Given my education, experience and, I'll admit, personality, mistakes in writing jump up and bite me on the nose. Once, as I flipped through a book, I stopped because something felt vaguely wrong. I read the page I had just glanced at and found a typo about two-thirds of the way down.

Given how much copy I see every day, I see a lot of mistakes, and I think some patterns are worth pointing out. Today I'll focus on the repetition that creeps into our phrasing (no, you shouldn't say people "mutually agree"; by definition, any agreement has to be mutual) and undercuts the crisp confidence we want to project.

These aren't the kinds of mistakes that spellcheck or even Grammarly, in most cases, will flag for you, but they're like termites in a wooden structure. They weaken our writing, while insurance needs to be projecting competence and strength.

Let's have a look.

To me, phrases such as "mutually agree" are like a record with a scratch in it. The phrases quickly repeat themselves, and they hit me with the same sort of screech that a record player can. I realize my reaction is unusually harsh — an occupational hazard and perhaps a personality defect — but such phrases are still worth purging. When you say people mutually agreed to do something, you sound defensive — "Honest, when I say we agreed, I meant it. Really." In fact, in a lot of cases, "mutual agreement" is a euphemism. A coach "mutually agreed" with a team that it was time to part? Yeah, he was fired. Just say "agreed" and get on with it. Your readers will sense your confidence, even if they don't react as viscerally to language as I do. 

If you look a bit, I think you'll mutually agree that there are lot of such screechy phrases. Here are just some that have crossed my desk since I started keeping a list a couple of weeks ago:

• Two people share a common trait. If you share a trait with someone, you have that trait in common, by definition.
• Some number of different people. Why different? You can't have more than one of the same person. But I see "different people," "different businesses," "different" this, "different" that.
• Closely scrutinize. To scrutinize is to look closely at something. You can't look closely closely.
• Major crisis, major catastrophe, major disaster. Can there be a crisis/catastrophe/disaster that isn't major?
• Advance warning. Warning after the fact isn't actually warning.
• Pre-planned. Planning after the fact isn't actually planning.
• Proactive risk management. Reactive risk management isn't actually risk management, at least not for whatever loss you just suffered.
• Someone successfully accomplished something. If you accomplished something, you succeeded. There are many variants of this issue. A New York Times column yesterday, for instance, redundantly said that something "successfully came to fruition" — a new one for me. "Successfully" gets sprinkled into articles and bios like fairy dust. Some aren't inherently repetitive. For instance, bios often say that someone "successfully launched" a product or business. It's certainly possible to launch a product or business that flops, but you wouldn't be telling us about a flop. "Success" is overrated. The word feels needy.
• Speaking of being used like fairy dust, I'll re-up my disdain for new, which I've expressed in earlier rants on language. I appreciate the temptation. We're trying to stir up excitement and move the industry forward, but not everything is new and shouldn't be labeled as such. I'd say the most common (mis)usage I see is "created a new" something (as though you can create an old something). The phrases that most set my teeth on edge are "new record" (as though you could set an old record) and "new innovations" (the root of "innovation" is "-nov-," which means new). Talking about new innovations makes us sound like an old late-night commercial — This product "is new, new, all new. And wait... there's more!"
• Proven track record. The whole point of a track record is that it's proven. It's written down. It's verifiable. You don't need to trust what the tout is telling you about a horse. You can see the track record for yourself.
• Most-well-known. This isn't a redundancy, but it's bizarre, and I'm seeing it a lot, so I'm tossing it in here. The progression goes "good," "better," "best." It doesn't go "good," "better," "most well." So why would the progression about how famous something or someone is go "known," "better-known," "most-well-known"? It doesn't. Yes, "well-known" is a legitimate phrase, but "most well" isn't a thing, so "most-well-known" surely isn't. I think people chicken out because "best" seems like an endorsement. They don't want to use "best" in connection with, say, a notorious criminal, but the only superlative available to you is "best-known." "Most well" simply doesn't exist in the English language, not even if you're describing how done you want your steak to be.

You get the idea. You probably even already go through the sort of self-editing I'm suggesting. You were probably harangued in elementary school to avoid the passive voice and may have been counseled to delete "very" every time you used it. I'm merely suggesting adding something to your to-don't list. 

Your writing will come across as more confident if you eliminate the weak redundancies I've listed — and the million others you'll spot once you start looking.

Fixing these redundancy issues may feel like a small thing, and even a grump like me will acknowledge that the changes will fly under the radar for most people, but I'm reminded of a saying that was my mantra when I used to take long bicycle trips and was packing: "If you take care of the ounces, the pounds will take care of themselves." Customers are demanding that insurance become more understandable, even friendlier. No more of the "whereofs" and "wherefores" in arcane documents that only a lawyer could love. So I don't think it's possible to pay too much attention to the language we use. Every little thing we do becomes part of how customers perceive us.

You now have your advance warning. You can proceed with your proactive pre-planning.

Cheers,

Paul

P.S. Here are some of my favorite previous rants on language: "Can We Please Tone Down All the 'Inflection Point' Talk?"; "Let's Stop With the Gibberish"' "May I Rant for a Moment?"; and "Two Words We Must Stop Using." 

For some reason, most Americans seem to think that when the U.S.-Iran conflict comes to an end, oil prices and the broader economy will quickly bounce back to normal. Unfortunately, that is just not realistic, and the longer-term damage is already set in motion. Subject matter experts are predicting a 12- to 18-month correction period once the situation stabilizes. The backup of oil tankers in the Strait of Hormuz will take at least a year to clear.

A year‑long oil crisis would hit both automobile sales and auto insurance in ways that go far beyond just higher gas prices. The short version: vehicle demand would likely shift sharply toward fuel‑efficient and electric models, overall sales could soften, and auto insurance costs would almost certainly rise due to inflation, repair costs, and economic stress. Below is a structured breakdown grounded in recent reporting and economic analysis.

