For the first time ever, direct premiums in P&C exceeded $1 trillion in 2025. Also a first in 2025: a $14.6 billion alleged fraud ring was exposed. (The prior record was $6 billion.)

The watchword for industry executives should be: "entity."

Fraud risk, customer experience, and effective AI? They're all keyed to entity. The money you make, the money you keep, and the faster you grow? Entity, again.

That total of direct premiums means there are now more than one trillion reasons to understand who is paying you and who you are paying. That "who" is an "entity" -- people, businesses, and organizations.

Entities have identity – names, addresses, phone numbers, etc. In logical fashion, there are only three kinds of entities – trusted, unknown, and untrusted. If you can't distinguish among these three kinds, then you are reading the right article.

With interaction, entities also have history, behavior, and outcomes. Entities may be related to each other. Sometimes those relations are very transparent, like parent-and-child or employer-employee. Sometimes they are hidden, like in an organized crime ring or in a conspiracy and collusion affiliation. Entities may be multifaceted – driver, renter, business owner, group leader, member of an organization, neighbor, volunteer, relative, known associate. These relationships all change over time, yet there is still the same entity.

Reflect on this for a pause here. Consider yourself for example as EntityONE. Now quickly list all the roles and relationships you have in the physical world at your home, office and neighborhood, and then online as an emailer, shopper, commentator, reader. Your identity in all those real and digital places may take different forms, but it is always you, EntityONE.

The everyday entity

In the day-to-day of insurance and business life, there is always a concern about fraud and abuse. From application through claims payment, your need to know your business extends from your new business funnel through, third parties, vendors, customers, agents, and even staff.

A new person applies for car insurance, a business makes a claim involving a third party, an invoice arrives from a new address, an agent makes a submission, finance issues a payment – to trust or not to trust?

Names, addresses, phone numbers, etc. are the data vestiges of ways to describe an entity. Either physical or digital in origin, these data are typically scattered across various boxes in an organization chart and different core, ancillary, and API-accessed third party systems.

We store identifier elements like names and address with varying lengths, spellings, inaccuracies, and levels of incompleteness, and in unstructured and semi-structured data entry fields and free form text like notes and templates.

Then we store them again and again over time, moving between systems, between carriers, between vendors, and of course, across multiple CRM applications, which are additionally stuffed with all manner of duplicate and partial records.

Think of yourself as EntityONE

If you tried to have your own self, hereafter called EntityONE, appear the same in every field in every system in every organization over time, you would fail. Even if you never moved and never changed your name, random data entry error alone would ruin your ambition.

One data exercise to try at home: If you have address data from northern California – find a system where "city" is collected as part of an address. Then see how many ways "San Francisco" appears. At one large carrier with tens of thousands of transactions across five years of data entry there were 97 unique entries.

The correct answer was the dominant response, "San Francisco." Shorthand like "SF" and nicknames like "SanFran," "Frisco," and "San Fran" were next. A lower-case version of the correct answer was next, "san francisco." All sorts of typos and transpositions followed. An unthought-of case was a space key entry as a valid character – "S F" is now different than "SF." And those space key values could be leading, trailing, or in the middle. Another very frequent response, when permitted by system data field edit logic, was "blank," no entry at all, or in some cases any number of space key entries.

If you ran a literal matching algorithm on the "city" field, in theory EntityONE could have 97 different data "cities" yet is still only a single unique entity.

Some other factors might also contribute to your failure to have perfect EntityONE data.

One system has separate fields for first name and last name, with no field for middle name and no fields for title/prefix, or suffix. Another system has one long field where all of that is supposed to be entered. Is it Dr. or Mrs. or Ms or Miss with suffix MD, PhD, DO?

Generally, the simplest of contact information – name, address, phone number – can be entered and stored so inconsistently in so many multiple places over time that EntityONE would not exist as a whole and unique name-address in the best of cases.

When it comes to legal entity, the EntityONE Family Trust, or your business version, EntityONE., it's still you, but you now may also have shared rights and not be the only decisionmaker. So enough of thinking of just yourself.

Think of how difficult it might be to search for your customer as their data is entered and maintained across different systems in different ways. Your decades-old processes still treat paper and data as if they were entities, not as entities that have related paper and data. 

This work process of literal data computing is at the core of delivering customer experience but allows an opening for fraudsters and is the bane of AI.

Let this sink in: Data are not entities; entities have data.

Entities have data. You as EntityONE are unique. All the aliases, name changes, addresses, business titles, partnership and shareholder situations, and your honorifics aside, you are still you. Even after you pass away, the estate of EntityONE will persist.

Resolving the many ways to identify you is now what you need to turn inside out.

Every other person, business, group, and organization has the same issues. When you encounter any identity, you need to resolve it down to the core entity, or you will not know who you are dealing with.

Whether an entity is legal or not legal or illegal or foreign or even sanctioned, as we think on the identity data we see every day, many entities present as if their data is thin, with seemingly little to none. Some appear squeaky clean. Some have long years of history. Some look like they popped out of thin air. Some, like a bad penny, keep popping up after we have decided not to interact with them. Synthetic, assumed, straw man, take over, hacked, phished, fraudulent, and other forms of malfeasance also exist.

Keeping tabs on entities (e.g. people and organizations), and the hidden relationships among them in real time is now practical with advanced analytics powered by a technology known as entity resolution. Entity resolution brings all the snippets of various identifiers around an entity into focus.

Entity resolution may involve several efforts, all claiming to do the same thing across your data and computer laden landscape. In the earliest days of computing, crazy sounding technical terms sprouted to try to address this existential data identity issue around keeping EntityONE clearly in focus. It started field by field in databases and has modernized to complex multi-attribute vector and graphical analytics.

These geeky but incomplete early algorithms left a lot undone while still showing some value – they had names like Levenshtein (an edit distance formula for suggesting a typo was made in text similarity), Hamming distance, and more recently in AI terms, tokens with Jaccard and Cosine TF-IDF similarity approaches. There are dozens upon hundreds of challenger approaches. But an analytic or a technique is not a product or a solution.

An early inventor created a combination of steps and orchestrated a set of code he called "fuzzy matching." (In memory of Charles Patridge, here is a link to a seminal paper he wrote.) Many data analytic communities shared that code and subsequent innovations to make progress on name and address standardization and name and address matching. The postal service benefited greatly with more deliverable mail, and database marketing boomed, while customer analytics and lifetime value ascended, as did provider and agent and vendor scorecards with more ambitious service level monitoring.

