custody argument. The defense bar needs to see a complete, unbroken record: the medical record chronology, the time series of decisions made, and documented consistency in how AI processed the underlying materials. When a claim ends up in litigation, the question is not just what decision was made; it is whether that decision can be reconstructed, sourced, and defended at deposition. WiseShare is built for that standard.

You have to be able to wrap intelligence around a decision, and that requires a platform. 

Decision intelligence needs to be comparative. You have to be able to see the claim you're dealing with in the context of other claims. The intelligence also needs to be sequential. Are we seeing similar patterns starting to develop on other claims in certain jurisdictions? Are we starting to see certain seasonal trends? Are we starting to see different types of treatment coming through? Finally, the intelligence must provide accountability. Is every inference sourced and every decision point documented? 

The defense bar needs to see that audit trail. They need to see the medical record chronology, the time series, and the consistency in terms of best practices for how AI actually processes documents and insights for better outcomes. From 2023 to 2024, nuclear verdicts rose 52%. Thermonuclear verdicts are up 81%, and overall verdicts are up 116%. 

You need one single environment where you store the materials, one single process that's consistent across an organization.

Bottom line: if you can't show defensibility, you're in a world of trouble.

Paul Carroll

There's discussion about AI replacing many human workers in the insurance industry. What is your perspective?

Mark Tainton 

There's this notion that AI is going to replace people at the desk. From my perspective, that's totally inaccurate. And I think that mindset sets back adoption.

But here's the inflection point: We're dealing with an aging workforce. Insurers and TPAs are struggling to attract talent. Why? Because some of the tools and technology have not evolved as quickly as in other industries. When you can walk hand in hand with AI and the person at the desk and show them all the benefits, that’s exciting. 

Paul Carroll

If you could change one thing about how the insurance industry is currently approaching AI adoption in claims, what would it be?

Mark Tainton

For me, it's what I call the evolution framework. AI is a journey, not a one-time event. Far too often, what I've seen is large organizations—mid-tier, tier two, tier three—treating this as basically an implementation. It's almost like they're going in, turning the light switch on and walking out.

I spend quite a bit of time working with clients all the way from inception to asking: Where are we actually going to implement this? What's the impact we're expecting? How does this align with strategic objectives? What are some of the key measurements we want to see in terms of adoption, change, and, ultimately, having the AI start to hit the hard dollars—reduction in litigation, average duration, and things like that.

I'll give you an example. I worked with a large carrier that wanted to implement AI across the entire organization. But they have an aging demographic in certain lines, and getting them to adopt AI would be difficult. They've also captured a lot of information very poorly in their systems—it's very much in their heads.

I said, Let's focus on the younger generation. They’ll adopt AI, and we’ll create a best practice, one that we can use when we bring in new talent. So we built a three-year program focused on them. Ultimately, the program was so successful that the older generation said, We want to be part of that, too. 

For me, the next window for anyone embarking on an AI journey is to focus on embedding it upfront—knowing, of course, that the process will evolve over time. 

Begin with what we call an EDA—exploratory data analysis—to determine what the baseline is. That way, you can prove that you’re opening and closing claims far more quickly and can see the change quarter over quarter. That data helps sell the journey. We've also done quite a bit of work around what we call data quality programs, where we assess the quality and change behavior at the desk in terms of how people are capturing data—all the way from structured to unstructured and, more importantly, in the adjuster call notes. That program embeds the solution into the fabric of the organization.

I think that's the next wave. 

Paul Carroll

Thanks, Mark.

Sponsored by Wisedocs

About Mark Tainton

Mark Tainton is the SVP of Data Solutions at Wisedocs, bringing over 30 years of AI, data and analytics transformation expertise in insurance and financial services. Having served as Chief Data Officer at multiple leading organizations, Mark understands the critical intersection of medical intelligence, litigation strategy, and claims outcomes. He advises Wisedocs on data and product strategy, go-to-market positioning, and the deployment of AI-powered solutions that address the most pressing challenges facing claims and legal professionals today.