Impact on Automobile Sales

Demand will shift toward fuel‑efficient and electric vehicles. When fuel becomes expensive for a long period, consumers rethink what they drive. Economic theory treats vehicles and gasoline as complementary goods, meaning high fuel prices suppress demand for gas‑heavy vehicles. Buyers tend to move away from trucks and large SUVs and toward smaller, more efficient cars or EVs.

Overall auto sales could decline. A prolonged oil crisis raises household expenses across the board. With budgets squeezed, many consumers delay big purchases like cars. This effect is amplified if the crisis also disrupts supply chains or raises production costs—both of which are likely when oil prices stay high for months.

Higher vehicle prices due to supply chain strain. Geopolitical disruptions tied to oil crises often spill into shipping and parts availability. Recent reporting shows that conflicts affecting oil supply also cause shipping delays, higher transport costs, and production cuts by major automakers. Toyota, for example, has already reduced output in response to Middle East instability. Fewer cars produced means higher prices for both new and used vehicles, further dampening sales.

Impact on Auto Insurance

Rising premiums driven by inflation and repair costs. Auto insurers are already facing a "severity crisis": repair costs have surged due to inflation, supply chain issues, and the increasing complexity of modern vehicles. A prolonged oil crisis would worsen these pressures by raising transportation and parts costs. Insurers have been "racing to take rate," and pessimistic outlooks suggest continued premium increases.

Higher replacement costs due to vehicle shortages. If automakers produce fewer vehicles because of high energy costs or supply disruptions, replacement vehicles become more expensive. Insurers must pay more for totaled cars, which pushes premiums higher. This dynamic has already been observed during labor strikes and supply chain disruptions.

Changes in customer retention because of Increased financial stress. When households face sustained high fuel costs, they may struggle to keep up with insurance payments. Analysts warn that squeezed budgets can lead to policy lapses, reduced coverage levels, or shopping for cheaper (and sometimes inadequate) policies.

More accidents in stressed industries. In sectors tied to oil and gas, worker shortages and fatigue have historically increased accident rates, which in turn raise liability claims and insurance costs. While this is industry‑specific, it contributes to overall market pressure.

The Big Picture

If the oil crisis lasts a year or more, the most likely outcome is:

• Automobile sales soften overall, with a strong shift toward efficient and electric models.
• Large SUVs and trucks lose market share, unless essential for work.
• Vehicle prices rise due to supply chain strain and higher transport costs.
• Auto insurance premiums continue climbing, driven by inflation, repair costs, and higher replacement values.
• Consumers face financial strain, leading to more lapses, reduced coverage, and slower sales cycles.

Reality bites, but understanding these outcomes and challenges will enable all participants to plan and adjust accordingly.

Paul Carroll

Denise, we were talking the other day about the fundamental changes occurring in insurance, and you had quite a list. Could you start us off by walking us through some of those?

Denise Garth

The industry is changing a lot, and it's not just technology — it's everything. Risk is changing, customer demographics and expectations are changing, where people are living is changing. 

One of the biggest things we're seeing is the growing protection gap. The cost of insurance has increased significantly due to climate and weather events, rising claims costs, and the legal challenges the industry faces. It is unsustainable for customers, forcing them to make difficult decisions such as not buying insurance, switching for a lower cost, increasing deductibles, and more. It is a tipping point of change.   

We see a new era for insurance — one that's really built around intelligence to enable adaptability.

The way forward is going to require both an operating model and a technology foundation redesign and redefinition. We've been talking about transformation for the last 10 to 20 years, and in most cases, it was about ripping out the technology and putting in something new over the existing operating model. Now we must rethink the operating model: how we want and need to do business to remain relevant.

In today's world, products are evolving. You still need auto, but there are so many variations of it now — autonomous vehicles, people doing things with Uber and the gig economy. There's a whole different set of product types needed to support those, and that goes across all products, whether it's P&C or L&A&H. 

We have to do business in a way that fits this future, not the past.

Our operating models have been crafted over decades around a myriad of constraints, business assumptions, and challenges from the past. They've evolved by layering in technologies, manual work, point solutions — and we now face what I call a "spaghetti infrastructure" that has created a really inefficient, unprofitable, and employee-constrained operation. It's added a level of complexity on top of an already complex business. 

Instead of just replacing technology with the next modern core solution, we have to think about what it is we compete on. That's where technology really begins to come into play — not just cloud-native technology and robust core systems, but now AI, both in terms of technology infrastructure and business architecture that can redefine the operating model and business processes. 

In a webinar I just did, I shared that 82% indicate they want to do something with AI, but very few are actually doing it, or they're doing it in a piecemeal way. AI needs to be more than just an add-on technology. It has to be embedded into and redefine how we do business, so you can constantly optimize what you're doing. That redefines the overall business value of cloud and AI-native core that the market begins to see and realize in business outcomes.

I predicted that by 2030, we could see a 20-point reduction in expense ratios — and it's starting to happen as you see publicly traded insurers talk about what they're doing with AI. That is going to completely change the competitive landscape. 

Paul Carroll

For me, the big thing I see companies potentially missing — because I've seen them miss it in other waves of technology over the past several decades — is the need for the agility you mention.

Gen AI is going to allow the sort of breakthrough that Amazon produced in the first wave of the internet. It didn’t just do the old things better; Amazon reinvented retail. If insurers lock themselves into developing a better form of what they've done before, they're going to miss out on a lot of opportunities.

From a technology standpoint, how do you enable the agility that insurers need?

Manish Shah

Before diving into the solution, I want to make sure we also look at the broader, common theme underlying these problems. A lot of people blame the insurance industry for not having modern systems, for not knowing their customers, for not having the right products or pricing. But if you really dig deep, the biggest issue facing the insurance industry — the one causing all those other problems — is that it simply cannot keep up with how fast the world is changing. Insurance is out of phase.