As with many other business problems, necessity is the mother of invention. Almost every company now has inventions that come from do-it-yourself, homegrown efforts. It is the only way forward before a workable, scalable solution is created.

Also likely installed are several versions and half attempts of making the problem better inside an application or between systems. First, companies used data quality checks, then field validation efforts, then more hardened data standards. For all that work, the human data entry staff invented "99999" and other bypass work hacks. You can see that still today.

This data is what you are training your AI models on.

The largest legacy problem today is this data pioneer spirit turned hubris. IT pros and data science teams do the best they can with what they have – full stop. The satisficing behavior limits their contribution. It also injects unneeded error into all the models they are building and operationalizing. Much of the AI risk is self-inflicted poor entity resolution management. Actuary staff feel largely immune at the aggregated triangle and spreadsheet point of view, but that is a false sense of security, since they cannot see into the granularity of transactions beneath a spreadsheet cell. This is changing dramatically fast with the emergence of the machine learning and AI wielding actuarial-data_scientist corps of employed professionals, academicians, and consultants.

New techniques like large language models (LLM) are making short work of text data in all forms to create new segmentation and features for existing models, while also enabling new modeling techniques to iterate faster. The next phase of workflow improvement is almost limitless. All these great breakthrough efforts need an entity level of application to have their highest value.

The rise of industrial-grade entity resolution

The financial stress indices are high. The sympathy toward companies is low. The opportunity to use AI and seemingly anonymous internet connections makes people think they can't get caught – a presumption with a lot of truth to it these days.

A shout out to our industry career criminal counterparts enjoying the status "transnational criminal organizations": Terms like straw owners, encrypted messaging, assumed and stolen credentials, synthetic identities, and fake documentation are now everyday occurrences.

And that's just what relates to money. For truly awful perpetrators, anarchists, drug dealers, arms dealers, human traffickers, hackers, terrorists, espionage, traitors, nation state actors, and worse, the problem space of entity resolution is mission critical.

Keeping tabs on entities (e.g. people and organizations), and the hidden relationships among them in real time is possible today. It elevates internal "good enough'" learned implementations to "never finished being done, continuously adapting, and real time' data driven implementations."

What you should do about entity

The most capable solutions sit around existing efforts in place, so no need to rip and replace anything. This makes entity resolution prioritization easier, as it can be adopted with what you do now. This extends to your analytic ambitions in cyber resilience and digital modernization, as it can interact seamlessly with additional identifiers like digital entity resolution – emails, domains, IP addresses, that have an address corollary to a street address in a neighborhood. (Here is an earlier article I wrote for ITL on "Your Invisible Neighbors and You.")

Do yourself, your board, your customers, and your future AI successes a favor and get serious about entity and entity resolution as the nearest thing to a single truth as you can get.

Some Background

The author has built matching and fuzzy matching applications multiple times with multiple technologies over a four-decade career and advises that benchmarking is essential for understanding fit for use in entity resolution. A four out of five, or 80%, accuracy might be fine for some use cases and considered corporately negligent in others.  Getting to the high 90s takes much more data and resources than most internal teams can dedicate on a sustained basis. 

A practical example from the author’s experience is Verisk Analytics, where they have billions of records of names and addresses coming from hundreds of carrier systems, all needing attribution to an entity level for highest business value. They have instituted an industrial solution to supplement or replace methods the author’s team built originally for fraud analytics. 

The vendor they give testimonials for is one that is now being adopted in insurance after widespread use in governments and security, customer management, financial integrity, and supply chain use cases globally. It is called Senzing. Their methodology creates the capability to recognize relationships across a number of data attributes and features shared across disparate records and systems, e.g.  names, addresses, phone numbers, etc. in real time. 

Modern entity resolution systems can deploy inside your company as an SDK, so you never need to share any data to move forward. Multiple use cases around your enterprise can also derive benefit from improving entity resolution management so it is reliable on the first shot. 

Paul Carroll

We've had a prolonged dance with the Federal Reserve over whether they would cut rates again this year, and they finally did, on Dec. 10, right as you and I began this conversation. They also signaled they’re probably done for a while. Where do we go from here?

Michel Léonard

First, I think the Fed made a policy mistake by cutting rates and changing monetary outlook from easing to holding. Setting expectations is more impactful on growth than actual rate changes. By saying “don’t expect rate cuts” they took the wind out of the current easing’s impact. We’re lucky the stock market didn’t drop by 4-5% in the days since. 

Instead, the proper policy would have been, in my and many economists’ opinion, to skip the cut but keep easing expectations alive. That would have a strong multiplying impact on GDP. 

Had the Fed stuck to easing, we would have started to see decreases in mortgage and auto loan rates by Q3 2026. We needed those lower rates to fuel homeowners and personal auto insurance premium volume growth. Instead, we’re likely to face historically high mortgage and auto loan rates through Q1 2027.  Most likely, we’re stuck with weak housing starts, weak existing home sales, and lower auto sales, and without that homeowners and personal auto premium volume driver. 

Commercial property, especially, needed the Fed’s help. We have all these commercial Class A downtown conversions into housing sitting still. This is Q4 2023 all over again: The Fed said, Don’t expect more rate cuts – and took the wind out of economic activity throughout 2024. It was just starting to recover by now. The Fed took the wind out of Class A conversions then, and it’s going to do it again. Conversions were starting to recover – now expect no significant changes until Q4 2026.

It’s likely the Fed just caused another soft year of overall U.S. GDP growth and P&C insurance underlying growth, especially when it comes to economic premium volume growth drivers. 

I was just looking at premium volume growth for homeowners, personal auto, and commercial property in 2025. Typically, actuaries build in a baseline for premium volume growth by adding net GDP growth and CPI.  For 2025, that would bring us to about 7%. But premium volume growth for those lines is below 5%. The argument can be made that, at that level, premium volume growth was flat to negative in 2025. 

Paul Carroll

You make a compelling case, as always. So why do you think the Fed cut rates again?

Michel Léonard

I was surprised that the Fed would cut once this year. I was surprised when they cut twice, and I was speechless when they cut a third time. 

The Fed's estimate is for real GDP growth to decrease to about 1.7% by 2027. That's starting to be at the lower end of their goal. They do not see inflation picking up significantly, which is probably why they felt comfortable with the statement about further cuts.

