In the timeless words of Sun Tzu in The Art of War: "If you know the enemy and know yourself, you need not fear the result of a hundred battles." Today, in the battle against fraud in business and government programs, entity resolution—combined with knowledge graphs and geospatial analytics—serves as that ultimate weapon, akin to Excalibur, the legendary magical sword that could cut through anything.

When it comes to fighting fraud, it cuts through layers of deception, revealing hidden connections between people, businesses, transactions, and locations that fraudsters purposefully endeavor to keep obscured. By mapping out entities and resolving disparate records across dispersed systems to the real individuals and organizations behind them, investigators gain the clarity to validate transactions, expose invalid transactions, and dismantle fraudulent networks.

Fraud in government programs and business operations thrives in the shadows of fragmented data: mismatched names, shell companies, fake addresses, synthetic identities, and manipulated locations. Without a unified view, billions of dollars are lost annually to schemes like improper benefit claims, procurement kickbacks, subsidy abuse, "paper mills," and phantom vendor payments.

Entity resolution bridges these gaps, linking records across databases—names and addresses, tax filings, business registries, transaction logs, social media, and public records—to create a "360-degree" profile of every entity involved.

Entity Superpower — Unmasking the True Actors

At its heart, entity resolution determines when multiple records refer to the same real-world person, business, or location, despite variations in spelling, abbreviations, typos, or deliberate obfuscation. Advanced algorithms and machine learning handle the noise: "John A. Smith LLC" might resolve to the same entity as "JAS Enterprises" owned by "Jon Smith," especially when tied to shared addresses, phone numbers, or transaction patterns.

When integrated into knowledge graphs, these resolved entities form connected networks of relationships—ownership links, family ties, shared board members, or transaction flows. Adding the basics of address geocoding and geospatial analytics overlays physical reality: mapping addresses, proximity of claimed locations, or clustering of suspicious activities in specific regions. This data fusion transforms isolated data points into a battlefield looking glass that maps where fraud patterns emerge clearly.

Consider a classic red flag in government-funded programs: more licensed or funded daycares than the number of children in an area could possibly require. Entity resolution uncovers this by resolving provider records to actual owners and cross-referencing enrollment claims against demographic data. Knowledge graphs reveal networks of colluding owners registering multiple entities at the same address or funneling funds through connected shell companies. Geospatial views highlight unnatural concentrations—clusters of daycares in low-population rural zones or urban blocks with improbable child-to-provider ratios—signaling potential ghost operations or subsidy farming.

So, as with childcare, insurance companies may apply entity resolution to chiropractors, MRI facilities, and clinics, but in addition now the named insured, agent, claimant, and adjuster meld in with medical providers, equipment, legal staff, vendors, and others in the graph across any line of business. As lines are combined and companies join forces, this process can literally map trillions of dollars of historical premiums and claims that could influence real-time payments.

The King's Sword Trumps All Use Cases

Drawing from innovative applications across business and government using knowledge graphs for fraud detection, the combination of entity resolution, knowledge graphs, and geospatial tools exposes fraud across diverse domains:

• Government Benefit and Subsidy Fraud: In childcare subsidies, housing assistance, unemployment benefits, or agricultural grants, resolved entities expose operators claiming impossibly high volumes. Geospatial analysis flags unnatural provider distributions relative to demographics, while knowledge graphs uncover collusive networks funneling funds through connected shells or using stolen identities for enrollment claims.
• Procurement and Contract Fraud: Vendors often conceal conflicts via layered ownership or bid-rigging. Entity resolution connects bidders to officials' associates or hidden entities; geospatial overlays reveal fictitious delivery sites or illogical routing; graphs detect circular payments or anomalous bidding patterns indicative of corruption.
• Fake Business and Identity Schemes: Fraud rings create phantom companies for loans, grants, tax credits, or PPP-style programs. Resolution merges digital and physical footprints—such as mismatched websites/IPs with abandoned addresses—while geospatial clustering pinpoints registration hotspots tied to broader scams.
• Money Laundering and Illicit Flows: In trade-based or benefit-related schemes, resolved entities link actors across jurisdictions. Knowledge graphs map multi-hop transaction chains; geospatial tools visualize fund movements against claimed origins, exposing laundering through high-risk locations or mismatched geographies.
• Insurance Claims Fraud: In property insurance schemes, fraudsters stage incidents like water damage during homeowners' vacations, directing repairs to complicit restoration providers. Entity resolution links claimants, properties, and service providers across cases, revealing common identities or ownership ties; knowledge graphs highlight recurring patterns in damage types, timing, and vendor involvement; geospatial analytics maps claim locations against provider clusters, unmasking organized rings exploiting insureds and property owners.

