Now is the winter of our discontent. With this famous line, Shakespeare voiced the frustration of a prince with kingly ambitions, but the sentiment is familiar to modern homeowners, agents and insurers. Winter is notorious for plumbing failures. Frozen pipes and related issues account for a growing share of water-damage claims, and losses add up quickly.

Home issues compound this time of year, from burst pipes and ice dams to heating system failures and holiday fire hazards. Property insurers know that many of these incidents are preventable. Most can be avoided with routine maintenance and better visibility into smart protection systems. And it's up to insurers to help homeowners understand the risks before they turn into losses.

Winter Woes & Tech Solutions

Plumbing failures remain one of the biggest drivers of non-weather water loss. Aging infrastructure, temperature volatility and unmonitored second homes create the perfect storm that leads to high-severity claims. Close to 23% of U.S. home insurance claims are categorized as "water damage or freezing," which includes pipe freezes and burst-pipe losses. It's second only to wind or hail, with average claims costing more than $15,000. Homeowners often don't realize pipes are vulnerable until after they burst, especially in attics, garages, exterior walls, pool houses and crawl spaces.

As winter plumbing failures escalate with more volatile weather swings, the need to adopt technology becomes clearer. Modern shutoff valves can prevent or reduce the severity of water damage by stopping the flow before a leak spreads, but a device that's incorrectly installed, unplugged or offline offers no protection.

Devices installed in cold-exposed areas or crawl spaces often fail because they're placed in vulnerable spots, leading to flooded boxes and power shutoffs. Industry monitoring data shows that 30–50% of insurer-credited shutoff systems are offline or not functioning properly, meaning homeowners believe they're protected when they're not.

Reducing Risk Through Seasonal Checklists

Risk management experts recognize the scenarios where homeowners consistently discover gaps in coverage. Some learn too late that losses tied to neglected maintenance are excluded, such as frozen pipes in unheated areas or long-term leaks. High-net-worth property owners in particular may assume "everything is covered," but policy sublimits for water damage vary widely. Secondary and vacation properties are also disproportionately exposed due to extended vacancy and delayed monitoring.

Carriers and agents can give clients clearer guidance on avoiding winter damage by reinforcing these steps:

• Providing temperature guidance — reminding customers to keep thermostats at 65°F or above, especially while traveling. Lower settings may save money, but temperatures that drop too low can cause pipes to freeze and expand, putting faucets and plumbing at risk of cracking or bursting.
• Reinforcing preseason prep — advising homeowners to shut off and drain exterior spigots and irrigation lines before the first freeze and to cover them with a pool noodle, a towel, or covers purchased from a hardware store.
• Promoting smart devices — encouraging customers to install automatic shutoff devices and to verify monthly that they are online; many losses occur because devices are installed incorrectly or are offline.
• Emphasizing backup power — recommending battery or power backup so leak-detection and shutoff devices stay active during outages.
• Making claims easier if something does go wrong — encouraging homeowners to maintain an updated inventory and a brief photo/video walkthrough documenting the items in their home.

At the end of the day, insurers provide something more than coverage; they provide peace of mind. And agents play an equally critical role as trusted advisors who help homeowners understand risks, make informed decisions and stay ahead of preventable losses.

Helping Homeowners Stay Educated

Beyond plumbing, carriers and agents should leverage their communication channels – email, social media, mailers and seasonal checklists – to highlight other winter risks. Unattended holiday candles and overloaded electrical circuits can quickly escalate into fire-related claims. Downed trees and ice-related roof damage are more common after early freezes followed by rain. And temporary residents, from visiting family to short-term renters, add risk that homeowners may overlook.

Insurer education remains essential to reducing loss severity and strengthening client relationships. Making this outreach before cold-weather events ensures homeowners are equipped with the right knowledge and coverage. Technology can help, but protecting a home still requires attention and involvement.

As the Bard wrote, our remedies oft in ourselves do lie.

Heavy regulation is a major challenge for the insurance industry. Every state has its own rules, and companies may also need to comply with Federal Trade Commission and Department of Health and Human Services regulations. The insurance industry is also susceptible to cyber attacks and data breaches due to the significant amounts of personal information they obtain and retain. Think about all the Social Security numbers, home addresses, financial information and personal health records in the insurance industry’s databanks. 