But they’re totally flying blind here.

There’s the diminishing growth multiplier impact of rate cuts by changing expectations from easing to holding. Perhaps even more so, the Fed decided to do this with no GDP numbers since June, and no CPI and employment numbers since September. For GDP, getting data for Q3 was critical because of inventory depletion in Q2. The same for getting CPI and unemployment numbers through November. You can’t make decisions about monetary policy without those three.  How about without even one?

Paul Carroll

With Trump expected to name his next nominee to run the Fed in January, does that introduce another layer of uncertainty into the equation?

Michel Léonard

There’s a lot of noise in the market asking why the Fed made the statement about the direction of monetary policy. It did not need to.  One view is that it did so to preempt rate cuts-galore next year with Trump’s new appointment(s). I don’t think that’s the case.

First, there are many governors other than the chairman who get to vote on rates. 

Second, the Fed has already altered its inflation target. A rate cut with CPI at 3.0% means the current board of governors already tolerates annual inflation up to 3.5% (significantly more than the former 2.0% goal). 

Third, I was surprised by how mainstream the president’s leading candidate for Fed governor, Stephen Miran, is. He’s a consensus candidate, even though he might put more emphasis on growth than price stability when it comes to the Fed’s dual mandate. Personally, I see that shift, within reason, as beneficial to the overall economy. That said, tolerating inflation up to 3.5% is not the same as up to 4.0%. That would ring alarm bells even from me. 

Now keep in mind that an increase of one percentage point in tolerable annual inflation is a significant number.  For context, 1% compounded over a 35-year career means U.S. households have to increase their annual savings by 21% just to keep up. 

Paul Carroll

What dates should we keep in mind for releases of economic data, so we know whether we’re getting a nice present or a lump of coal in our stocking?

Michel Léonard

The next key date is Dec. 16, for unemployment data. A couple of days later, we get CPI, then GDP on the 23rd. Let me walk through these in chronological order, starting with unemployment.

The recent ADP numbers were a bit worse than expected but certainly within an acceptable range. We're currently at 4.40% unemployment in the U.S., and the consensus is that the new number will be 4.45%. If we get anywhere above 4.45% or 4.5%, I think the market may start reacting. [Editor’s note: The unemployment rate came in at 4.6%.]

The market consensus for the CPI number right now is 3.05%. I think we can be fine up to 3.2% or 3.25%. If we get above that, if we get to 3.5%, that might not be catastrophic, but it would certainly be the last nail in the coffin of further rate cuts. [Editor's note: The CPI number came in at 2.7%. There were, however, anomalies in data collection because of the government shutdown, so the number is being treated with some caution.]

Now we get to GDP. The market consensus expectation for Q3, at 2.48% growth annualized, is much more than I and the Fed think is feasible, which is around 1.9% and 2.0%. The market consensus is likely overly optimistic because Q2 GDP reached 3.8% on a quarterly basis. Again, we’re flying blind. 

Paul Carroll

We’ll have another of these conversations in January, and there’s so much uncertainty now, even about the economic numbers, that I can imagine you’ll want to hold your thoughts about next year until then, but can I tempt you into making any projections about 2026?

Michel Léonard

Market reaction to the Q3 and November economic releases will be critical in determining the course of the economy in the next six months, which makes that Dec. 23 release unusually significant in terms of potential impact on the equity market, consumer spending, and private commercial capital investments. 

My concern with the equity markets is the Fed's statement about expectations. And you can write this down: I think that decision is the most ill-advised the Fed has made in three years.

Paul Carroll

Thanks, Michel. Great talking to you, as always. 

My recent article on continuous underwriting drew some pushback from peers regarding the Tesla Insurance case study. Their argument: Progressive, State Farm, and Allstate have offered usage-based insurance for more than a decade—and they write far more of it. So, what makes Tesla Insurance special?

The difference is that Tesla sells cars, not insurance. Auto insurance is a variable in the car's total cost of ownership (TCO)—and Tesla Insurance exists to drive that number toward zero, or as close to zero as possible. Insurance isn't their business; it's their anti-business.

Tesla brings world-class engineering, its own AI infrastructure, and relentless drive to overcome barriers—including regulatory ones. With a $1.5 trillion market cap (as of this writing)—the equivalent of 11 Progressives, 24 Travelers, 28 Allstates, or 40 Hartfords—it packs serious financial firepower.

Embedded Insurance

It's hard to discuss continuous underwriting without considering embedded insurance. When Tesla bundles coverage with vehicle purchases, when Apple offers device protection, or when DoorDash automatically includes delivery insurance—these feel like features, not insurance products. The insurance almost disappears into the broader service relationship.

Tesla Insurance captures customers at the point of sale, when they're most inclined to bundle. Its customer acquisition cost is nearly zero (versus the industry average of $200-$800), it writes policies 20–30% below standard rates for good drivers while maintaining acceptable loss ratios, and its Net Promoter Scores exceed the industry average by more than 40 points.

This kind of experience turns insurance from a grudge purchase into an invisible part of something people actually enjoy. That perceptual shift creates space for continuous underwriting innovations to take root gradually, without friction.

The Convergence Thesis

Here's where this gets strategically fascinating: Multiple trillion-dollar technology companies—Tesla, Amazon, Apple, Google, Microsoft—all have business models where minimizing insurance costs creates competitive advantage in their primary markets. They possess:

• Superior data: continuous behavioral and environmental monitoring through devices and services
• Direct customer relationships: eliminating distribution costs that constitute 15-25% of premiums
• Technology infrastructure: claims automation, fraud detection, and risk modeling capabilities
• Brand trust: customers already trust them with payments, personal data, and critical services

Traditional insurers face a daunting scenario: the companies best positioned to innovate in insurance have no interest in sustaining the industry as currently structured. They want insurance to become a nearly free utility that enables their actual businesses.

The Timeline Question

• Will this disruption happen quickly or slowly? The answer varies by line:
• Auto insurance: 10-15 years as autonomous vehicles scale
• Cyber insurance: 5-10 years as security tools improve and become commoditized
• Property insurance: 15-20 years as smart home technology reaches critical mass
• Health/life insurance: 20+ years due to regulatory complexity and medical cost inflation

But the direction is clear. We are moving toward a world where insurance exists primarily as:

• Embedded features bundled free or nearly free with other products
• Regulatory compliance where coverage is legally mandated
• Catastrophic protection for truly unpredictable tail risks

The companies building this future aren't insurers trying to sell more policies. They are automakers, technology platforms, security vendors, and device makers for whom insurance is an obstacle to be minimized or eliminated.