Customer expectations are significantly different and changing almost daily. There’s a huge change in risks and in how those risk profiles are developing. And the technological advancements happening today are leaps and bounds faster than what insurance companies' general culture allows them to absorb.

They're not unaware of the problem. The issue is how fast they can adopt new technology, how fast they can change their culture and get to changes in products, better pricing, better distribution, and so forth.. 

Our view is that it's not just about using technology or solving a niche problem. It's about making your mission-critical systems nimbler and relying on a partner and ecosystem framework rather than a traditional command-and-control framework. 

Not every innovation has to be built in-house from the ground up. The real value companies can leverage is to test the technological innovations that companies like ours bring to them in a meaningful way — roll them out to customers, learn from them, test them, understand user behavior, and refine them.

That's why our approach is not simply about selling technology or a core system. It's about having intelligence built into every workflow, every process, every customer interaction — so you can get meaningful feedback from customers that allows you to evolve faster than the rest.

It's not a technology discussion — it's a speed discussion. How fast can I validate my ideas? That, clearly, is the biggest impediment in the industry.

Most people are still grossly underestimating what AI can and will do to every single business. Insurance is not an exception. Regulations will shield you only for so long, but when it comes to customer service, operational efficiency, improved profitability, faster turnaround, claims resolution, and better underwriting — AI, and more importantly, agentic AI, is going to play a huge role in every single one of those areas.

Whether people embrace it or resist it, in the next 18 to 24 months, a hybrid workforce — built with humans and AI agents working together — is going to be common. We're literally talking about leveraging artificial intelligence not as a tool but as an entity that works alongside humans. And that means the human workforce is going to have a very different role. They won't be writing the first draft — they'll be validating it. That is a huge cultural shift.

If organizations don't start engaging with this thought process early and experimenting with it now, they'll eventually be pressured to do it in a hurry. And if you try to implement this in a rush, even if you can get the technology in place, you cannot simultaneously implement the cultural shift that needs to accompany it. Doing it sooner is critically important.

Denise Garth

We talk about the "capacity gap." The capacity to have the right type of people running the business inside an insurance company is under significant strain — particularly given that a large percentage of the workforce is expected to retire by 2030. Estimates put those losses at 40% to 50%. You're going to lose your underwriters, your claims adjusters, your billing professionals — people who know your legacy systems, let alone people who understand your products and your business.

That's exactly where the hybrid workforce comes into play. Not only can it help you do more with the resources you have, but it can also educate and train new people in a consistent way — creating real value, consistency, and quality for those coming in and trying to learn this business. It gives them the confidence to do the work and learn along the way. That's a major factor in all of this that a lot of insurers haven't fully faced up to yet.

Paul Carroll

Peter Drucker used to say that culture eats strategy for breakfast. And when you look at AI — or just the new technology environment, in general — if you approach it as a destination, something you're going to do once, you're going to fail. 

It has to be a cultural shift, something you work on this week, next week, next month, and the month after that. 

Denise Garth

It really comes down to leadership, because you're going to have to redefine the organization and people's roles — jobs are going to look very different. 

Paul Carroll

How does software need to evolve to support a hybrid workforce of both humans and AI agents?

Manish Shah

Today’s software was designed to be used 100% by humans. And human users have a little bit different constraints than AI users. For example, humans can't process too much information at once. We need multipage forms in a user interface, relational databases, more structured data — things like that. AI agents don't have those same constraints. Software today must be designed for both people and AI agents to do the work they’re best suited for. 

Toward the latter part of the year, we plan to release a brand-new user interface, suited for each type of user. Providing seamless handoffs between them is also a key part of that design consideration. 

The current core system user design is simply not going to be adequate for where the world is moving. The industry has come a long way in the last 20 to 25 years in modernizing, but the fundamental pain points are still there — how long it takes to implement modern software, the cost, how long it takes to maintain it, the total cost of ownership. 

Just like Claude has created a significant dent — in a lot of people's minds and in the markets — with the idea that "I can build the software," we think the same kind of shift is possible for enterprise implementation. Sure, that came with a lot more enthusiasm than realism at first, but I think it will get there.

Why can't AI implement our software? Why does an implementation take three years? Our goal is to build a Claude-like AI capability that interacts directly with business users and translates that into system configurations — allowing our customers to actually move forward.

Paul Carroll

Thanks, Denise and Manish. 

About Denise Garth

Chief Strategy Officer at Majesco, Denise Garth drives thought leadership and innovation strategy for insurers worldwide. She’s a global voice on digital transformation, customer experience, and the future of intelligent insurance ecosystems, shaping how carriers modernize and reimagine their business models.

About Manish Shah

President and Chief Product Officer at Majesco, Manish leads global product innovation across intelligent core systems, AI-powered platforms, and digital ecosystems. A visionary technologist, he’s known for helping insurers accelerate modernization while staying true to human-centric design and trust.

For decades, insurance has relied on a model that assumes time is on its side. Risk could be assessed, priced, and adjusted in cycles. Products evolved gradually, and systems were built for control rather than speed. That model is now under pressure in ways it was never designed to handle.

The issue is not that insurers lack insight. Most organizations have more data than ever before, along with increasingly sophisticated models to interpret it. The problem is far more practical: they cannot act on that insight fast enough. Pricing updates remain tied to fixed cycles, model changes take time to deploy, and by the time adjustments are implemented, the underlying risk has already shifted.

Inside insurance organizations, this tension is well understood. There is no shortage of awareness or intent. The frustration comes from the gap between what teams know needs to happen and what they can execute. Pricing changes sit in queues, model updates wait for deployment windows, and while those changes move through the system, the underlying risk continues to move.