Most companies believe they have good cyber security because they use multifactor authentication, better known as MFA. According to the Crowell and Mooring law firm, a bad actor known as “Scattered Spider” is targeting large insurance companies in the U.S. Scattered Spider bypasses multifactor authentication and internal security protocols by using sophisticated social engineering and identity theft strategies. The attacks and breaches by Scattered Spider fit into a wider trend of criminal organizations that take advantage of vulnerable supply chains and third-party relationships.

You and your staff spend a large portion of their work days accessing carrier systems, logging in with different credentials for each one. It’s more than a cybersecurity issue. It impacts productivity as well as causing employee frustration and customer dissatisfaction. If an agency has 100 employees who access 65 carriers or MGAs in the course of business, that agency is responsible for protecting no fewer than 6,500 IDs. 

By the numbers

According to A Cyber Security Assessment of the Insurance Industry Supply Chain, a report issued in February 2025 by SecurityScorecard, an analysis of 150 leading insurance companies found that their cybersecurity was being compromised through their supply chain partners. 

The following are some surprising statistics from the report:

• Third-party breaches reached 59%, the highest rate observed so far and more than double the global cross-industry average of 29%.
• Third-party software & IT caused 50% of these breaches. Cross-industry software & IT accounted for 37%, far outpacing insurance-specific IT (13%).
• Out of 150 companies, 84 (56%) had at least one compromised credential in the past two years.
• U.S.-based companies, particularly insurance carriers and agencies and brokers, face disproportionately high breach rates.

MFA is 40 years old

Although MFA may seem new, it’s been in use in some form since the 1980s, starting with the SecurID token, which was introduced by RSA in 1986. The token generated strings of numbers that were good for a short time, then the numbers were replaced by another string. Beginning in the 2000s and 2010s, MFA adoption became more widespread. The pandemic speeded up adoption for many companies as their employees worked from home, not always on a company-owned device.

The New York State Department of Financial Services (DFS) first issued cybersecurity regulations for financial services companies on March 1, 2017, and has regularly updated the requirements. [23 NYCRR Part 500] The most current update, effective Nov. 1, 2025, requires covered entities, small businesses and Class A companies to comply with enhanced MFA regulations. As of that date, covered entities are required to use MFA for any individual accessing any of its information systems, regardless of location, type of user, and type of electronic information contained on the information system being accessed, among other things.

The definition of covered entities includes “partnerships, corporations, branches, agencies, and associations operating under, or required to operate under, a license, registration, charter, certificate, permit, accreditation, or similar authorization under the Banking Law, the Insurance Law, or the Financial Services Law.” 

The text of the updated MFA regulations, Section 500.12, Multi-factor authentication, reads as follows:

(a) Multi-factor authentication shall be utilized for any individual accessing any information systems of a covered entity, unless the covered entity qualifies for a limited exemption pursuant to section 500.19(a) of this Part in which case multi-factor authentication shall be utilized for:

(1) remote access to the covered entity’s information systems;

(2) remote access to third-party applications, including but not limited to those that are cloud based, from which nonpublic information is accessible; and

(3) all privileged accounts other than service accounts that prohibit interactive login.

(b) If the covered entity has a CISO, the CISO may approve in writing the use of reasonably equivalent or more secure compensating controls. Such controls shall be reviewed periodically, but at a minimum annually.

The key here is “remote access to third-party applications.” Section 500.11, Third-party service provider security policy, mandates that covered entities implement written policies and procedures designed to ensure the security of information systems and nonpublic information accessible to or held by third-party service providers. The covered entity’s policies and procedures must include relevant guidelines for due diligence and contractual protections addressing the third-party service provider’s policies and procedures for access controls, including its use of multifactor authentication as required by Section 500.12, and the use of encryption to protect nonpublic information in transit and at rest.

In October 2025, DFS published an industry guidance letter on the responsibility covered entities have when TPSPs are utilized. The DFS regulations require compliance from covered entities and puts the onus for third-party services provider compliance on the carrier or agent using that service. Regarding authentication, the industry letter clarifies this requirement for TPSPs stating “requirements for TPSPs to develop and implement policies and procedures addressing access controls, including multi-factor authentication, that comply with requirements in Sections 500.7 and 500.12.[22).”