Traditional insurers are defending a $1.3 trillion U.S. market. But they're facing adversaries who would gladly destroy 70% of that market if it means selling more of their own products.

The Transition—The Cybersecurity Vendors

Cyber is a textbook case of the elimination incentive. Cybersecurity firms like CrowdStrike, Palo Alto Networks, SentinelOne, and Microsoft profit from prevention, not protection—making cyber insurance their natural competitor.

Some see opportunity in cooperation. CrowdStrike partners with insurers, sharing data to improve underwriting and prove its users suffer fewer breaches. That helps customers pay less for coverage while quietly shrinking the cyber insurance market itself.

Microsoft's position is even more intricate. As both a top security vendor and the source of many exploited vulnerabilities, it has every reason to make breaches rarer. Its visibility into corporate systems through Azure, Office 365, and Windows gives it the data to underwrite risk directly—or eliminate it by making insurance nearly irrelevant.

The endgame isn't selling policies; it's securing systems so completely that the need for insurance disappears.

The Transition—The Hybrids

Out on the edge where technological possibility meets regulatory reality, a new wave of tech‑enabled MGAs and MGUs is emerging. They blend niche specialization—restaurants, beauty, wellness—with continuous underwriting, real‑time risk visibility, behavior‑based credits, and agent‑first digital distribution and economics.

In simple terms, they aim to be the best "program carrier + software layer" for independent agents in specific verticals, not broad, direct‑to‑consumer threats to the long-standing agency model.

With digital sophistication rivaling top cyber vendors, these companies work comfortably within existing regulations and sell through independent agencies using a human‑first model—spending most of their day actually talking to people.

If Tesla Insurance is a glimpse of what auto coverage will look like in 10 years, startups like Rainbow, Next, Coterie, Relm, and Thimble show the incremental progress toward that future happening right now.

In the end, healthy tension between innovators pushing for better experiences and regulators safeguarding financial markets keeps our industry moving—slowly, but in the right direction.

Based on the notion that sunlight is the best disinfectant, I've long advocated that homeowners insurance companies give clients as much information as possible about the risks they face. Don't just quote me a premium. Tell me that, perhaps, I'm at more risk of flood or wildfire than old government maps show--and help me understand what I can do to reduce those risks.

Zillow just took a step in the opposite direction. 

It had announced 15 months ago that it would feature detailed climate risk information for flood, wildfire, wind, heat and air quality, but the company quietly dropped that information last month. 

The reason is obvious: pressure from sellers who didn't want the risks to their properties spelled out.

The implications are disheartening. 

What Zillow was attempting was always going to be tough, because we humans aren't wired to think rationally about probabilities. If some political poll says a candidate has only a one-in-10 chance of winning, and they win, we leap to the conclusion that the poll was wrong and the pollster incompetent. Maybe. But maybe not. 

The only way to test is to look over a body of work and over time. Did those predicted to have a one-in-two chance win about half the time? Did the one-in-fours win a quarter of the time? Did those one-in-10s win a tenth of the time?

But models like the one from First Street that Zillow used haven't been around long enough for us to have much evidence about whether they're right when they say there's a one-in-50 chance of a wildfire affecting a home each year. 

A spokesman for First Street said, "During the Los Angeles wildfires, our maps identified over 90 percent of the homes that ultimately burned as being at severe or extreme risk — our highest risk rating — and 100 percent as having some level of risk, significantly outperforming CalFire’s official state hazard maps.”

But we humans are still wired to think, "Zillow said I was at severe risk of flooding, and I didn't have a flood this year, so those bozos were wrong." In the context of the risk ratings provided by Zillow, someone with a house to sell would surely also think, "And their error is costing me money."

While that sort of thinking led to enough pressure on realtors, a key constituency of Zillow's, that Zillow pulled the ratings, there's still some hope for the long run. Even Zillow still provides a link to First Street so those curious enough can find information about risks to properties they might buy. And good models like First Street's will not only get better but will be more accepted over time, as they build up a track record.

It'll just take longer than I had hoped, perhaps much longer.

Sorry, I don't make the rules. I sure wish I did....

Cheers,

Paul

P.S. So I don't end on a total downer, I'll share two links that contain a healthy dose of encouragement. First is a webinar I did recently with Francis Bouchard, a managing director at Marsh McLennan who has focused on resilience for years, and Nancy Watkins, a principal at Milliman who has developed a Data Commons to help mitigate wildfire risks in the wildland-urban interface. Second is the ITL Focus from September on resilience and sustainability, featuring an interview with Francis and parts of an interview with Nancy. 

Both describe the sort of conversation that insurers need to have--and are starting to have-- with architects, builders, city planners and others so that, as a group, we can build resilience into properties from the outset and can at least offer advice to homeowners and communities on how to reduce risks related to severe weather. 

The P&C insurance industry is about to lose nearly half of its workforce to retirement in the next five years, driven by the baby boomer exodus from the workforce. Much of that loss is deep expertise about underwriting, decades-honed claims handling skill, and the undocumented tribal knowledge that carry the day for carriers.

This "Great Retirement," also called "The Silver Tsunami," is fast approaching. According to a recent survey by APQC (American Productivity and Quality Center), 93% of insurance CxOs are genuinely concerned ("mission-critical", "strong", or "moderate" concern) about this knowledge hemorrhage. Coincidentally and paradoxically, the same percentage of carriers are not capturing knowledge consistently from departing employees before they walk out the door.

The result of this concern-complacency disconnect? A perfect storm of knowledge drain, compliance exposure, operational disruption, and customer experience degradation—unless insurers leap out of the "boiling frog" syndrome.

Methods Create Barriers

According to the survey, 83% of respondents capture knowledge using manual methods such as people-to-people transfer and time-consuming documentation, a Sisyphean approach that is neither scalable nor sustainable. No wonder time (mentioned by 62% of respondents) and resources (mentioned by 41% of respondents) topped the list of barriers to knowledge capture and management in the survey.

While interest in AI remains high, a stunning 87% of carriers surveyed have yet to operationalize it to automate knowledge capture and management. AI adoption has been slowed down by concerns about compliance (cited by 59%) and correctness of answers (cited by 38%). AI initiatives have been stymied by "garbage in, garbage out" where some carriers tried to slap AI onto enterprise knowledge silos of dubious consistency, accuracy, and compliance. No wonder a recent MIT survey found that only 5% of AI deployments have created any business value!