The gap between the speed of risk and the speed of response is no longer just inefficiency. It's showing up in loss ratios, missed growth opportunities, and an increasing inability to compete on speed.

A model that cannot keep up

Insurance was not designed for continuous change. Pricing is still adjusted at defined intervals, underwriting models are updated periodically, and product changes move through systems that assume a relatively stable environment.

Risk no longer behaves that way. Exposure can shift materially between pricing reviews. New data arrives continuously, often from sources that did not exist even a few years ago. By the time updates are implemented, the assumptions they were based on are frequently out of date.

Most insurers recognize this dynamic. The challenge is not diagnosing the problem, but overcoming the structural constraints that prevent them from responding in real time. Legacy systems, internal processes, and the way decision-making is organized all introduce delay, even when the business is trying to move faster.

The result is a fundamental mismatch between how risk evolves and how insurance operates.

When technology slows you down

Much of the industry conversation around innovation focuses on adopting new technologies. But for many insurers, the more immediate issue is the technology already in place.

Core systems continue to underpin underwriting, pricing, and product configuration, yet were built for a different era. They prioritize stability and control, which made sense when change was incremental, but they are far less suited to an environment where conditions shift constantly.

This creates a form of operational inertia. Even relatively straightforward changes can trigger complex processes, requiring coordination across multiple teams and systems. As a result, external changes move faster than internal responses. Updates queue behind IT backlogs, implementation timelines stretch, and opportunities to respond to emerging risks are missed.

It's not a lack of capability that holds insurers back. It's the difficulty of translating that capability into action within the constraints of the existing operating model.

The AI gap is an execution gap

The same pattern is playing out with AI and advanced analytics. The potential is widely understood, and in many cases, already proven. More precise pricing, improved risk selection, and better customer engagement are all achievable outcomes.

What remains unresolved is how to operationalize those capabilities at scale.

In many organizations, AI is still being deployed as a series of point solutions rather than integrated into the core of decision-making. Data remains fragmented, insights are generated in isolation, and the process of moving from analysis to action is slower than it needs to be. This is not a failure of ambition but one of integration.

Without an operating model that can absorb and act on these capabilities continuously, AI risks adding another layer of complexity rather than delivering meaningful transformation. The gap between what is technically possible and what is practically achievable continues to grow.

Innovation that arrives too late

One of the clearest consequences of this dynamic is the speed of product innovation. Emerging risks require new forms of coverage, more flexible pricing, and the ability to adapt offerings as conditions change. Yet bringing new products to market remains a slow, resource-intensive process. By the time a product is launched, the risk it was designed to address may already have evolved.

In effect, insurers are often pricing yesterday's risk in today's market.

This lag has direct commercial implications. It limits the ability to seize new opportunities, exposes reliance on outdated assumptions, and makes it harder to compete in areas where speed and adaptability are becoming critical.

More than an efficiency problem

It's tempting to frame these challenges as operational inefficiencies. At its core, this is a question of missed opportunity. Every delay in responding to changing risk conditions shows up somewhere. In pricing that no longer reflects exposure. In products that reach the market too late. In capital deployed against assumptions that are already outdated.

Over time, this erodes both profitability and competitiveness. It also has wider implications for the role insurance plays in the economy. When insurers cannot respond quickly enough to evolving risk, it becomes harder to price and transfer that risk effectively, which in turn affects how capital is deployed.

A breaking point for the operating model

The insurance industry has adapted to change many times before, but the current moment is different in both speed and scale. What the industry is facing is not a series of isolated challenges, but a structural shift in how risk behaves. The operating model that has supported insurance for decades is reaching its limits.

Closing the gap between the speed of risk and the speed of response will require more than incremental improvement. It will require a fundamentally different approach, one that allows insurers to move from periodic decision-making to continuous, real-time action.

The industry is not short on data, insight, or ambition. What it lacks is the ability to translate those strengths into action at the pace the market now demands. That is why this moment feels different. This is not simply another innovation "phase," it's the point at which the traditional operating model breaks.

Has a family member ever given you a gift you can't bear, yet can't refuse, and it simply becomes part of the decor? It might be a decanter so impractical that it's ornamental, but it has to be brought out every time they come over; or a portrait that asks fundamental questions about the nature of your relationship, yet over time you no longer register that it's there. 

Operational challenges can be like this; unwanted gifts that become clutter, obstacles that are easy to rationalize. As they accumulate, they require incremental effort to navigate and leach efficiency. Yet when we approach a familiar operational challenge from inside the organization, we risk framing the challenge so narrowly that we're boxed in with too few options available. We refer to this as approaching challenges through a lens of our experience - and it can become part of the problem, rather than a means of solving for it.

Seeing a problem through the lens of our experience describes a way of seeing that includes all our knowledge of the history of the problem. All the attempts to resolve it, the failures, the frustrations; it's the voice that says, "We've tried that before and it didn't work." Returning to our furniture metaphor, it's not dissimilar to saying, "We can't move that painting. We took it down once, and my brother got upset." The lens of experience is effective at keeping you on the same track but it's less likely to help change direction. Evaluating a persistent operational challenge through a lens of expertise is vastly more effective.

Approaching a familiar challenge through a lens of expertise means stepping outside of the challenge, viewing it more objectively, and applying our knowledge to that problem. This is the secret sauce of consulting, the classic "outside-in perspective," yet it's possible to strengthen this capability within your own organization. The key is understanding how changing the structure of a problem helps to create new ways of seeing it. By carefully evaluating a problem and adjusting its constraints, experienced operators can see a familiar challenge with a broader perspective, and then bring their hard-won expertise to bear.