The October industry letter includes definitions for “covered entity,” “third party service provider,” “information system,” and “nonpublic information.” With as much as 80% of the personal lines comparative quoting using TPSPs and the amount of non-public information in these transactions this feels like a significant compliance risk across the industry.

The DFS has been more aggressive in compliance breach actions, securing more than $19 million in fines from eight auto insurance companies. However, ID Federation carriers and service providers can utilize SignOn Once, which includes MFA capability, to reduce cyber and regulatory risk exposure.

Why should carriers and agents be paying careful attention to this regulation? Because many other states have been known to follow New York’s lead when it comes to regulating the insurance industry. That means carriers and agents soon may have to comply with stricter MFA regulations in all the states in which they are licensed to operate.

Carrier and agent accountability

All insurance industry participants are accountable for protecting policyholder information to some degree, especially the carrier that maintains the largest databank of that information. Additionally, the insurance agencies that sell the insurance products access the databank most, often more than the carrier’s employees. 

The agency management system (AMS) constitutes a critical doorway for efficiency in managing the day-to-day business, but convenience and efficiency come with increased risk and obligation. Reasonable care requires carriers and agents to manage identity responsibly, effectively and efficiently. Federating identity at the AMS level and adding the convenience of SignOn Once increases the overall security of the system and shows the carrier’s commitment to protecting policyholders by restricting access responsibly. The agency gains the efficiency of having a simplified login but also benefits from association with carriers that demonstrate responsible management of consumer data. 

ID Federation is working towards an ideal state for the industry in which implementing SignOn Once simplifies incorporating MFA. The agency administrator adds a new user to their agency management system, and they check the MFA box. Then, a flag is sent during the logon process if the carrier is participating in SignOn Once. This indicates the user went through MFA as they logged into their AMS. Users only need to remember login credentials for their own AMS, not for all their participating carrier partners.

This is a huge benefit. If an agency management system user connects to 10 carriers and all have implemented SignOn Once, those users only need to manage MFA once when logging into the system, not for every participating carrier. The time saving is enormous.

Here’s what that means for the insurance agency:

• One secure login across participating systems: Sticky notes on bulletin boards or computer monitors full of passwords will be eliminated.
• Fewer MFA interruptions: Employees won’t need their personal smartphones available at all times even as security requirements become more stringent.
• A smoother, more efficient workflow: Productivity and employee satisfaction will increase while frustration and mistakes decrease.

How can ID Federation help?

ID Federation is the nonprofit organization formed to develop a single sign-on technology for the property-casualty industry. That technology — called SignOn Once — has been in place for about 10 years and continues to show positive results.

What are the advantages of SignOn Once?

1. Trustworthy security. ID Federation developed a trust framework (downloadable here on this site) to protect the security of its federated partners. By using individual credentials and tokens, and certifying identity providers (vendors such as Vertafore and Applied Systems), SignOn Once ensures logins are safe and eliminates many of the issues associated with poor password protection.
2. Ease of doing business. SignOn Once allows carriers and agencies to do what they do best: Sell insurance and serve clients. With a seamless, secure connection to insurance carriers and solution providers, users can spend more time collaborating and less time worrying about passwords.
3. Reduced independent agent channel cost. Eliminate the operation time and cost to maintain and use multiple user IDs, passwords and MFA processes. Direct and captive agents don’t incur this cost with a single carrier partner. This also reduces the cost related to password resets and cyber breach by requiring fewer credentials.

SignOn Once won’t eliminate the additional layer of authentication required by New York State’s updated cybersecurity rules, but it will reduce the number of times carrier and agency staff have to log into systems each day. As long as they remain logged in to their management system, they are able to seamlessly and securely access other federated partners.

Learn more about becoming an ID Federation member and engaging your carriers at  https://idfederation.org/engage-your-carriers/. 