In auto insurance, staged accidents generate multiple unrelated passengers all seeking medical treatment from the same provider and being represented by the same lawyer even though they themselves may live far apart and curiously are frequently unable to be located.

The schemes for various lines of casualty and property in auto, home, workers' compensation, and commercial insurance all are well mapped by the NICB (National Insurance Crime Bureau). And new schemes are emerging all the time — especially with the backing of transnational criminal organizations, but also with just everyday people getting creative with generative AI.

En Garde — the Industry Keeps Its Hand on the Hilt

As fraud schemes grow more sophisticated with digital mapping tools and global reach, entity resolution in knowledge graphs—enhanced by geospatial context—will only sharpen. Real-time monitoring, AI-driven anomaly detection, and dynamic mapping will make deception harder to sustain. The result? Interdiction of transactions. Faster and better recoveries. Frustrated, if not deterred, criminals. Lower premiums for insureds. Safeguarded public funds.

In the war on fraud, knowledge is power—but resolved, connected, and spatially aware knowledge is the key to victory. Like Excalibur drawn from the stone, we across these industries, companies, and public bodies draw data from our legacy and modern systems. This combination of data and technology empowers those who wield it to cut through illusion and restore justice.

Paul Carroll

AI is everywhere in insurance right now. Where do you see it being used especially well?

Dr. Michael Bewley

One mature application is the use of something called supervised machine learning, for aerial imagery. The application provides a way of getting reliable recognition of objects and images, which can be really informative about a property. Then you can use what you see in trusted frameworks. You know, given the roof had large patches of rusting or missing shingles or a hole in it before the event, what's the likelihood of damage in the event? That can be modeled in a pretty clean way.

But there's a whole spectrum of AI from really quantifiable, reliable, and well-understood systems all the way through to things where it's all about creativity. You throw in an idea, and it comes up with some more ideas. 

Even traditional risk modeling can be seen as AI. You're trying to predict the likelihood of claims.

Paul Carroll

What are the risks associated with using AI?

Dr. Michael Bewley

You've got to get the newly available data and realize it's amazing but then apply it carefully, because all data comes with uncertainty. Even if we're really confident it's a solar panel on that roof, we'll tell customers we're 98% confident. There's a 2% chance we're wrong, and saying so allows insurers to treat the data in a more nuanced way.

Paul Carroll

There's growing pushback against AI-based property assessments. People are told they have a roof problem through AI and aerial surveillance, and while they may acknowledge the issue is real, they resist being charged based on that information. How do you address this customer trust challenge?

Dr. Michael Bewley

That we can determine a roof's condition remotely is really valuable—not just to the insurer, but to the insured. Not many people climb on their roof on a regular basis. The fact that we can not only say there's an issue with that roof, but we can show the image it comes from is really important.

If we can tell someone their roof is damaged, they can fix it. They can reduce their risk, and that's in everyone's interest.

Paul Carroll

Organizations are dealing with information in countless different forms—one insurance system had 37 ways that San Francisco was described, from San Francisco to San Fran, SF, Frisco and so on. Is this data uncertainty part of the reason property-related decisions are still so difficult to make?

Dr. Michael Bewley

Just having so much more data today doesn't necessarily make for good decisions in and of itself. There are so many questionable sources of information out there, and there are so many sources where it's unclear how accurate they are because you can't actually see the provenance. It's very difficult to ascribe a level of trust.

This is why we've hinged our whole strategy on aerial imagery. We bring in third-party data and other information, but the core is what your eyes can see.