Brute force has been the corporate response to the normalization of nine-figure payouts—build taller insurance towers. But by 2026, we've reached the breaking point of that strategy. Adding more capacity is no longer a hedge; it's a target. Leaders who continue adhering to a "wait-and-see" strategy will likely hand over their negotiating power to plaintiffs' counsel. It's time to stop reacting and shift to a more aggressive tactic of data-driven litigation counter-anchoring, a tactical maneuver that uses historical benchmarks and hard modeling to ground a case's valuation.

The Psychology of the First Number

Refusing to name a number isn't a denial of liability; it's a tactical surrender. When we stay silent and treat it as a problem for later, we leave a vacuum that the plaintiff is only too happy to fill. This is the psychology of anchoring: the first number heard becomes the mental hook upon which all subsequent negotiations hang. If the opening bid is a $100 million "lottery ticket," even a successful defense that cuts it in half results in a $50 million disaster.

Counter-anchoring disrupts this by providing a grounded alternative before the plaintiff's number can take root. This isn't a guess; it is a calculated figure backed by historical industry benchmarks and internal safety data. By presenting a credible, data-backed valuation early, we offer juries a "safe harbor."

Most jurors are actually overwhelmed by the emotional volatility of nuclear-risk cases; they want to be fair, but they lack a yardstick. When the defense provides that yardstick—derived from logic rather than emotion—it grants the jury the permission they need to reject an inflated demand without feeling they are dismissing the injury itself.

Deployment: When to Anchor (and When to Pivot)

Counter-anchoring is most effective in "gray area" liability cases—scenarios where the question isn't if the company is responsible, but for how much. In these high-value moments, the goal is to cap the ceiling before it vanishes. By introducing a data-backed valuation early in mediation, you effectively narrow the range between "reasonable" and "astronomical."

However, data is a double-edged sword. The greatest risk in this strategy is the "Cold Corporation" trap. If your counter-anchor looks like a sterile spreadsheet in the face of a human tragedy, you don't just lose the argument; you lose the jury.

There is a razor-thin line between being "grounded in reality" and being "callous to suffering." The math must be the foundation, but the delivery must be human. If the jury perceives your data as a tool to devalue a life rather than a method to find a fair resolution, the anchor will drag your defense to the bottom.

When executed with empathy, speed becomes your primary weapon. By removing the "valuation fog" early in the process, counter-anchoring forces both sides to deal with reality. It strips away the performative inflation of the discovery phase and gets to the heart of the settlement, often shaving months—and millions—off the litigation lifecycle.

The 2026 Toolkit: Credibility Over Calculation

In 2026, a spreadsheet is not a strategy. While internal loss runs are necessary, they are rarely sufficient to move a jury. To make an anchor stick, you must look beyond internal data. A jury will instinctively view a company's own historical figures as self-serving; to achieve true "safe harbor" status, your numbers must be validated against industry cohorts. Credibility is built on external benchmarks—proving that your valuation isn't just what you want to pay but what the broader market defines as objective reality.

The most critical hurdle, however, is the communication gap. Raw modeling is the foundation, but the courtroom narrative trumps all. If you cannot translate a complex actuarial model into a story about fairness and community standards, the data will be dismissed as "corporate math." The numbers provide the boundaries, but the narrative provides the "why."

Finally, this strategy demands a collapse of the traditional corporate silo. We are seeing the rise of the general counsel/risk manager nexus. In the past, Risk bought the insurance, and Legal fought the claims. Today, these two must merge their datasets well before a summons is served. By aligning on valuation models during the underwriting phase, the defense is armed and ready on Day 1 to set the anchor before the ink on the complaint is even dry.

The Underwriting Reality: From Defense to Differentiation

Adopting a counter-anchoring strategy does more than win cases; it fundamentally shifts the power dynamic at the renewal table. In the 2026 market, excess underwriters are no longer just looking at loss history—they are scrutinizing a firm's "litigation maturity." When you can demonstrate a repeatable, data-backed method for suppressing social inflation, you move from being a commodity risk to a "preferred risk."