About the author

Alvito Vaz is executive director of the ID Federation. He has had over 30 years of leadership in the insurance industry with technology positions at Progressive and Travelers. His involvement in the agency automation space has included working with comparative rater and management system solution providers. As a member of ACORD’s Property & Casualty Steering Committee, he was engaged in the insurance standards setting process. An inaugural member of IIABA’s Agents Council for Technology (ACT), he has chaired and participated in ACT workgroups. Alvito continues to champion the use of standards to improve operational efficiency across the IA channel.

Sponsored by: SignOn Once

The CEO of SAP caused a stir in late January when he said, "The end of the keyboard is near.... “The future will be, for sure, that you are not typing any data information into an SAP system." He added that people will use their voices, not their fingers, to ask analytical questions of SAP systems, to trigger tasks, to make pipeline entries and more. 

A recent Wall Street Journal column carried the headline, "Our Gadgets Finally Speak Human, and Tech Will Never Be the Same." The columnist, Christopher Mims, wrote: "This is shaping up to be the year that AI makes talking as powerful as tapping and swiping. The shift could be as transformative for the tech industry as the introduction of the Mac, Windows or the iPhone."

While I'm not convinced the change will be quite as total or as fast as the SAP CEO says it will be, he and Mims are describing an important new wave of convenience and productivity that AI will provide.

Let's have a look at what will — and maybe won’t — happen.

Ever since I learned to type in ninth grade, I haven't been fully convinced I could have a serious thought without my fingers on a typewriter or a keyboard. I'm sentimental enough about my father's manual typewriter that I have one displayed on my bookshelves. But, if you step back and think about it for a moment, keyboards are extremely inefficient, especially as a way to control a computer. 

The core problem is that few people can type as fast as they think. But beyond that, we're always moving our cursors around to click on one thing or another. A touch screen can remove some of the inefficiency, but even then you're moving on and off the keyboard frequently. 

Voice is a much faster and more natural way to do a lot of the things we now do with our fingers — and the voice capabilities of AI have improved by leaps and bounds. As a result, the capabilities are starting to be built into software that we use for all sorts of tasks. 

I now dictate my text messages — saving me ever so much time by ending the trouble my fat fingers used to cause me. Voice is also showing up in word processing and email systems. Voice will allow me to tell my email that I want to reply to someone, then to dictate a quick response and be done with it. As the CEO of SAP says, data entry is also a natural for voice. No more peering over at forms, while trying to read the numbers on a sheet of paper and flipping your eyes back and forth between the paper and the screen to make sure you've typed the information correctly. You just hold the paper in front of the screen and read the data, which you can easily check with a glance. 

Combining the voice capabilities with generative AI, we'll be able to just ask for data, for access to corporate systems or for any number of other things that would have taken us much longer in the past. No single action will save a ton of time, but the efficiencies will add up, and considerable drudgery will disappear. The computer mouse was an exceptionally important invention, but voice is faster and more natural. 

The voice capabilities will soon be everywhere, because the arms race for AI dominance is still going full speed. In recent earnings announcements, big AI players said they were going to spend $650 billion on AI infrastructure in 2026. That's roughly the GDP of Belgium or Nigeria  — and those announcements were just from four companies. Eleven Labs, a startup using AI to translate speech to text and vice versa, just raised $500 million at an $11 billion valuation, more than three times the valuation when it raised capital a year ago. Mims, the WSJ columnist, says the AI voice capabilities will increasingly find their way into convenient hardware, including Meta's glasses. 

I still don't think keyboards will go away, at least not soon. I still can't really think without my fingers on the keys for something as long as what I'm writing here, and, in general, I think keyboards will be important for editing. Talking to my computer to dash off a quick, formulaic email is one thing. But editing is serious business, and it's easier just to use a mouse or a finger to get to the offending spot in the text than it would be to tell the computer to go to the second sentence of the third paragraph, to a specific word in the sentence that starts with XYZ. Even with the explosion of voice capabilities, it will take time for software developers to come up with the right mix of voice and touch commands. 

I also don't think, as I've written previously, that voice will be a great way to buy things, certainly not things as important and complex as insurance. The lure of voice for purchasing was behind a lot of Amazon's early efforts with its Echo devices, but voice-based buying hasn't really happened even for paper towels and dog food. For anything more complicated, a buyer wants to see all the details so they can weigh the various variables against each other. You need a screen for that, and likely a keyboard.