Insurers are being bombarded by a huge range of information from different vendors and open information out there on the Web. So we're very particular about how we form our information, and we make that transparent to the user. Every bit of data that we serve up in our APIs comes with a link so you can go and look at the photo. 

It's well-articulated information that matters. Volume can actually be a detractor because you get lost in the noise.

Paul Carroll

The insurance industry has historically moved slowly, but in catastrophe response, speed is critical. Where do we speed things up?

Dr. Michael Bewley

The challenge is that a catastrophe is a continually unraveling scenario. It's not just that the cat event occurs, then we're done, and we all move on. The hurricane makes landfall, properties get damaged, the storm keeps moving, further events occur, there are recovery efforts, and so on. So while speed is good, clarity is important, as well. 

If there's an event that we're going to capture with our cameras, we'll get a plane up in the air as soon as it's safe. As soon as we capture some valid imagery, we turn it around as fast as we can, using AI. In Hurricane Milton, I think we flew over 100 flights because there were so many things going on—the weather changes, what's going on on the ground changes.

Paul Carroll

Would you talk a bit more about how insurance can move from the traditional repair-and-replace model to a Predict & Prevent approach?

Dr. Michael Bewley

That's a great question. If we step back from the catastrophe-specific discussion, our regular capture program covers most well-populated areas multiple times a year. We’ve done this for a decade now in the U.S. and 18 years in Australia.

The regular uptake of imagery, year in, year out, shows you where things are today and where they've been historically, and then captures an event in that context. A really good example is our new roof edge product. We've run AI on stupendous quantities of imagery. We've looked at our full imagery archive in the U.S. and run every single house on every single historical date to work out when a new roof got put in. If an event is coming up, you can start to feed that into an understanding of whether the roof is getting to end of life anyway, so maybe it's time to replace it. Maybe that reduces the risk. You can have a mature discussion between the insured and the insurer about that. 

The exact same imagery is being used by insurers, by local governments, by construction, by town planning, by environmental groups, by so many different sorts of people. So they can have discussions about how to remediate the risks on a property before an event happens. We can talk about how we plan towns better. It's wonderful if we can all look at that same source of truth.

Paul Carroll 

What is one challenge you'd like to offer to insurers about their assumptions on property risk? What are they missing that they should understand?

Dr. Michael Bewley

I think the challenge is really for them to understand that there are new. high-quality sources of information available. They may be used to doing things a certain way with limited information, so they have to understand the incoming information and make good use of it.

In the AI space, the challenge is sifting the signal from the noise. There is genuinely a bunch of AI stuff, particularly the stuff that's in the media a lot, that one needs to treat very carefully. All the large language models and Gen AI imagery stuff—there is a place for that in insurance, but it's different from the more tried-and-tested machine learning approaches, and we have to weave that in carefully. It's very important to understand the full tapestry of AI solutions that there are and not to get them muddled up. 

Gen AI opened up a new world. It is absolutely revolutionary. I think it's on the level of the internet being invented or the personal computer. So you definitely don't want to sit by and say, "Well, I'll wait and see what happens," or "This one's not for me." You've got to get involved.

But as with the personal computer and the internet coming online, there's uncertainty about how to use it. There's uncertainty about what the impact will be. You just have to get in there and get involved. But you have to do it with wisdom and care.

Paul Carroll

Yeah, I think we've just scratched the surface. This is quite a ride we're on.

About Dr. Michael Bewley

Dr. Michael Bewley’s passion for AI began in 2007. Graduating with degrees in electrical engineering and physics (University of Sydney), he received the University Medal for using machine learning (ML) on brain scans to detect Alzheimer’s disease. He joined Cochlear to work on implantable hearing solutions, also implementing its first customer-use product analytics.  A sea-change led to a PhD program at the Australian Centre for Field Robotics, using ML to interpret sea-floor imagery from autonomous submersible surveys. He also established a data science team as Lead Data Scientist at the Commonwealth Bank.  Mike joined Nearmap in 2017 and is now VP of AI & Computer Vision, leading the development of AI technology, applying petabyte-scale deep learning on geospatial imagery and AI data sets.