The conversation with underwriters changes the moment you move beyond passive risk transfer. Instead of simply presenting a tower of limits, you are presenting a proactive defense framework. Underwriters are tired of "blank check" litigation; showing them that you have the tools to anchor damages early provides them with something they value more than anything: predictability. By proving you can cap the ceiling of a potential nuclear verdict, you provide the actuarial certainty that justifies lower attachments or more competitive pricing.

The ultimate result is a stronger strategic partnership with your carrier. You aren't just buying paper to cover a potential disaster; you are demonstrating a sophisticated operational control that protects the carrier's capital as much as your own balance sheet. In an era of escalating awards, the companies that thrive will be those that prove they aren't just insured against the storm—they have the data to ground the lightning.

A Grounded Future

The era of "buying our way out" of litigation risk is over. In a 2026 landscape where $100 million is the new baseline for a nuclear verdict, silence on damages is a luxury no risk team can afford. By embracing data-driven counter-anchoring, general counsels and risk managers can reclaim the narrative, providing juries and mediators with a logical "safe harbor" before the emotional tide takes over.

Success now requires a fusion of math and empathy—a strategy where the data is the foundation, but the story is the house. Ultimately, those who anchor early won't just lower their payouts; they will redefine what it means to be a resilient, data-forward organization in an age of outsized expectations.

In 1979, Gallup asked Americans how much confidence they had in banks. Sixty percent said a great deal or quite a lot. Banks ranked second out of nine institutions — behind only the church.

Today that number is 26%.

The collapse didn't happen because of one crisis or one bad actor. It happened over 40-plus years, one automated interaction at a time. ATMs that replaced tellers. Interactive voice response systems that replaced those ATMs. Digital channels that replaced the IVR. And now AI-driven decisions replacing the digital channel that replaced the thing that replaced the person who used to know your name.

Each wave came with a business case. And each wave, when it touched the moments that actually matter to customers — a confusing charge, a decision that needed explanation, the thing that went wrong at the worst possible time — quietly withdrew a small deposit from an account that doesn't show up on any balance sheet.

That account is trust. And trust, it turns out, is an organizational capability problem — not a sentiment problem.

The Moment That Reveals Everything

Here's what I observed working inside a global bank during those automation waves: the technology worked. The process was faster. The costs came down. And customers were fine — until they weren't.

When something went wrong, people didn't want a faster process. They wanted a person who understood the situation, had the authority to act on it, and demonstrated that the institution they'd trusted actually cared what happened to them. What they got, too often, was a system designed for the average case, handling something that wasn't average at all.

What struck me wasn't the technology failure. It was the organizational failure underneath it. The leaders driving automation were making efficiency decisions. Nobody was accountable for the capability question: Does this organization know how to rebuild trust when the automated system fails a real person? The answer, in most cases, was no — because that capability had never been built. It had been assumed.

That pattern — confusing an efficiency decision for a capability decision and discovering the difference too late — is what eroded four decades of public confidence in banking. And it's the pattern insurers are now repeating.

This Is Now Insurers' Problem

Insurers are making the same bet banks made, in the same places banks made it.

Claims. Denials. Coverage decisions. Underwriting. These are not commodity interactions. They are, almost by definition, the moments when a policyholder is most vulnerable — a damaged home, a health crisis, a business interruption, a death. They are the moments that test whether the relationship the insurer sold is real.

The industry is automating them anyway. With AI systems that make faster decisions, with chatbots that handle first contact, with models that assess claims before a human ever sees them. The business case is real. The efficiency gains are real. The risk is also real — and it is being systematically underestimated.

Here's what gets missed in most of these conversations: The risk isn't primarily in the technology. It's in the organizational capability gaps the technology exposes. Does this organization have the judgment infrastructure to know when a claim needs a human? Does it have the change leadership — not change management, but genuine leadership capability — to ensure that the people still in the room when it matters are empowered to act? Can it tell the difference between a process that's working and a relationship that's quietly eroding?

Most organizations can't answer yes to all three. Not yet.