The transition will be a journey and will take the form of a voice/keyboard hybrid for the foreseeable future. But I'll certainly be happy to increasingly use my voice to control my devices, saving bits of time and of frustration along the way. 

Cheers,

Paul

Insurers are navigating fast-moving markets, shifting performance, and growing data volumes. Machine learning and AI can turn complexity into clarity by spotting what's changing, explaining why, and guiding action with confidence.

Used appropriately, machine learning speeds analysis, strengthens decisions, and reduces manual effort without compromising governance. I will outline five practical ways machine learning is advancing portfolio management today and how modern tooling helps teams manage the complexity that comes with larger model estates and richer data.

1. Identify emerging trends faster

When performance diverges from plan, the difference between a timely intervention and a late response often comes down to signal detection.

Machine learning helps surface early signs of change: whether it's a spike in repair cost inflation, a drop in claims frequency in a specific region, or a retention shift that alters portfolio mix.

By revealing meaningful patterns sooner, teams can protect margin, target profitable growth, and rebalance exposure before small deviations become big problems.

2. Strengthen risk assessment and segmentation

Machine learning brings sharper granularity to segmentation and risk assessment, enabling insurers to recalibrate assumptions as conditions evolve. The most effective approaches are built for insurance: explainable to business stakeholders, defensible to regulators, and practical for day-to-day use.

The payoff is clearer pricing and underwriting decisions across geographies, demographics, and product features—turning complex data into decisions that withstand scrutiny.

3. Accelerate decision-making with automation

Automation turns model monitoring from a periodic task into a continuous source of insight. Near-real-time reporting highlights what changed and where to act, while governed thresholds can trigger refits or reruns to keep performance on track.

This entire process can be automated. Automated model monitoring allows businesses reliable insights on business performance; portfolio and segment trends; and changing risk exposures, all while models can also be automatically rerun in the background if their performance degrades below a certain point. Analytics teams can get back to identifying new opportunities, rather than manually reviewing current model performance.

It's important to note monitoring is a key part of portfolio management in model-dense environments, but it's not the whole story. It complements broader actions such as dynamic pricing, geographic rebalancing, channel strategy and product design—helping teams move from observation to execution faster.

4. Manage complexity without the drag

As insurers expand their model estates and integrate richer data sources, the challenge shifts from building models to running them efficiently at scale. Many insurers now operate estates numbering in the hundreds, a testament to how quickly the industry has embraced machine learning. With scale comes complexity. More models mean more oversight, especially as newer model types tend to degrade faster. If models underperform, so will the business.

Modern platforms help insurers stay ahead by combining automation, versioning and governance, allowing teams to maintain transparency and control without slowing down. A governed environment for deploying AI and machine learning models, including Python, reduces IT bottlenecks while preserving auditability. Radar’s Python deployment component enables insurers to benefit from the flexibility and innovation of open source but in a controlled, robust manner that supports business critical decisions and generates real value. This combination of flexibility with control turns operational complexity into a strategic advantage.

5. Go beyond monitoring to steer the portfolio

Machine learning's role does not end with detecting issues. It also helps answer "what if?", from the impact of price changes and rate adoption to exposure limits and product mix.

By pairing trend detection with scenario testing, insurers can quantify trade-offs before rollout and build a continuous loop from insight to action to measurable outcomes. The result is a more responsive, disciplined approach to portfolio management that aligns daily decisions with strategic goals.

KEY TAKEAWAYS

• The membership economy is reshaping insurance by replacing transactional, annual renewals with continuing, affinity-driven relationships rooted in trust and shared identity.
• Subscription-based services offer insurers a sustainable growth path by embedding protection as a seamless benefit of membership rather than a standalone product shopped for based on price.
• The future of the insurance industry depends on shifting from policy-centric models to member-centric experiences that deliver continuous, visible value beyond claims.
• Affinity organizations hold a competitive advantage because their existing trust and engagement dramatically reduce churn and customer acquisition costs.
• Scalable insurance technology solutions are essential to enable frictionless self-service, personalization, and real-time engagement that modern members now expect.

For decades, insurance has been a transactional, "set it and forget it" chore tied to an annual renewal. But this episodic approach is increasingly out of step with how people actually live.