For years, even decades, senior leaders in the insurance industry have pursued the goal of fully digitized claims operations. The business case was especially strong for straightforward property and casualty claims, where high volumes and repeatable patterns made automation attractive. Still, carriers across all lines of business saw the potential benefits of streamlining workflows. The logic was simple. If insurers could automatically capture the right data, use claims processing automation to handle routine steps, and speed payouts, operating costs would decline, and customer satisfaction would improve.

Today, for many insurers, that vision is no longer theoretical. With the help of claim management automation solutions, routine claims can now move through the system with limited manual intervention. Costs have come down, timelines have shortened, and straightforward claims are often resolved faster than ever before.

But this progress raises a new question. Now that efficiency has improved, what comes next?

Why This Conversation Matters Now

For many years, claims transformation was defined by speed. Insurers focused on faster first notice of loss, assessment, adjudication, and payout. Speed became the main indicator of progress.

Speed still matters. Delays create financial strain for customers and reputational strain for insurers. But in 2026, speed alone is no longer sufficient.

1. The Need for Fairness and Defensibility

Insurance companies promise more than financial payment. They promise fair treatment. When a customer files a claim, they are often stressed or confused. In that moment, how the claim is handled matters as much as the final settlement. A delayed response, unclear explanation, inconsistent decision, or weak documentation can quickly escalate into a bad-faith allegation. Once that happens, legal costs rise, and reputational damage follows.

This is where automated claims processing insurance platforms are gaining attention. Beyond efficiency, they establish clearer documentation and consistent workflows.

They also create traceable decision pathways with well-articulated audit trails. Such a lucid and transparent structure enables insurers to demonstrate that claims were handled judiciously and in good faith.

2. Rising Complexity and Fraud

Claims complexity is also increasing. CAT events are more frequent and destructive. Fraud schemes are more coordinated. Regulatory oversight is more exacting. Each decision may be reviewed months or even years later.

Fraud alone presents enormous pressure. Deloitte estimates suggest that roughly 10% of property and casualty claims are fraudulent, contributing to approximately $122 billion in annual losses. Deloitte also projects that by implementing AI-driven technologies across the claims life cycle and integrating real-time analysis from multiple data sources, P&C insurers could reduce fraudulent claims and save between $80 billion and $160 billion by 2032.

Modern insurance claims automation solutions help detect suspicious patterns at an early stage. High-risk claims are then routed for deeper scrutiny. This enables insurers to mitigate fraudulent activity. It also shields legitimate policyholders from the downstream repercussions of deceit.

3. Changing Risk Profiles Due to Workforce Strain

While claims complexity rises, the workforce is under strain. Many experienced claims professionals have retired. Institutional memory has thinned. Newer adjusters manage heavy caseloads with less experience. This creates uneven judgment and operational fragility.

With more advanced, affordable AI-based claims management automation solutions available, insurers have an opportunity to rethink the role of claims altogether. Instead of viewing claims purely as a cost center, forward-looking carriers are exploring how smarter, data-driven claims operations can create value. This includes improving loss ratios through better fraud detection and prevention, offering more personalized claims experiences, and even using insights from claims data to reduce future losses.

Automation and algorithmic decision-making are now common. Systems evaluate, approve, flag, and sometimes deny claims with limited human involvement. These tools increase efficiency. They also raise questions about accountability, bias, and explainability.

The central question has shifted. The industry must now ask not how fast claims can move, but how intelligently they can be handled.

The New Era of Claims Management

The future of claims processing is not about moving faster through workflows. It is about making better decisions at every step. Here are the core characteristics of the future of claims:

I. Balance

Smarter claims processing balances speed with accuracy. It balances automation with human judgment. It also balances efficiency with trust.

II. Fairness

A claim processed quickly but incorrectly creates rework, complaints, and litigation. An automated claim without context can harm a vulnerable customer. A decision issued without a clear rationale can invite regulatory scrutiny. The resulting fairness and transparency instills greater trust in the insurer-insured relationship.

III. Quality

Claims performance must be evaluated through decision quality. Cycle time and cost remain important, but they are incomplete measures. A high-quality decision is consistent, fair, traceable, and defensible.