Trusted Knowledge Foundational to AI Success

The precious few AI-savvy carriers succeed in AI with a trusted knowledge infrastructure, which addresses adoption barriers such as correctness of answers and compliance head-on. At the same time, these organizations use AI to automate the knowledge capture, management, and optimization process:

• Capture questions that are the highest in volume, value, and complexity, and mine gold-standard answers for them from customer interactions with high-performance agents and intra-enterprise conversation stores among SMEs
• Capture procedures from flowcharts into in-band guidance for customer conversations
• Create drafts of knowledge articles that are aligned with the brand voice for human experts in the loop to review and approve
• Curate content to make it findable and AI-ready
• Analyze and optimize to identify gaps and improve knowledge performance

Winning best practices

Leading carriers treat knowledge as a strategic asset rather than a collection of documents and unstructured content. Their best practices include:

• Using AI to continuously capture expertise from daily work, not just end-of-career interviews
• Embedding trusted knowledge in claims, underwriting, and customer service systems
• Including compliance checks in knowledge workflows to ensure that answers are correct and aligned with regulatory requirements
• Training employees to use AI tools as assistants and not adversaries

A 10X acceleration in creation and curation of knowledge and a 3X acceleration in time-to-value is possible when companies use AI well.

The Bottom Line

With AI-powered knowledge capture and management, forward-thinking carriers capture, preserve, and activate institutional knowledge at scale—so every employee—from new adjusters to seasoned underwriters—can access trusted answers and the best thinking the organization has to offer exactly when they need it. Others are well advised to follow suit lest the Silver Tsunami sweep them away!

Around the world, political, economic, and social uncertainties are on the rise. They can affect every aspect of a company's operations, as well as lead to significant changes in financial, regulatory, and legal environments. Failure to anticipate and adapt can expose companies to operational failings, financial loss and reputational harm with consequences for the companies' directors and officers.

According to Allianz Commercial's latest Directors and Officers (D&O) Insurance Insights report, D&Os can be held accountable for misjudging the impact of geopolitical developments on their company's operations or for failing to adequately adapt to the legal or regulatory requirements in different countries. Liability for D&Os may arise from shareholder lawsuits or regulatory penalties directed both against the entity and individual decision-makers.

At the same time, cyber liability risks for directors and officers have risen sharply in recent years with higher expectations for board level oversight of cyber security and a trend toward more litigation and regulatory actions. Exposures for D&Os typically arise from their duty to oversee the organization's cyber security posture.

Claims against directors have been triggered by a wide range of events, including data breaches, ransomware attacks, and even technical failures. Ransomware accounted for around 60% of the value of large cyber insurance claims (>€1mn) seen by Allianz Commercial during the first six months of 2025, according to its annual Cyber Security Resilience Outlook. Should a cyber incident result in financial loss, directors could face legal claims from shareholders, customers or suppliers if the board is seen to have failed to implement adequate risk controls or business continuity planning.

Insolvencies drive D&O claims globally

Bankruptcy and regulatory enforcement actions are among the top sources of private D&O claims, although claims can also arise from allegations for breach of fiduciary duty, such as misleading or inadequate disclosure, or negligence. According to Allianz Trade, global business insolvencies are expected to rise by 6% in 2025 and 5% in 2026. Next year will mark five consecutive years of increases to reach a record high number of bankruptcies, 24% above the pre-pandemic average. Insolvency risks are particularly concentrated in the automotive, construction, retail, and consumer goods sectors.

There has also recently been a notable rise in "mega bankruptcies" in the U.S. – those filed by companies with over US$1bn in reported assets. The first half of 2025 saw 17 such bankruptcies, the highest number since the Covid-19 pandemic, with 32 in the past 12 months, well above the historical average. The current challenging business environment – marked by factors such as tariffs, weak demand, rising costs, technological transformation, growing competition, and regulatory changes – is heightening the risk of bankruptcy and also claims against directors.

Claims activity is increasing in the highly dynamic D&O market

Over the past three years, there has been a continual increase in the frequency of new claims against directors and officers, now approaching or exceeding pre-pandemic rates in most regions of the world. Claims severity continues to be an issue in North America. For D&O insurers, the U.S. especially is a highly complex market due to its high frequency of securities class action claims and surging average settlement costs, which rose by 27% in the first six months of 2025 to US$56mn. Meanwhile, shifting governmental policy in the U.S. and parts of Europe regarding DEI (diversity, equity, inclusion), ESG (environmental, social, governance), and artificial intelligence (AI) have introduced new complexities for boards to navigate.

To read the full Allianz Commercial D&O Insurance Insights report, please visit: Report | Directors and officers (D&O) insurance insights 2026

There's nothing like starting the year off with some good news. Conning's Year-End Life-Annuity forecast for 2025-2027 certainly has a lot of that. For life-annuity industry executives, statutory net operating results, after tax and dividends, are forecast to increase from $27 billion in 2025 to $30 billion in 2027.

Those strong results start with premiums, of course. Due to an increasing number of individuals saving for retirement and pension risk transfers, annuity premiums are projected to increase 16% from 2025 to 2027. Meanwhile, life insurance remains a key financial need for younger generations starting or building their families. As a result, life insurance premiums are forecast to increase 8% from 2025 to 2027.

While premium headlines grab attention, life-annuity insurance executives know profitability depends on successfully managing investment returns, reserves and capital. At the same time, both life and annuity insurers need to begin grappling with the potential impact of GLP-1 drugs on claims and pricing.

Investment performance drives sales and profitability

Life-annuity sales and profitability are sensitive to General Account investment performance. That performance depends on two broad factors: the external rate environment and asset allocation strategy. As we look out to 2027, we see the continuation of a favorable external interest rate environment. We also expect insurers to continue diversifying their asset strategies to achieve higher portfolio yields.

Portfolio yields forecast to increase through 2030

Even if the Federal Reserve cuts rates over the forecast period, we project life-annuity insurers will benefit from higher portfolio yield. Higher portfolio yields support fixed annuity and universal life interest-crediting rates, which are favorable for more sales over our forecast period.