Today, the most successful protection models are built on affinity. Consumers aren't looking for another disconnected vendor; they want to leverage the memberships and associations they already trust. They make purchases within these trusted circles because of the value and identity those groups provide.

For the insurance sector, this means shifting from a passive contract to a seamless, value-added benefit of belonging, turning insurance from a standalone bill into a core advantage of the affinity relationship.

The Death of the Transactional Mindset

The rise of the subscription economy has fundamentally reshaped consumer behavior, moving the needle from ownership to access and from transactions to relationships. In most retail and service sectors, the "membership mindset" has eliminated the friction of the re-purchase decision. When a consumer subscribes, they are choosing to bypass the exhaustion of constant comparison. However, this loyalty is not granted for free; it is traded for continuing value, transparency, and ease of use.

Consider the Amazon Prime model: Members don't spend hours cross-referencing prices across a dozen different websites for every household item. Instead, they head straight to Amazon, use "Buy Now" for a frictionless checkout, and move on with their day. That loyalty is rooted in the belief that, as a member, they are already getting a competitive price and a level of convenience that far outweighs the potential of saving a few cents elsewhere.

To understand the future of the insurance industry, we must look at how subscription models bridge the loyalty gap. In the traditional model, the annual renewal cycle creates a natural friction point. It's at this moment where the lack of a deeper relationship encourages the customer to comparison shop.

Shifting to an affinity-based membership model changes this dynamic entirely. Instead of the renewal notice acting as a yearly "call to action" to find a cheaper alternative, the insurance remains anchored to the value of the organization itself. When protection is part of a trusted membership, the "choice" to stay is already made; the consumer remains because the insurance is a seamless part of a community they already value.

Trust as an Operating System in Affinity

While legacy insurers struggle to pivot away from actuarial-centric models, affinity groups possess a natural advantage. These organizations already have the most expensive ingredient in the subscription recipe: trust.

Traditional carriers spend billions on customer acquisition, often fighting for "switching" customers who are motivated solely by price. Affinity insurers, conversely, operate within a framework of pre-existing loyalty. By adopting a subscription-based model, these groups can embed insurance within a larger bundle of perks, such as exclusive content, community access, or professional tools.

This bundling changes the psychology of the consumer. If a policyholder feels the value of their membership every week through a discount portal or a professional resource, the underlying insurance product becomes "sticky" by association. The subscription model allows affinity groups to move away from being a mere distribution channel and toward becoming a holistic service provider.

The Role of Frictionless Technology

The transition from a policy-centric model to a member-centric one is impossible without a robust digital foundation. Insurance technology solutions are the backbone of this evolution. In a subscription framework, the "user experience" is the product. If a member can upgrade their Netflix plan in two clicks but must call a broker and wait 48 hours to adjust an insurance limit, the relationship is doomed to fail.

Modern insurance technology solutions enable the self-service capabilities that subscribers now demand. This includes flexible payment structures, transparent "tiers" of coverage, and the ability to pause or pivot protection as life circumstances change. Beyond the interface, technology allows for the data-driven personalization that defines the membership economy.

By leveraging member data and technology, insurers can move from being reactive, such as merely responding to a claim, to being proactive by offering a specific coverage adjustment based on a member's life stage or behavior.

Overcoming the "Invisibility" of Insurance

The greatest challenge in applying the subscription model to insurance is the lack of a tangible "delivery." Unlike a streaming service where you see the content daily, or a meal kit that arrives at your door, insurance is often invisible until something goes wrong. To ensure long-term success, insurers must find ways to communicate value between claims.

This requires a total rethinking of engagement. A successful insurance subscription should provide "living benefits." This might include risk mitigation alerts, wellness rewards, or integration with IoT devices that provide the member with a sense of security and utility on a regular Tuesday afternoon, not just when a basement floods.

Furthermore, the industry must grapple with legacy infrastructure. Most core systems were built for the rigid architecture of annual cycles. Moving to a subscription model requires an investment in agile billing systems and CRM platforms that can handle the high-frequency interactions of a membership relationship. It also requires a sophisticated approach to regulation, ensuring that monthly billing and "cancellation at will" policies comply with state-level consumer protection laws.