Modern claims processing solutions should therefore be judged not only by how quickly files move, but by how reliably they withstand complaints, audits, and disputes.

Why Speed-First Models Are Breaking Down

Speed-first models were built for a different era. They assumed predictable claims, stable risk patterns, and clean data at intake. That environment no longer exists.

• Built for a Simpler Environment

Speed-first claims models were built for predictability. They assumed standard patterns and limited variation. Claims were treated like transactions moving down a straight pipeline.

That assumption no longer holds.

Today's claims are more varied. Policies are more complex. Weather-related losses are larger and less predictable. Fraud tactics are more organized. What once worked for routine cases now struggles under real-world pressure.

• Weak Intake Leads to Faster Mistakes

When intake data is incomplete and claims processing automation pushes the file forward anyway, errors spread quickly. Missing documents, incorrect coding, or misread policy terms can move through the system without being caught.

Automated claims processing insurance systems do not fix weak inputs on their own. They can magnify them. An improper denial can move just as quickly as a correct approval. When that happens, complaints rise. Rework increases. Legal risk grows.

Strong claims processing solutions must therefore focus on data accuracy at the start, not just speed at the end.

• Over-Automation Reduces Judgment

Over-automation also creates rigidity. Rule-driven systems work well for simple claims. A broken windshield or minor water leak may follow a clear path.

But many claims are not simple. A severe storm loss, a multi-party liability dispute, or a policyholder in financial distress requires context. It requires judgment. Claim management automation solutions should guide these cases, not force them into narrow rules.

Insurance claims automation solutions must be able to flag unusual patterns and route them for review. If everything is treated the same, fairness suffers.

• Explainability and Trust Are at Risk

Explainability is another weakness of speed-first models. A rapid decision without a clear explanation erodes trust. Customers may feel ignored. Regulators may question whether similar cases are handled the same way. Leaders may struggle to defend outcomes during audits.

Clear documentation matters. Claims processing automation should record what was reviewed, what rules were applied, and why a decision was made. Without that record, even a correct decision looks careless.

•  Automation Without Intelligence

When it comes to claims processing, the problem is not automation itself. Claims processing automation can reduce manual errors and improve consistency. Automated claims processing insurance systems can shorten timelines and improve service.

The problem is automation without thought. It offers speed without review, and establishes rules without room for context.

The next stage of claims modernization must combine structure with judgment. Automation should support sound decisions, not replace them.

What Smarter Claims Really Means

Smarter claims processing has a practical definition. It means using technology to support sound judgment rather than replace it.

AI-driven automated claims processing systems can:

• Extract and verify data from documents
• Compare claim details against policy terms
• Detect fraud patterns across large datasets
• Prioritize claims by complexity and risk
• Route sensitive cases for human review
• Provide clear documentation of every step taken

This does not eliminate the insurer's legal duty to act reasonably. It helps fulfill that duty more consistently.

Smarter claims automation systems integrate  integrate policy data, claimant history, prior outcomes, and external signals before guiding decisions. Straightforward claims move quickly. Complex or high-risk claims receive deeper review.

Learning is embedded in the system. Complaints, reversals, litigation outcomes, and regulatory findings feed back into decision support models. Over time, the system becomes more refined and less erratic.

Even modest improvements matter. Best-in-class insurers applying AI in specific domains have already achieved measurable results, including a 3% to 5% improvement in claims accuracy. That may seem small, but at scale it can mean thousands fewer disputes.

The Shift from Workflow Engines to Decision Engines

Traditional claims platforms functioned as workflow engines. They moved files from one predefined step to the next. The focus was on process efficiency.

Modern claims capabilities are evolving into decision engines.

Instead of simply pushing tasks forward, decision engines evaluate context and risk in real time. They determine whether a claim should be automated, referred, or escalated. They assess gradients of complexity rather than forcing uniform treatment.

In a workflow model, success is defined by movement. However, in a decision model, success is defined by the integrity of the outcome.

This structural shift strengthens defensibility when decisions are later challenged.

How Trust Has Become the New KPI

As automation deepens, trust becomes central.

For starters, customers want to understand why their claim was approved, adjusted, or denied. As such, transparency is no longer optional.