What Happens to the Humans Left in the Room

Here is the part the business case doesn't model: what automation does to the agents and claims professionals who remain.

When an organization systematically automates the high-stakes moments, it doesn't just remove humans from those interactions. It degrades the humans who stay. Authority gets stripped. Judgment gets overridden. The agent or adjuster who once had the latitude to assess a situation and act on it becomes an escalation path for complaints the system couldn't handle — without the context, the tools, or the organizational backing to actually resolve them.

This matters because the agent is still the face of the insurer when the policyholder calls. The claims handler is still the voice on the other end when the denial needs explaining.

The data on this dynamic in financial services is stark. An Eagle Hill Consulting survey of more than 500 U.S. financial services employees found that 62% say their organizations have prioritized improving customer over employee experience — yet those same employees report that their own work experience directly affects their ability to serve clients. Dissatisfied employees are more than three times as likely to report that their negative work feelings reduce their willingness to help others.

Deloitte's research adds another dimension: When AI tools are introduced without careful design and change leadership, employees perceive their organizations as nearly two times less empathetic and human. That dynamic doesn't stay inside the organization. It travels. Policyholders feel it.

For insurers that rely on independent agents — professionals whose loyalty is earned, not owned — the stakes are even higher. Think of independent agents as the community bankers of insurance: For decades, they've translated corporate rules into human terms, sitting across the table from policyholders at the moments that matter most. J.D. Power's independent agent satisfaction research consistently finds that scores are dramatically higher — by hundreds of points — when carriers make agents easier to work with: faster quotes, transparent claims status, access to a human on complex cases. When AI becomes a black box agents can't explain to a policyholder, that advantage reverses. An agent who can't get a straight answer on a claim denial, or can't reach a human on an exception, doesn't complain to the carrier. They quietly shift their next piece of business elsewhere. The trust problem isn't just with policyholders. It runs through the entire distribution chain.

The Balance Sheet Doesn't Show the Problem — Until It Does

What makes this dynamic particularly dangerous is that trust erosion is invisible on a quarterly basis.

The banking sector learned this the hard way in early 2023. When Silicon Valley Bank failed, uninsured deposits left the broader banking system at the fastest rate recorded since the FDIC began tracking data in 1984 — an 8.2% quarterly decline, industry-wide, in a single quarter. The FDIC noted that SVB's deposits were "remarkably quick to run" precisely because they were concentrated among depositors whose trust, once shaken, had no friction to slow it.

Insurers don't face bank runs. But they face their own version: policy non-renewals, lapse rates, coverage migration, claims disputes that become regulatory attention, and the slow erosion of the trusted advisor position that has historically made insurance a relationship business.

The erosion rarely announces itself. It accumulates in policyholder satisfaction scores that drift, in agent feedback that doesn't make it up the chain, in claims handling data that gets read as operational variance rather than relationship signal. By the time it's visible on the balance sheet, the capability gap that caused it has been open for years.

This Is a Capability Problem. Capability Can Be Built.

The research on AI deployment in financial services confirms what the banking experience suggests. McKinsey finds that AI high performers are more than 1.5 times as likely to have changed their standard operating procedures and talent practices — not just deployed tools. MIT CISR shows that firms stuck in the pilot stage financially underperform their industries, while those that have embedded AI into their operating models significantly outperform.

What those numbers describe, underneath the data, is an organizational capability gap. The high performers aren't distinguished by better technology. They're distinguished by having built the mindsets, the skillsets, and the operating conditions — the governance, the decision rights, the human judgment infrastructure — that allow them to absorb what the technology makes possible without losing what made them trustworthy.

That's the real lesson from banking. The institutions that automated their way into a trust deficit weren't led by people who didn't care about customers. They were led by people who treated trust as a communications challenge rather than a capability one. They managed it. They didn't build it.

Insurers now face a choice that banks didn't get to make deliberately. Insurers can design AI deployments that preserve human judgment at the moments that matter most. They can build the change leadership and workforce capability that determines whether AI enhances the relationship or quietly erodes it. They can treat trust not as a sentiment to be managed after the fact but as an organizational capability to be built before the moment of truth arrives.