I worked with an insurance property repair firm whose leaders shifted their focus from a lens of experience to a lens of expertise with spectacular results. They were part of an insurer's repair vendor panel and found themselves competing across a broad range of repair categories, tackling jobs that ranged from minor fence repairs and garage doors through to major reconstructive work for insureds. Smaller jobs only required general handymen - low cost, low risk, and the pool of available contractors was broad - whereas the larger jobs required more skilled trades and more oversight - higher cost, higher risk, and a narrower pool of trades. Larger firms on the panel could absorb the occasional job that went off the rails, but this firm was small enough that even one or two jobs that went over budget hit profits hard. That was the model. Until this firm opted to re-imagine and renegotiate their panel membership.

The repair firm reimagined their business in two stages: first, they negotiated with the carrier to remain on the panel as a "small repairer." They would only accept smaller repair work but take higher volumes. This was feasible because the pool of trades was large and - given the nature of largely weather-related property damage - jobs were often geographically co-located. One trade could attend multiple sites in a day, which allowed for bundling and improved efficiency. In exchange, the repairer would offer a reduced rate because they weren't subsidizing larger jobs.

Second, they re-designed their operations from within by re-structuring their project management approach. They turned the entire model upside-down, from how they hired trades and retained them to how they would project manage each job. Each repair was broken into its discrete segments (plastering, painting, electrical, and so on) and were arranged such that the right trade attended at the right time - a virtual production line. Trades tapped in and out on their cellphone app, which gave the business visibility of their activity, plus allowed for them to estimate the time required for each job - a feedback loop that informed project, pricing, and contract-hiring forecasts.

The results were significant. The carrier ultimately integrated the model directly into its property claims flow, allowing customers to move from first notice of loss to completed repairs with a speed that hadn't previously been possible. Customer satisfaction ratings exceeded 90%. The firm had transformed itself not by responding to competitive pressure, but by isolating the fundamental conditions of their business and restructuring them to reveal entirely new ways of operating.

Your operations function may be more or less complicated than this example, but there's likely at least a handful of persistent challenges you'd love to unpick. Start by examining the assumptions and constraints that shape how you interpret the problem. Change those, and new solutions will follow.

Artificial intelligence is already changing insurance claims operations. It can shorten cycle times, improve fraud detection, reduce administrative costs, and help carriers handle routine claims with greater speed and consistency. Those benefits are real. But the difference between a useful AI system and a risky one is rarely the model itself. It is the control environment around it. 

After 15 years in financial operations across telecommunications, banking, and healthcare, I have learned that systems do not usually fail because they produce outputs. They fail because organizations do not build the right controls for what happens when those outputs are wrong. That lesson is especially relevant in insurance claims, where AI can recommend payments, trigger denials, or escalate fraud investigations at speed and scale.

This is why the most important question in claims AI is not whether a model performs well on average. It is what happens when it does not. Who reviews the outlier decision? What happens when source data is incomplete or inconsistent? Which claims are allowed to move straight through, and which require human judgment? Without clear answers to those questions, automation creates exposure faster than it creates value. 

The insurance industry has made real progress. Many carriers now use AI in some part of claims handling, especially for low-complexity workflows. But mature deployment remains limited. The gap is not just technical. It is operational. Insurers often struggle with fragmented data, inconsistent workflows, weak escalation paths, and governance models that are more aspirational than enforceable. In practice, that means claims AI often performs inside silos rather than inside a coherent control framework. 

In financial operations, this kind of weakness is familiar. I have seen organizations lose significant revenue not because the systems were incapable, but because no one had defined what should happen when an exception appeared. In one credit control role, I identified more than $10 million in revenue leakages. Those leakages persisted not because no system existed, but because process gaps allowed errors to go unchallenged. Claims AI creates the same risk, except with higher speed, broader scale, and greater regulatory sensitivity.

So what guardrails actually work?

Human review for non-routine claims. Straight-through processing can be appropriate for low-value, low-complexity claims where the decision logic is narrow and well tested. But once a claim involves material exposure, medical complexity, ambiguity in coverage, or fraud indicators, human judgment must re-enter the process. This is not resistance to AI. It is sound risk design.

Explainability for adverse decisions. If an AI system recommends denial, escalation, or fraud review, the rationale must be understandable to the people accountable for that outcome. An adjuster cannot meaningfully supervise a recommendation that cannot be explained in plain terms. Explainability is not just a technical preference. It is the basis for accountability, defensibility, and fair review.

Continuous data-quality control. AI systems do not fail only because of bad models. They also fail because of incomplete, stale, fragmented, or poorly governed data. In claims operations, a data issue is not a minor defect. At scale, it becomes a multiplier of bad decisions. Regular review of upstream data sources, transfer points, and exception patterns is essential.

Defined exception and escalation pathways. Every model has edge cases. Effective governance assumes this from the start. Claims that fall outside confidence thresholds, conflict with policy logic, or present unusual fact patterns should move automatically into a structured review queue with identified owners and documented next steps. In strong operating environments, exceptions are not left hanging. They are routed.

Active regulatory monitoring. AI governance in insurance is no longer an internal policy matter alone. Carriers now operate in an environment of increasing scrutiny around disclosure, fairness, bias, consumer protection, and human oversight. Any organization deploying AI in claims must treat compliance monitoring as part of the operating model, not as an afterthought.

It is equally important to be clear about what does not work.

Principles without enforcement do not work. A statement about responsible AI is not a control unless it is backed by auditability, accountability, and operating discipline.

Black-box decision making in high-stakes contexts does not work. A model that cannot be explained may still produce accurate outputs in aggregate, but it creates real risk when applied to adverse decisions that affect claimants and attract scrutiny.

Deployment on unvalidated source data does not work. AI does not fix weak data foundations. It accelerates the consequences of them.

Minimal staff training does not work. Claims professionals do not need to become data scientists, but they do need enough AI literacy to interpret outputs, question recommendations, recognize limitations, and escalate when needed.

The operational stakes are high. Carriers that deploy AI well can improve speed, consistency, and cost performance. Carriers that deploy it poorly can create regulatory exposure, claimant harm, and reputational damage that overwhelms any efficiency gain.