On the other hand, regulators expect traceability. They want audit trails that show how data flowed through systems and how conclusions were reached.

Finally, executives expect risk control. They want assurance that automation does not introduce hidden bias or unpredictable exposure.

Trust can be measured through:

• Lower complaint volumes
• Fewer bad-faith allegations
• Reduced litigation frequency
• Consistent audit outcomes

Smarter claims systems embed traceability and governance into the decision path itself. They generate documentation in real time rather than reconstructing it after disputes arise.

It is vital to note that trust is not built on speed. It is built on clarity and consistency.

What CIOs Need to Focus on Now

For CIOs, smarter claims processing is not just a technology upgrade. It is a capability shift. Here's what they should focus on:

• Claims should be treated as a decision system. Investments must support contextual insight, structured judgment, and adaptive routing.
• Data quality must be strengthened at intake. Weak upstream data produces fragile downstream outcomes.
• Human oversight needs to be intentional. It cannot be perfunctory or symbolic. Thresholds for escalation must be clearly defined. Mechanisms for override and structured pathways for review should remain controlled and unambiguous.
• Governance is not optional. It is foundational. Explainability, audit trails, and bias monitoring cannot be treated as incidental add-ons or postscript considerations. They must be embedded from the outset.
• Metrics need constant recalibration. Static scorecards will not suffice. Beyond cycle time and cost efficiency, insurers should track decision consistency and complaint frequency. They must also monitor litigation exposure and fraud-detection efficacy with greater granularity.

All in all, claims modernization is not about acceleration alone. It is about discernment and prudent judgment. Speed matters, of course. But sagacity matters more.

The Bottom Line

Insurance companies promise fair treatment, not just fast payment. In a volatile and heavily scrutinized environment, that promise must be defensible and demonstrable.

The future of claims will continue to value efficiency. Its defining attribute, however, will be intelligence and calibrated reasoning.

Insurers that prioritize decision quality alongside speed will be better positioned for long-term resilience. They will reduce bad-faith exposure and manage fraud risk with greater dexterity. They will also sustain regulatory confidence and preserve customer trust.

The next phase of transformation will hinge on responsible claims stewardship. Ethical automation, explicit oversight, and equitable decision-making will be indispensable. Insurers that combine claims processing automation with transparency and robust governance will not merely control costs. They will fortify customer trust and cultivate enduring loyalty.

AI use by insurance carriers will eventually become ubiquitous – or at least that's the prevalent hypothesis as AI development continues in earnest. Indeed, AI-native operating models will be a necessity to fully embrace AI's potential within the insurance landscape.

But we do not need to get to a fully automated environment to identify a significant opportunity for insurance carriers.

AI patents represent the next great competitive frontier for carriers, and most carriers are completely unprepared for what comes next.

Consider that AI patents are mostly filed by a handful of carriers, predominantly in the P&C space. But an estimate is that just three carriers have filed for 77% of all patents. That is a significant concentration of assets among a small number of carriers.

What does that mean for insurance carriers?

Immediate Implications

For insurance carriers, AI investment is likely driven by at least one of three considerations:

1. Reducing cost
2. Building on an existing strength
3. Addressing a deficiency or weakness

AI investment and development is too early-stage to truly address the third item. Lack of data, an inability to successfully drive adoption, and limited resources would not reward carriers for placing initial AI bets on areas where they are weak. For example, an insurance carrier with 30-day underwriting cycle times is not likely to invest in AI in this space. Instead, they will either focus on process improvements or leveraging an out-of-box solution that can instantly reduce 30-day cycle times into 10-day cycle times.

That leaves cost reduction or developing strengths as the primary motivation for AI investment.

In either situation, the development of a successful AI tool and its inevitable patent is defensive. This means it will help to develop a moat that keeps other insurance carriers at bay.

That may seem intuitive, but the concentration of AI patents among a few carriers tells a different story. Either insurance carriers have not achieved AI results strong enough to justify patents, or the industry has chosen to pursue trade secrets to protect its intellectual property. The trade secret route is unlikely – there is too much movement within the industry, and independent development of AI tools is an inevitability.