Our portfolio projection uses the moving average ten-year Treasury rate and models three scenarios. The first is based on the third quarter of 2025 Philadelphia Federal Reserve's Survey of Professional Forecasters. The second assumes credit losses reduce the spread over Treasuries achieved in insurer portfolios. The third is an aggregate blend of one and two. By 2027, our aggregate projection is for the portfolio yield to reach 4.22%, up from 4.03% in 2024.

Continued asset diversification supports higher portfolio yields

During the longer-for-lower interest rate period of 2015 through 2021, life and annuity insurers began diversifying their assets to generate higher General Account portfolio yields. They decreased allocations to bonds and redistributed to mortgages and Schedule BA assets (alternative assets such as joint ventures, hedge funds, and private equity investments) to gain yield. In addition, there has been a marked shift within the bond portfolio towards private credit.

Even with the recovery of interest rates, we anticipate that diversification efforts will continue through our forecast period.

Reinsurance Continues to Support Growth

Whether onshore or offshore, reinsurance remains a key reserve and capital management tool for life annuity insurers. For example, in 2024, 21% of direct and assumed premiums were ceded. In 2025 and through 2027, we expect that key role will continue. What will be noticed, however, is the growing use of sidecars to support life-annuity reinsurance transactions.

Since 2019, over 15 new sidecars have formed. Looking ahead, we believe sidecars will continue to bring more capital to support life and annuity reinsurance growth. This is a strong positive for the life and annuity industry's forecast capital strength and profitability through 2027.

GLP-1s Affect Claims and Pricing

When we think about claims through 2027, the good news is that excess mortality has receded from the COVID-19 pandemic peak. At the same time, an increasing number of retirees use annuities to generate retirement income and increase annuity benefits. However, the impact of GLP-1 drugs on mortality, morbidity, and longevity is a new factor we and insurers need to consider. These drugs hold the potential to affect life insurance underwriting as well as life insurance and annuity pricing.

There is a concern that applicants may be using the drugs when underwritten for new policies, but then later stop using the drugs, leading to a return of weight and/or other health conditions the drugs treat. On the annuity side, longevity improvements due to GLP-1 change longevity expectations and strain original annuity pricing assumptions. The current impact of GLP-1 on life-annuity profitability may not be large. That said, these drugs may have a long-term effect on claims and pricing beyond 2027.

A Forecast for Strategic Adaptation

How should life-annuity executives respond to the good news in this forecast? The next three years give insurers a rare alignment of strong earnings, improving yields, new sources of third-party capital, and a return to more normal mortality. The companies that use this period to adapt and realign strategy, instead of simply enjoying favorable conditions, will be the ones that lead the industry in the decade ahead.

To position for continued growth and success, life-annuity insurers should accelerate product innovation. Developing flexible products that can adapt to shifting longevity and morbidity trends will help address emerging customer needs and market dynamics.

At the same time, enhancing the investment strategy remains essential. Continued diversification of the General Account portfolio, with a focus on private credit, mortgages, and alternative assets, will help optimize yields. Partnering with key asset management partners will be crucial to executing successful diversification strategies.

Leveraging reinsurance and third-party capital is another strategic priority. Expanding the use of reinsurance, including innovative structures like sidecars, can help manage capital efficiently and support growth objectives.

Driving operational excellence is crucial for sustainable growth. Investing in technology and analytics can improve underwriting, claims management, and customer engagement, while streamlining operations will enhance efficiency and scalability.

Finally, strengthening regulatory and capital management practices will help companies stay ahead of evolving requirements, maintain robust capital buffers, and support business expansion in a volatile market environment.

By executing on these strategic priorities, life-annuity insurers can capitalize on favorable market conditions, manage emerging risks, and position themselves for sustainable growth through 2027 and beyond.

KEY TAKEAWAYS:

• The top-down leadership model is losing relevance as future insurance leaders seek collaboration and dynamic career growth
• Emphasizing the industry's purpose-driven mission and community impact helps attract professionals seeking meaningful work
• Affinity insurance, mentorship, and emerging tech roles offer diverse career paths that foster growth and customer engagement in insurance

With the U.S. Bureau of Labor Statistics projecting the loss of nearly 400,000 workers by 2026, the insurance industry is entering a period of significant change. This potential workforce decline underscores the urgent need for insurers to invest in people, culture, and modern career paths that appeal to today's professionals. To attract and retain future insurance leaders, insurers must rethink how they engage talent and structure career development.

Though often seen as traditional and slow to adapt, the industry is actually changing fast. Technology, evolving consumer expectations, and a workforce that values purpose and flexibility are driving this transformation. To bridge the gap between perception and reality, insurance leaders must embrace modern approaches to leadership, career development, and company culture.

To help our industry overcome today's talent challenges, I believe there are four key areas leaders in the industry should focus on.

1. Evolve Leadership Models

The traditional top-down leadership model, where decisions flow from the top, is losing its effectiveness in today's fast-paced environment that emphasizes collaboration and adaptability. Once common, this hierarchical approach no longer aligns with the expectations of younger professionals. According to PwC's 2024 Workforce Radar, this group is seeking environments where their voices are heard, their ideas influence outcomes, and their career paths feel dynamic rather than rigid. They want to be contributors, not just implementers.

Similar research shows that employees who see a bright future at their company are 1.7 times more likely to stay, 2.3 times more engaged, and 2.4 times more likely to recommend their employer. Yet, more than half of Gen Z and Millennial workers hold a negative or neutral view of our industry, often perceiving it as complex and rigid. This growing desire for dynamic workplaces, combined with the industry's negative perception, creates a major challenge in attracting and keeping new talent.

By shifting to collaborative, team-based leadership, we can bring a wider range of perspectives into decision-making, which is crucial for understanding the diversity of today's consumers. Diverse perspectives foster environments where fresh voices enhance market understanding, strengthen decisions, and build greater customer engagement in insurance. This shift also serves as a powerful signal to younger talent that their contributions are not only valued but also essential to our success and future. Collaborative cultures will help forward-thinking insurance leaders appeal to the next generation of talent.

2. Play Up Our Strengths: The Purpose-Driven Core

At its heart, insurance is an industry built on trust and human connection. Our fundamental mission is to help people navigate some of the worst moments of their lives by providing support and financial security. This core mission is a powerful differentiator for attracting talent who seek meaningful, purpose-driven careers.

For many younger professionals, a job is an opportunity to make a tangible, positive impact. Findings from Deloitte suggest that this generation is motivated by a desire to contribute to something bigger than themselves. By highlighting the security and peace of mind we provide for individuals, families, and communities, we show that a career in insurance is deeply meaningful. When corporate culture and leadership embody the industry's thoughtful, service-oriented values, they resonate with employees who want their work to matter and inspire them to bring their best every day.