In the end, the real issue is not whether AI belongs in claims. It does. The issue is whether insurers will build the operational discipline required to make AI trustworthy. The winning organizations will not be the ones with the most impressive demos. They will be the ones with the clearest controls, the strongest escalation design, the cleanest data discipline, and the most accountable governance.

AI can make claims operations faster. Only guardrails make them reliable.

The insurance industry can take pride in the fact that innovation can't happen without it. Until innovators and their insurers figure out how to defray the risk from driverless cars, commercial space flight, etc., they can't go to market. But innovation also can't happen without lawyers. While we non-lawyers complain about how they slow things down, innovations can't scale until the legal system develops a framework for adjudicating the inevitable problems. 

Generative AI is moving into its early legal phase, according to a report from Gartner Group. The report predicts that by the end of the year there will be more than 2,000 legal claims worldwide related to "death by AI," as mistakes by the software or by those implementing it may be the root cause of fatalities. 

The implications will be most immediate for health insurers but will be felt soon enough in just about every corner of the insurance industry, especially where AI is being used to try to anticipate and prevent losses.

Let's have a look.

Gartner frames the "death by AI" issue as a broad one for companies in all industries, suggesting that general counsels need to be aware of the risks and need to work with insurers to purchase coverage. Gartner predicts that by 2030 there will be a 60% increased in corporate spending on security and governance related to AI. From that standpoint, AI looks like a big, new opportunity for insurers.

I'm more concerned about the potential surprises that may be waiting for insurers. 

Those insuring medical practices, for instance, may be caught by surprise if the caretakers turn tasks over to AI that then go awry. Human doctors are still very much in the loop at the moment, but there's a real push toward instituting a combination of telemedicine and automated AI advice, especially to reach people who live in remote areas or other "healthcare deserts." So decisions will real consequences may start moving quickly into the AI. 

The theory is great. You outfit people with wearables that monitor their health, alerting doctors of any warning signs. You coach people on eating, sleeping, exercise and so on. Doctors are reachable by Zoom for consultation and diagnosis. 

But what happens when the AI misses the signs of an impending stroke? What happens when it misdiagnoses a diabetic? 

A columnist in the Washington Post recently wrote about an experiment in Utah that raises all of these questions. It's a very responsible test, limited to having AI refill prescriptions, and could have major benefits. The columnist, an MD and former health commissioner in Baltimore, writes: 

"Right now, getting a prescription refilled can be challenging. Many patients call a doctor’s office and struggle to reach the right person or are told it’s not possible without an in-person visit, which requires time and travel. Some end up putting off that visit and go without medications, which can be dangerous for those with chronic diseases such as hypertension, diabetes and cardiovascular issues."

But she also quotes a professor at Harvard Medical School who says that, "while some drugs might appear to be low-risk on paper, prescribing them is often complicated and patient-specific. He noted that many drugs require ongoing monitoring, including regular lab tests, attention to side effects and careful and nuanced discussions with patients. 'It’s not clear that AI is fully able to replicate that,' he said."

And I believe that people -- including those on juries -- hold machines to higher standards than they do humans. Humans can make errors in the heat of the moment. We know we aren't perfect. But software is written by very smart people who aren't under instant time pressure and are vetted by large, responsible organizations (with deep pockets). So AI can't just be good. It has to be perfect.

The potential for legal surprises won't just relate to "death by AI," either. There will also be "injury by AI," at a far greater rate. (While more than 40,000 people die in car accidents in the U.S. each year, for instance, some 2.5 million are injured.) 

And the claims won't just hit healthcare providers that may have misdiagnosed or mistreated someone. I worry about the companies that use AI to detect dangerous situations in workplaces. What happens when they miss one and someone is hurt or killed? What happens when sensors don't detect the electrical problem in a home that leads to a fire, or the leak that's about to become a flood? When the forward-looking dashcam doesn't spot the deer that has jumped into the road? 

As I've written, consumer advocates are already blaming the big, bad algorithm for any decisions they don't like on underwriting and claims. Those legal issues are about to broaden, especially for those promising prevention via AI.

We'll get through this. The legal framework will gradually develop, and we'll learn what the rules are going to be. But we need to brace ourselves for complications like the coming wave of "death by AI" claims.

Cheers,

Paul

Insurers aren't struggling to collect data. They're struggling to use it before it goes cold.

The difference between catching fraud before payment and spending weeks recovering funds typically comes down to whether data moves through their systems in real time or in batches. That gap is fixable, and it doesn't require replacing core systems to close it.

The business case for real-time data is well established, from faster fraud detection to more efficient claims handling, and sharper underwriting decisions.

What's less straightforward is the path to getting there without destabilizing the systems the business depends on. Legacy architecture, batch-processing dependencies, and deeply embedded operating models represent genuine organizational risk, and treating that concern seriously is the starting point for solving it.

Here's where insurers typically get stuck, and how to move past barriers.

The Barriers to Real-Time Data Adoption

For most insurers, the obstacles are organizational as much as they are technical:

Batch Processing Architecture

Many policy administration systems (PASs), billing platforms, and claims management systems (CMSs) are built to process data in batches, typically writing updates to a database once every night.

The data is accurate, but by the time it reaches an analytics engine, it could be 24 hours old.

For AI-powered fraud detection, the lag is a window of exposure.

Data Silos

Modern, cloud-based software and risk management platforms have torn down many data silos, but enough persist to create operational friction. Claims, underwriting, and billing often run on different systems, and gaps between them can have real-world consequences.

For example, an auto insurer may be collecting telematics data in real time. But claims data is only fed downstream after the first payment is made. So, insights from claims information may arrive weeks after a claim is made.

When fraud history lives in yet another analytics environment, investigators are left to perform time-consuming, manual analyses.