This lack of strategy is problematic for carriers – it will only widen the gap between performers if unaddressed.

Long-Term Implications

In the long run, carriers that possess AI patents will inevitably focus first on their strengths to solidify their market position. If a carrier already has strong underwriting discipline and cycle times, leveraging AI will only improve that strength within the market. To be sure, some level of trade secret and proprietary knowledge will make the AI tool more successful for one carrier over another, but a patentable AI tool provides strong defensive capabilities to the insurance carrier.

Imagine an annuity carrier that develops an AI tool that automatically reviews new business applications for annuity exchanges that involve an income rider – typically, this would trigger some enhanced, manual review. If instead an AI tool is designed that performs this specific function and a patent were issued on it, the carrier now has a unique position. Not only can it perform this well, but it can effectively block others from being able to do the same thing.

Now as patent lawyers will tell you, there are ways around this – a carrier could create a different process from the initial patent. But the importance is not that there are other ways to achieve it; it is that one pathway has been closed. And as carriers continue to invest in AI and develop AI-native processes, you could significantly increase the cost of doing business for competitors.

Factor in that the carriers filing patents are the strongest carriers, and strengths are enhancing strengths to create chasms between these carriers and their competitors.

But the value of patent development is not just defensive – it can also be an offensive tool.

A Hidden Financial Goldmine

Carriers should not just look at their patents as ways to protect themselves. While the value of patents may first be their protective nature, there is a significant opportunity for carriers to potentially monetize patents by licensing them to competitors.

Consider that in some instances, carriers have out-innovated insurtech firms. This has spurred insurtech from being a competitor to legacy carriers to being a partner.

Developing patents could be the evolution of this relationship where carriers incubate technical solutions, apply them internally, patent them, and then seek to commercialize them.

Will every patent follow this model? No. In fact, a defensive strategy should be the primary consideration to ensure that an insurance carrier maintains a competitive market position.

But in certain circumstances, owning the right technology is only half of the equation. Consider a carrier that has developed a lead-generation AI tool and can successfully patent and defend it. That tool will only be as useful as the data that is provided to it.

The licensing carrier could license the technology to another carrier (Carrier B), knowing that Carrier B does not have access to the same level of data as the licensing carrier.

The result? Carrier B can obtain significant gains, but not as significant as the licensing carrier will see. We see this today with lead generation models, where generic data still provides a significant lift in cross-selling and up-selling efforts, but not as strong as models that leverage proprietary data. But for Carrier B, who may be a laggard in the lead generation area, they have an opportunity to significantly improve their capability's maturity. This coopetition model allows all carriers to compete but provides the lion's share of rewards to the most innovative carrier.

What Carriers Need To Do To Unlock This Value

Insurance carriers that understand the value of these patents need to take concrete steps to be leaders in this space.

1. Identify Strengths: Patents require disclosure of the underlying model. The best patents will be areas where simply having the AI tool is not enough to win. Areas where the insurance carrier has operational expertise, unique data, or capabilities that cannot be easily replicated are good candidates for patents. This ensures that a carrier protects its competitive advantage while also having the capability to leverage its technology for monetization.

2. Develop A Patent Strategy: Not everything should be patented – some things may be internal trade secrets or rely on other protections. And most importantly, not everything can be patented. Insurance carriers should form teams that combine internal and external counsel, operational expertise, and technical leaders to evaluate which options are most likely to be legally defensible and impactful to the organization.

3. Design Commercialization Capabilities: Just as important to the patent strategy is the ability to commercialize the technology itself. Carriers need two capabilities. The first is the ability to implement the technology itself within the carrier successfully and recognize a benefit. That provides proof of concept and reaffirms what makes the tool successful. The second is to develop an incubator that can pursue partnerships with other carriers akin to how insurtech works with legacy carriers.

4. Adopt An Offensive Posture: Insurance carriers need to be aggressive with reviewing the patent landscape. When insurance carriers file patents, it provides a clear perspective on where other carriers are placing their bets. For example, a large number of patents on claims payments probably means a carrier believes they can differentiate in their claims experience. Carriers should review patent filings and prepare to be litigious, particularly mid-sized and smaller carriers. Insurance carriers cannot allow the competition to simply move unimpeded. If they do, they risk being pushed out of the competition.