Emphasizing this core purpose helps attract emerging talent, demonstrating to them that a career in insurance offers meaningful, lasting impact.

3. Highlight Diverse and Emerging Career Paths

While traditional insurance roles like underwriting, claims, and sales remain vital, they're only part of the story. The industry has untapped potential to showcase exciting paths that attract talent seeking innovation, impact, and growth.

Advanced technology, analytics, and digital platforms are redefining the insurance experience from the ground up. New opportunities in AI, cybersecurity, data science, and user experience design open the door to talent that may have once overlooked the industry as a viable career path. By showcasing how these modern disciplines are transforming our industry, we can attract professionals who may have otherwise gravitated toward the tech sector.

Beyond technology, we can highlight unique career paths like those in affinity insurance programs, where professionals work with organizations that reflect their personal interests, such as a university or a professional association. These positions allow individuals to combine their professional skills with their passions, creating highly engaging and fulfilling careers.

Mentorship programs also play a critical role in both recruitment and retention. Mentees gain exposure to cross-functional experiences and broaden their understanding of career possibilities, while mentors gain fresh perspectives on the priorities and aspirations of the next generation. This mutual learning builds stronger connections and encourages long-term engagement.

When insurance leaders effectively highlight diverse career paths and foster mentorship and engagement, emerging talent is more likely to see the industry as a place where they can grow, innovate, and build meaningful careers.

4. Create Genuine Connections

Building and maintaining strong ties with young professionals is essential for long-term success because when employees feel part of a community, they stay engaged and motivated. In addition to nurturing internal relationships, cultivating external networks and partnerships helps new professionals feel welcome, included, and connected to the broader industry community.

Leveraging professional membership organizations is a powerful way to facilitate meaningful connections between young professionals and experienced leaders. These groups provide a vital link between young professionals and experienced leaders, creating opportunities for collaboration and learning. By participating in these networks, new employees can attend special interest groups, join committees, and form relationships that will shape their careers for years to come.

A strong sense of community helps ease the feeling of being an outsider in a new and complex industry. Membership organizations also provide valuable resources and support, especially as we navigate new technologies and evolving market dynamics. In an era defined by change, having a trusted network for shared knowledge and support is more important than ever.

These connections strengthen the industry as a whole. While young professionals gain guidance and insight, established leaders gain access to fresh perspectives and promising talent. Together, these relationships strengthen the industry's future and foster a culture of shared growth and innovation that will help attract, develop, and retain the next generation of insurance leaders.

Shaping the Future of Insurance Careers

The talent gap in the insurance industry is both a challenge and an opportunity shaped by technology, talent trends, and culture. Companies that invest in meaningful connections, diverse career paths, and inclusive environments will not only retain top performers but also build a more engaged workforce. Supporting mentorship and embracing new technologies helps organizations innovate while staying grounded in core industry values. By doing so, they create environments that draw emerging professionals and strengthen the industry for years to come.

How quickly have enterprises across industries embraced AI? The answer depends on your view of its origins. Some people believe AI dates back to Alan Turing's early theories of machine intelligence. Others see the Dartmouth Summer Research Project on AI in 1956 as its birth.

What is unarguable, however, is that AI has moved at breakneck speed since the launch of ChatGPT three years ago. A recent McKinsey survey revealed that 88% of organizations now use AI regularly. Yet there is a downside. Only one-third of respondents have scaled AI enterprise-wide, and fewer than half can tie any significant earnings before interest and taxes (EBIT) to their efforts.

What will it take for insurers to see real ROI from their AI investments? And how can carriers prepare for an agentic AI future? A look at past tech trends can show us quite a bit about what is to come.

Why Every Tech Revolution Stalls Before It Scales

All significant technological breakthroughs of the past 20 to 30 years have followed a distinct progression. First comes the hype, where inflated expectations generate massive excitement and speculation. Next comes normalization, where practical use cases emerge. Lastly, there is substantive change, where technology becomes embedded in core operations, driving genuine business value.

Within each cycle, however, key barriers exist that block progress once the initial hype fades. In the late 1990s, high-speed connectivity was going to reshape business overnight, but ROI did not come until broadband access expanded. Apple's first iPhone launched in 2007 but did not achieve widespread use until its app store and developer ecosystem matured. Cloud computing brought huge promise in the mid-2000s but did not mature fully until operating models shifted toward DevOps, APIs and microservices.

Even insurtech itself went through a metamorphosis. Early disruptors that set out to overturn insurance incumbents about a decade ago have either been acquired or failed to prosper. Those that focused on partnering with incumbents to modernize core parts of the insurance value chain, however, have grown stronger and manifested an industry that drives innovation in insurance forward.

What's Holding AI Back?

There clearly is no shortage of excitement or action when it comes to implementing AI in insurance. Seventy-six percent of insurers already use generative AI in at least one core business function, making insurance the second-fastest industry to AI adoption, trailing only tech, according to BCG research.

Yet true transformation value remains rather elusive. Despite the breakneck speed of adoption, the results of AI pilots are uneven, and progress is patchy. Only 6% of respondents to McKinsey's survey are classified as high performers. That means they set targets beyond efficiency, embedding AI in existing operating models and new products to capture more revenue and expand cross-selling opportunities.

These high performers have already moved beyond generative AI and toward agentic AI, which involves using AI-powered agents to plan and execute multi-step work. Sixty-two percent of McKinsey respondents are already experimenting with agentic AI, and 23% are scaling agents somewhere in the enterprise. Here again, however, penetration is narrow, with agentic deployments living in one or two functions.

So, what's holding insurers back from ROI with generative and agentic AI? One common thread is deployments that are saddled by the limitations of legacy and mainframe systems. Put simply, insurers cannot reach an AI-enabled future if their workflows are dependent upon decades-old technology that is inflexible and impossible to integrate.

Breaking Through the Barrier

To deliver meaningful returns from their AI bets, carriers must rethink the foundations supporting their technology ecosystems. Many of today's challenges stem from years of product-centric digital transformation that delivered incremental wins but left insurers with a patchwork of disconnected systems. These solutions were never designed for the real-time data or observability that AI and agentic AI require.