Latency Built Into the System

An API call made every 30 minutes is not real-time data, even if it's often treated as such.

Fraud rings don't operate on half-hour cycles; they execute in minutes. Even a short delay can be the difference between interrupting a payment beforehand or recovering funds days or weeks later.

Organizational Disruption

Replacing core platforms is expensive, time-consuming, and organizationally disruptive. The good news is that building real-time capabilities doesn't require a wholesale system replacement.

Five Steps for Building Real-Time Capabilities Without Starting Over

Step One: Start with Decisions That Can't Wait

Not every process needs real-time data, and trying to modernize everything at once is how transformation projects stall. The better approach is to identify where latency creates the most exposure. For most insurers, that means FNOL triage, claims severity scoring, underwriting risk signals, and fraud assessment prior to payment.

Step Two: Stream Events as They Happen

Instead of importing entire databases, the goal is to stream individual events, such as a claim submitted, a policy bound, a payment requested, as they occur.

The most common mechanism for this is change data capture (CDC), which detects updates in your database and publishes them instantly to a downstream application. Tools like Amazon Kinesis and Apache Kafka are widely used for this purpose. CDC can run in parallel with your existing systems, so you don't have to choose between modernization and stability.

When those event streams feed an AI-powered analytics engine, the model is working with data accurate to within moments rather than many hours, a difference that matters tremendously in fraud detection and claims triage.

Step Three: Build a Real-Time Data Layer

A real-time data layer aggregates events from multiple systems as they continuously update using a message broker to receive, store, and deliver events to consuming applications like an AI analytics engine.

The practical value goes beyond speed. Because the message broker sits between your transactional systems and your AI models, you avoid direct integrations that are brittle and expensive to maintain. The data layer becomes the connective tissue, retaining event history for as long as needed and providing your models with both current signals and historical context.

Step Four: Enrich Your Data

Data enrichment is all about adding information to raw data so it's easier to use it to power a decision.

Enrichment pulls context from external sources such as geolocation, weather data, claims history, and fraud signals, and transforms a data point into a decision-ready insight. This is where AI earns its place in the architecture. An LLM can ingest and synthesize contextual data at a scale and speed no manual process can match, surfacing the risk indicators your team needs before the window for action closes.

Step Five: Connect Insights to Actions

Real-time insights have no value if they don't reach the right person or system at the right moment. That means building automated workflows that route findings directly into operations: flagging a claim for SIU review, pausing a payment pending investigation, or holding an underwriting decision for additional scrutiny.

AI can support both ends of this process: generating enriched insights and helping the teams who receive them determine next steps. The goal is to make sure that judgment is informed by current data.

The technology is mature and the implementation path is clear. Insurers who start with a single high-value use typically see measurable efficiency gains within weeks, not months. What's left is the organizational will to start. For most insurers, that's the only thing still standing between where they are and where they need to be.

Insurance has always been about navigating uncertainty, but the kind of volatility we face today is different. In just a few years, underwriters have had to absorb the impacts of a pandemic, new conflicts, evolving sanctions, and persistent inflation, all while global trade routes and partnerships grow less predictable.

The difficult truth is that many major risks can't simply be avoided. Crude oil still passes through the Strait of Hormuz. Agricultural goods still move through contested territories. The job for insurers is not to reroute around these risks but to reprice them as conditions change.

That shift is forcing a fundamental rethink of how the industry perceives exposure, how it uses data, and how quickly it can make decisions.

When Stability Assumptions Break Down

Most analytical and AI models are built on an assumption of stability. They work best when trade patterns, political conditions, and market behavior stay within the limits of historical norms. But that isn't how the world works anymore.

In a structurally unstable environment, it's not that insurers lack sophisticated tools. The problem is that the information those tools rely on is changing faster than the models can adjust. A sanctions update, a sudden military escalation, or a disruption in shipping routes can alter risk conditions overnight.

When that happens, the gap between model predictions and real-world conditions widens, leaving insurers uncertain about when and how to act.

The True Constraint: Decision Velocity

The biggest limitation facing insurers today is not computing power or model design. It's decision velocity: the ability to act at the speed of change.

Underwriters constantly face a tradeoff. They can make quick decisions based on incomplete information or slower, more informed ones that come too late to matter. That tension is especially visible in specialty markets like marine or trade credit, where exposure conditions shift daily.

To stay ahead, insurers need to move from fixed risk assessments to continuously updated ones that integrate internal and external signals in near real time.

Building Trusted Context at Scale

Improving decision velocity starts with better data, but it doesn't end there. The real challenge is turning large amounts of fragmented data into a foundation of trusted, connected context.

Consider a marine insurer covering shipments through the Red Sea. By pulling in vessel tracking data, shipping advisories, satellite imagery, and even local security updates, that insurer can build a live picture of exposure as conditions evolve.

The same applies to other lines of business. Trade credit insurers can monitor political developments, sanctions dynamics, and partner credit signals to anticipate defaults. Property and business interruption insurers can track supply chain issues or regional cost surges to better understand how claims severity might shift.

When these insights are connected, decision-making becomes faster, sharper, and more confident.

From Static Underwriting to Continuous Risk Assessment

Traditional underwriting cycles were built around periodic reviews: evaluate, bind, and revisit at renewal. In a world where risk conditions evolve daily, that cadence no longer fits. The industry's next step is continuous risk assessment. With a connected data ecosystem, insurers can refresh exposure views constantly, manage forms, endorsements, and pricing as new intelligence arrives, and align capacity decisions with live market conditions.

This approach doesn't replace actuarial discipline; it enhances it with context. The result is underwriting that keeps pace with the environment it's meant to protect against.

Seeing, Trusting, and Acting Faster

The future of insurance will belong to organizations that can see more, trust their data, and act faster than disruption can spread. Speed, in this case, does not mean cutting corners. It means using connected, contextual insight to make sound decisions at the right moment.