Or they can assume their situation is different from banking.

Banks assumed that, too.

As the Penn State wrestling team won yet another Division 1 title over the weekend--its 13th of the past 16 awarded--and did so in overwhelming fashion, I realized there is a deeper competitive advantage at play than exists even in other sports. 

College wrestling dominance requires a layer that goes beyond the normal advantages that come from having a great coach and a roster of superb college athletes. Penn State-level dominance in wrestling requires an additional, self-reinforcing factor--of the sort I think can come from early success with AI, as it builds and builds and builds on itself.

I'll explain. 

To understand that self-reinforcing factor, you need to look at the Penn State coach and at the coach whose record of 15 NCAA wrestling titles in 21 seasons Penn State is now approaching. 

The Penn State coach is Cael Sanderson, arguably the best college wrestler ever. He was undefeated in college, winning 159 matches, and won four NCAA individual titles. He also won a gold medal at the 2004 Olympics. 

The man he's chasing, Dan Gable, who coached the University of Iowa from 1976 through 1997, ranks even higher in the wrestling pantheon. He not only won two NCAA individual titles (in an era when freshmen weren't allowed in the tournament) but took the gold medal at the 1971 world championships and at the 1972 Olympics. In those tournaments, Gable won each of his six matches in those tournaments without giving up a point--a preposterous achievement given how scoring works in international wrestling.

Sanderson's and Gable's credentials are so impressive that they naturally attracted top recruits -- and started to build that self-reinforcing layer. 

Wrestling differs from most college sports because the very best tend to pursue international careers after graduating but don't have any affiliation akin to what other athletes take on in professional leagues. Post-college wrestlers need a home. They need a wrestling room. And the best go to the best room, making it even better... and on and on we go.

Penn State has easily the best roster of collegiate talent at the moment -- six wrestlers made it to the NCAA finals among the 10 weight classes last weekend, tying the record, and four won titles. And Penn State has even better talent among the international wrestlers, who bring with them scores of NCAA titles and medals from world championships and the Olympics. In the finals of the 190-pound weight class at the U.S. trials for the 2024 Olympics, two wrestlers from that room went up against each other and had an epic battle -- which qualified as just another day in the life of Penn State wrestling.

The insurance industry should, I think, draw a lesson because AI can create a flywheel effect similar to what's happening at Penn State and what happened under Dan Gable at Iowa in the '80s and '90s. 

Adopting AI won't happen overnight. Using it is an unnatural act for many people, especially older ones, so you need to find ways to get people to start to get comfortable with it. You need to produce successes that you can use to evangelize about AI. You need to create rock stars that, while not at the level of a Sanderson or Gable, can attract talented people who want to take on more ambitious projects. You need to keep testing and feeling your way toward more aspirational business models, going beyond efficiencies to, perhaps, embedding insurance in other companies' sales processes or developing services that predict and prevent losses before they can occur.

In fact, early successes with AI can generate savings that you can pump into more future projects, so you just keep accelerating. 

(I realize I made more or less this point about a flywheel in last week's commentary on Lemonade, but I think it's so important that it's worth reinforcing, and college wrestling turns out to be even a better example than Lemonade.) 

No competitive advantage lasts forever. Gable retired at age 48 -- coaches often mix it up with their wrestlers, and even an all-time great eventually wears down. The Iowa program, while still strong, has drifted in the decades since. Sanderson is now 46, and maybe he'll tire out one of these days, too. Meanwhile, David Taylor, a just-retired big name, has set up camp at Oklahoma State, which had four wrestlers make the NCAA finals. Three won. All four are freshman. So another cauldron of a wrestling room may be taking shape.

But I'll bet any insurer would be happy with an advantage on AI of the sort that Sanderson has produced at Penn State and that Gable developed at Iowa before him.

Cheers,

Paul

Policyholders are dying with dozens of open digital accounts, no record of what they own, and no plan for what happens to any of it. When that happens, a fraud window opens. That gap has a cost, and insurers are absorbing it. Life insurance is where the stakes concentrate and the exposure is most acute.