A platform-centric approach offers a practical path forward. Unlike point solutions that focus on single steps in the value chain, modern platforms provide the connective tissue that allows data, workflows, models and controls to operate together. Platforms succeed because they allow insurers to redesign workflows holistically; something point solutions were never built to support.

The most effective platforms in the coming three to five years will share several attributes.

• Flexible, API-driven architectures that allow carriers to integrate new technologies into existing systems without a costly rip-and-replace.
• A broad ecosystem of partners and integrations, giving carriers access to specialized tools, such as distribution and fraud detection, without adding further fragmentation.
• AI-enablement at the foundation, meaning agents and models can operate safely and consistently across underwriting, claims, servicing, finance and product development.
• Built-in governance and human-in-the-loop patterns that build transparency and trust as insurers ask AI to take on more complex work.
• Outcome dashboards that provide clear metrics, including cycle time, loss adjustment expense, bind ratio, Net Promoter Score, and revenue uplift per use case.

With a platform-centric mindset, insurers can begin building momentum with their existing AI investments and gradually move toward an agentic future. A smart approach is to embed AI agents into crucial workflows where it can consolidate data, reason about actions, and hand off smoothly to humans, such as quote triage in commercial lines or claims final notice of loss (FNOL) to settlement recommendations. Start small, measure the impact, and then implement AI agents into other projects to make adoption stick.

Navigating the Road Ahead

From the dot-com boom to the rise of AI, history shows us reinvention in insurance happens over time, not overnight. AI is moving faster than any wave before it, but the core principles for insurtech companies and carriers alike remain the same: build and implement platforms that solve real business problems and engender trust with customers along the way. This type of measured approach will propel the next generation of AI-powered insurtech platforms and help accelerate the AI adoption cycle.

Four years ago, days after Mark Zuckerberg debuted the Metaverse, I wrote a Six Things commentary that began: "The vision of a metaverse laid out by Mark Zuckerberg last week is bonkers. Nutso on steroids. It won't be realized in my lifetime, yours or his, even if some of the wildest claims about longevity come true and we all live to be 150."

Since then, the Metaverse group within the company Zuckerberg renamed after what I referred to in that commentary as "a fever dream for gamers" has racked up $70 billion in losses, and Bloomberg and the New York Times reported last week that he is planning to cut staff by between 10% and 30%, possibly in January.

So, in retrospect, I'm just sorry I pulled my punches. :)

Trashing the Metaverse on Day One was not a remotely hard call, because it violated one of the cardinal rules of innovation: As much as possible, an innovation has to fit within the existing work environment or lifestyle of the prospective user. Yet the Metaverse required radical changes in how individuals interact — with, as far as I could discern, no appreciable benefits.

It's worth taking a minute to look at where Meta went wrong, because the mistake is awfully tempting for all of us. 

The Metaverse assumes that people want to live online a huge percentage of the time. You have to produce an avatar to act as you and learn all sorts of new behaviors to interact with other avatars and with everything else that populates the online world. (I tried this a couple of years into the Metaverse experiment, courtesy of a consulting firm that was enthusiastic about its prospects, and it was still quite hard just to maneuver, let alone to talk with others' avatars or to conduct a transaction.) 

The rule of thumb in Silicon Valley is that an innovation has to be 10 times better than anything it is intended to replace, yet the Metaverse was far less useful than the Zoom calls and other technologies we already used, while requiring huge changes in people's routines. 

Apple made a similar mistake with its Vision Pro virtual reality device — and yes, I trashed that, too, right after it was announced at the beginning of last year. I wrote: "There's simply no reason to strap a 1 1/2-pound device to your face (nearly the weight of a quart of milk) and put a three-quarter-pound battery in your back pocket so you can type with your two index fingers in mid-air while strangers or officemates gawk at you. Not when some combination of today's laptops, tablets and phones will do just fine."

The Vision Pro has been a dud for precisely the same reasons the Metaverse has flopped. 

By contrast, Metaverse has a budding hit with the AI it has built into Ray-Ban "smart display" sunglasses. The capabilities are still pretty limited but are enough to get started: You can use voice commands to snap photos, record videos, send messages, make calls, and ask questions of Meta's AI. And Meta isn't asking customers to do anything out of the ordinary. Just about everybody wears sunglasses. Besides, Ray-Bans look cool.

When you look at the history of major technology innovations, they almost all replace something similar. Smartphones replaced iPods, which replaced the Walkman, which replaced transistor radios. Smartphones also replaced early cellular phones, which replaced hardwired phones in homes. There was almost no need for changes in behavior; everything just became easier and better.

Note that once you get a new device into people's lives, like a smartphone, you can start to get them to change behaviors that have nothing to do with the original purpose — when I first saw a smartphone demo, some 25 years ago, I had no idea I'd be doing my banking and shopping on a phone, or listening to podcasts on it and having it monitor my driving.

The insurance industry seems to mostly get this principle, that innovation has to fit into existing behaviors. That's why we're seeing so many dashboards that incorporate the advances in generative AI, gathering information and making evaluations in the background and presenting them to underwriters, claims professionals or agents and brokers as part of their normal workflow. I think chatbots were initially seen too much as a standalone technology but are now being integrated much better into the customer experience.  Whisker Labs' Ting device has taken off because a customer simply has to plug it into a wall socket to have it monitor for electrical issues and prevent home fires. Roost built another Predict & Prevent business by offering batteries that can be plugged into existing smoke detectors and ping a customer's cellphone when an alarm sounds, in case they aren't at home to hear it.

Still, the principle is worth keeping in mind, because the temptation — which I've witnessed across industries in my decades of writing about innovation — is to think that what you're doing is so useful that people will adapt to you, freeing you from worrying about how to adapt to them. 

If Meta and Apple can make that mistake, you can, too.

Cheers, 

Paul

P.S. While I've patted myself on the back for dumping on the Metaverse and Vision Pro right out of the gate, I need to acknowledge that I've made mistakes, too. While I can't recall a time when I savaged an idea or product and been wrong, I've certainly been too optimistic about how quickly change would happen. I try to live by the Silicon Valley dictum that "you should never confuse a clear view for a short distance," yet, well, I sometimes do.

For instance, I wrote an article in 1991 or 1992 that said paper forms no longer had a reason to live, given that we could all input information into personal computers connected to whomever or whatever needed the data. That was more than three decades ago, and, hmmmm....

But at least the article only ran on the front page of the second section of the Wall Street Journal, so only a few people read it, right?