In a fragmented, fast-changing world, the winners won't necessarily have the most complex models. They will have the clearest view of reality. Because when everything is connected, the real constraint isn't intelligence. It's decision velocity.

Key Takeaways

• Direct buyers invest heavily in consumer-facing marketing with a single objective: acquire policies at the lowest cost possible.
• The vast majority of policyholders who sell their life insurance never receive a competing offer, creating a structural information asymmetry in the market.
• Fiduciary brokerage representation introduces competition into the transaction and shifts the incentive structure in favor of the seller.
• The life settlement industry needs to prioritize brokerage advocacy as a consumer protection standard, not treat it as optional.

The life settlement market has grown significantly over the past two decades. More policyholders are becoming aware that selling a life insurance policy is a legal, regulated option. More institutional capital is flowing into the space. And more technology platforms are making the process faster and more accessible than it was even five years ago.

But there is a structural problem sitting at the center of this growth that the industry has been slow to address. The players with the largest marketing budgets and the most aggressive consumer outreach are the ones whose financial incentive is to pay sellers as little as possible.

The Asymmetry Problem

When a senior decides to explore selling their life insurance policy, the first point of contact almost always determines the outcome. And in the current market, that first point of contact is overwhelmingly a direct buyer.

Direct buyers, also known as life settlement providers, are institutional investors or companies backed by institutional capital. Their business model is straightforward: acquire life insurance policies from policyholders at the steepest discount possible. Some hold those policies to maturity, collecting the death benefit when the insured passes away. But many do not hold the policy at all. Instead, they turn around and resell it, often immediately, to institutional investors, hedge funds, or bundled portfolio buyers at a significant markup. They are functioning as middlemen, buying low from an uninformed seller and selling high into the institutional market. The policyholder takes the discounted payout while the direct buyer captures the spread.

This is the part of the life settlement market that rarely gets discussed publicly. A direct buyer who purchases a $500,000 policy from a senior for $80,000 and resells it into the institutional market for $160,000 has just made a substantial profit without ever holding the policy as a long-term investment. The senior, meanwhile, accepted what felt like a windfall without ever knowing their policy was worth twice what they received.

None of this is illegal. But it reveals a structural imbalance that the industry has been slow to address. Direct buyers are spending millions of dollars on television ads, direct mail campaigns, digital advertising, and call center operations designed to reach policyholders before anyone else does. Their goal is to be the only offer on the table.

And it works. A significant number of policyholders who sell their life insurance in the United States receive only one offer. They have no basis for comparison, no competitive tension in the process, and no independent representation looking out for their financial interest.

The Marketing Budget Gap

Consider the economics. A direct buyer who acquires a $500,000 policy for $80,000 instead of $150,000 has just improved their return by a significant margin. That $70,000 difference is real money, and it came directly out of the seller's pocket. This means every dollar a direct buyer spends on marketing to reach that seller first is a high-ROI investment, because the payoff is a cheaper acquisition.

Brokerages, by contrast, earn a commission on the transaction. Their fee is a percentage of the sale price. They have an incentive to maximize the payout, but their marketing budgets are a fraction of what direct buyers spend. The result is a market where the loudest voice in the room belongs to the party with the least alignment to the seller's financial interest.

This is not a niche issue. According to the Life Insurance Settlement Association (LISA), the life settlement market processes billions of dollars in face value annually. But the gap between what sellers actually receive and what their policies are worth on the open competitive market remains significant. That gap is the direct consequence of a market where most transactions happen without competitive bidding.

What Brokerage Advocacy Actually Changes

A life settlement broker is licensed by the state and, in most jurisdictions, carries a fiduciary obligation to the policyholder. The broker does not buy the policy. Instead, they take the policy to a network of competing institutional buyers and facilitate a competitive bidding process. The result is straightforward: More buyers see the policy, more offers come in, and the seller receives a higher payout.

The data supports this consistently. Policies that go through a competitive brokerage process routinely settle for multiples of what a single direct buyer initially offers. This is not because direct buyers are acting in bad faith. It is because a buyer in a non-competitive environment has no reason to offer more than the minimum a seller will accept.

Brokerage representation changes the dynamic entirely. It introduces market forces into a transaction that would otherwise be a private negotiation between an institutional buyer and an individual seller who has no leverage, no information, and no representation.

What the Industry Needs to Do

The life settlement industry has made real progress on transparency, technology, and regulatory standards over the past decade. But if the default path for most sellers is still a single offer from a direct buyer with no competing bids and no independent representation, then the market is not functioning the way it should.

There are several things that need to happen:

• Regulatory bodies should require disclosure at the point of sale informing policyholders of their right to independent brokerage representation before accepting any offer.
• Financial advisors and estate attorneys need to understand the difference between referring a client to a single buyer and referring them to a fiduciary broker who will create competitive tension.
• Industry associations should advocate for brokerage representation as a best practice standard, not a secondary option.
• Consumer education efforts need to come from sources other than the buyers themselves, who have a vested interest in keeping the process simple and non-competitive.

None of this requires new legislation or a fundamental restructuring of the market. It requires the industry to acknowledge that a market where most sellers receive exactly one offer is a market that is underserving the people it claims to protect.

The Bottom Line

The life settlement market is not short on capital, technology, or regulatory infrastructure. What it is short on is seller advocacy. The policyholders entering this market are overwhelmingly seniors on fixed incomes making one of the most consequential financial decisions of their later years. They deserve more than a single take-it-or-leave-it offer from the party with the most to gain from underpaying them.

Stronger brokerage advocacy is not about attacking direct buyers. It is about building a market where the seller has a real seat at the table, with real representation, real competition, and a real chance at receiving fair market value for their asset. Until that becomes the standard, the life settlement industry will continue to leave billions of dollars on the wrong side of the transaction.