AI patents provide a significant opportunity for insurance carriers to achieve strong defensive positions, with the potential for monetization in the future. But most importantly, as insurance carriers transition to AI-native operating models, controlling patents secures competitive positioning while successfully blocking others. The carriers that are able to develop the most effective strategies and execute will place themselves in a strong position as carriers begin operating in AI-native environments.

My auto insurer, Progressive, knows when I (rarely) hit the brakes hard. But why just use that information to determine my premium? Why not amalgamate data on hard braking and provide it to the people who design and maintain roads? 

If I'm the only one hitting the brakes hard at a specific spot, that's a me problem, but if loads of people are doing the same at the same spot, that's a systemic problem that some government agency can and should fix, heading off accidents.

A recent report makes the case persuasively and, I hope, will lead to the amalgamation and sharing of near-crash information by insurers. Doing so could save a lot of lives and avoid a lot injuries and property damage. 

Two researchers at Google looked at 10 years of public crash data, compared it against aggregated data on hard braking and found an extremely high correlation. They then used the hard braking data, separate from the crash reports, to predict trouble spots and again had very strong results. For instance, the intersection of Highway 101 in California with Interstate 880 in San Jose was in the top 1% of all road segments for hard braking — and police reports show a crash every six weeks, on average, for decades.

Google is making its data available to transportation agencies — and they should use it. While the U.S. has traditionally viewed auto safety as the responsibility of the individual driving the vehicle, European countries have shown the importance of system design. 

Using features such as roundabouts, protected bike lanes, lower speed limits and narrower lanes (which prompt drivers to go more slowly), European countries have far fewer traffic deaths per capita than the U.S. does. For instance, the U.K. reports 2.6 traffic deaths per 100,000 people per year; France, 4.9; Germany, 3.3; Spain, 3.7; and Italy, 5.3; while the U.S. reports 14.2.

The U.S. has such a car culture, including a love for pickups and massive SUVs, so I'm not sure U.S. roads will ever be as safe as those in Europe. But using hard braking, rather than police reports, provides information rapidly and overcomes the inconsistencies that arise because different police agencies handle traffic reports differently. The telematics also can extend the use of data to roads that, unlike the 101/880 interchange, aren't so heavily traveled and aren't such obvious outliers — simply because of randomness, an accident may not happen for a long time in a less-traveled spot, but hard braking can still alert authorities that a big problem exists. In addition, the telematics data can be more precise — you don't just notice that accidents happen in a certain spot but can see that hard braking picks up at a certain time of day, in certain weather or at a certain time of year.

In general, I wish the insurance industry had been faster to use the full capabilities of telematics. For years, they were just used to tell people after the fact that they had been recorded doing something dangerous. The incentive to do better was there but remote, because the incidents would only affect premiums somewhere down the line. It's only in recent years that telematics devices are being used to coach drivers in real time about being drowsy, following too closely, etc., and even now the focus is mostly on fleets of drivers, not individuals. 

I understand technology adoption curves, so I know the industry couldn't just wave a magic wand. I also realize that part of the issue is critical mass — you can't do something like aggregate data on hard braking if you don't have enough cars on the road using telematics that can report instances of the behavior.

But I think back to how magical it seemed 25 years ago when I wrote something about how it was going to be possible to learn about traffic jams in real time, because authorities were going to track mobile phones in cars. If phones in an area were stopped, you had a problem. If they were all going 75mph, things were all clear. 

And I think we're at this sort of place now with sensors in cars. Hard braking is actually just one example. Sensors will be able to report on potholes or other problems with roads. Dashcams can monitor for other safety-related issues, including crazy driving. (Yes, privacy will be a thorny problem.)

But, for now, I'll be happy if we can just get information on potential accidents into the hands of the right people so they can do what they can to head off fatalities, injuries and property damage. Traffic deaths in the U.S. have been falling in recent years, but more than 40,000 people lost their lives on U.S. roads in 2024, and that's far too many, even for a country that loves its cars.

Cheers,

Paul  

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