Sandra filed the life insurance claim four days after her husband's death. She had everything she was supposed to have: the policy number, the death certificate, executor authority. Her insurer had 17 unverifiable digital accounts, a death record that hadn't reached the broker databases yet, and a fraud window that had been open since the obituary ran.

That's the default condition for life insurance claims today.

The scale of the problem

Policyholders maintain dozens of active digital accounts - financial, medical, cloud storage, subscriptions, social media. Many hold documentation directly relevant to estate and insurance administration. Death doesn't close those accounts; it severs access to them.

Only 36% of Americans use password managers, meaning most policyholders leave no systematic record of what they own digitally or how to reach it. Most major platforms offer some form of legacy contact or digital will feature, but adoption remains low. Death leaves a scattered, largely inaccessible digital estate, one that intersects directly with claims management processes.

Where the cost lands

This is where the exposure becomes the insurer's problem, and that immediate exposure is fraud. After a death, a gap opens between when the death certificate is issued and when that record propagates to the commercial databases that underpin identity verification. During that window, the deceased's digital accounts remain accessible to anyone who can answer a few security questions, questions drawn from the same broker records that haven't been updated yet.

Thieves target recently deceased identities, while life insurers absorb the cost - fraudulent claims, delayed payouts to legitimate beneficiaries, reputational harm when carriers pay bad actors.

There's a legal dimension too. Most platform terms of service were not written with estate law in mind. Even where the Revised Uniform Fiduciary Access to Digital Assets Act (RUFADAA) gives executors legal access to digital accounts, platforms often don't honor it in practice. The beneficiary has a legal right that the platform won't act on. The adjuster has no clean path forward.

Health insurance and workers' compensation face the same fragmentation - medical records, employer portals, and benefit accounts scattered across systems that don't communicate. But life insurance sits at the sharp end of the problem, where the industry's exposure is most acute.

The verification gap

The infrastructure for verifying identity after death has a gap built into it. Deceased individuals' records persist in commercial data broker databases indefinitely, with no real-time connection to official death records. Verification systems that rely on those databases can't distinguish between a living person and a recently deceased one. The fraud window is a consequence of infrastructure that was never designed to handle life transitions.

Sandra's experience perfectly illustrates both sides of that gap. Sandra couldn't get to her husband's financial accounts. Platforms that held documentation she needed for the claim locked her out despite her legal authority as executor. While she was fighting for access, the fraud window that had opened at his death was available to anyone with enough of his personal history to answer a few questions. The accounts she couldn't reach to support her claim were simultaneously drainable by strangers.

AI as accelerant

Voice cloning and deepfake technology now allow a bad actor to reconstruct a deceased person's voice or likeness from publicly available material, and use it to defeat authentication systems that were never designed with post-death scenarios in mind. As a result, the cost of perpetrating this type of fraud is falling and the risk is rising.

No standard consent or identity framework currently governs the use of a deceased person's biometric data. No enforceable mechanism exists for people to specify how their likeness can be used after death, and insurers have no protection against the claims that follow.

The limits of individual planning

Those who use password managers are ahead of their peers, but individual preparation has a ceiling. Even the most organized policyholder can't force their bank, their cloud provider, and their insurer to exchange data in a standardized way after their death. That requires infrastructure that doesn't yet exist.

The question is: Who shapes that infrastructure? And will the sectors with the most to lose have a seat at the table when the standards are written?

A call for industry engagement

The Death and the Digital Estate (DADE) Community Group at the OpenID Foundation, which I co-chair, recently published a white paper and a planning guide laying out the problem and recommendations for addressing it. Developing interoperable standards for the full lifecycle of digital estate management will require expertise from every affected sector; the insurance industry's knowledge of fraud vectors, claims complexity, and regulatory exposure is specifically what's missing from this conversation.

The groundwork for those standards is being laid now. The sectors that engage early will shape the agenda before the formal process begins. If your organization has a stake in how they get built - and insurers clearly do - the DADE Community Group welcomes participation.