The Future of the Insurance Industry

The shift toward the membership economy requires the insurance industry to stop asking, "How do we sell a policy?" and start asking, "How do we become an indispensable part of this person's life?"

For affinity groups and forward-thinking carriers, the subscription model offers a path toward increased lifetime value and decreased churn. By mirroring the convenience of subscription-based services and leveraging insurance technology solutions, the industry can finally bridge the gap between what it sells and how people actually want to buy.

The future of the insurance industry is one where protection is seamless, the benefits are constant, and the relationship is renewed not by a contract, but by the continuing delivery of value. The insurers that thrive will be those that stop treating people like risks to be managed and start treating them like members to be served.

The middle market remains one of the most persistent protection gaps in the insurance industry. Middle-income households face meaningful financial exposure yet remain underinsured across life and annuity (L&A) insurance. Traditional insurance distribution models were designed for high-touch sales, longer underwriting cycles, and relatively high premiums. These models struggle to operate efficiently for lower-margin products that require speed, simplicity, and flexibility. As a result, many middle-market customers remain unserved, not because insurance is irrelevant but because existing distribution models cannot reach them at scale.

Traditional Distribution Model Wasn't Built for the Middle Market

Insurance has long been described as a product that is sold rather than bought. This dynamic becomes especially pronounced in the middle market, where customers often lack the urgency or familiarity that would prompt purchasing through conventional channels. At the same time, the economics of agent-led distribution are poorly suited to products with lower premiums and shorter lifecycles.

Middle-market customers typically require coverage that can be purchased quickly, adjusted over time, and delivered within digital experiences they already use. Traditional models, built around lengthy sales processes and manual servicing, are misaligned with these expectations.

How AI Makes Embedded Distribution Scalable

Embedded insurance is no longer a niche distribution experiment. Recent analysis estimates global embedded insurance sales of $87.4 billion, projected to grow at a 20% CAGR from 2023 to 2032. The channel is maturing quickly, but its strategic significance is not simply reach. It is that embedded models shift how protection is priced, sold, and supported when coverage is delivered inside third-party ecosystems rather than insurer-owned journeys.

As outlined by RGA, embedded programs generally take three forms. Soft-embedded (opt-in) insurance is presented contextually at the point of a primary purchase, such as travel insurance offered during flight booking. Hard-embedded (opt-out) insurance is included by default within a broader transaction, requiring the customer to actively decline coverage. Invisible insurance is embedded so deeply within the primary service that coverage is automatically activated based on participation in the service.

While embedded distribution expands access, it also introduces new operational pressures. Embedded products are high-volume, lower-premium, and event-driven, which compresses decision timelines and pushes servicing costs closer to economic limits. Servicing expectations remain high, even as tolerance for manual intervention declines.

This is where AI plays a critical role. Embedded insurance changes not only where insurance is offered, but how protection is evaluated, quoted, bound, and serviced. For insurers and MGAs, AI is applied across distributed journeys to coordinate decisioning, data access, and workflow execution across underwriting, policy issuance, servicing, and claims.

Applied at the workflow level, AI aligns automated decisions, business rules, and human oversight within a single operating flow. This supports consistency, traceability, and governance as products scale across partners and channels.

Embedded insurance only succeeds when the experience is seamless. For bite-sized products, this translates into near-100% straight-through processing at purchase and a largely touchless claims and servicing experience thereafter. AI enables this by coordinating real-time data access, automated decisioning, and workflow execution across the lifecycle, allowing insurers to scale volume without increasing operational friction.

Embedded Insurance in Practice

These models place different demands on insurers across product design, distribution, servicing, and compliance. They also reset expectations around speed, simplicity, and relevance, particularly in middle market segments where traditional distribution often struggles to operate economically. Boston Consulting Group has cited Prudential Financial’s Simplified Solutions initiative with Neutrinos as an example of how AI-enabled, embedded distribution models can expand access to life insurance in North America.

Market sizing reinforces the upside: Forrester estimates that building new solutions for just 1% of the roughly 4 billion underserved people globally could translate into approximately 40 million new customers, if insurers can deliver simpler products through scalable journeys.

Closing the Gap at Scale

Embedded insurance is increasingly proving itself as a scalable distribution model rather than a niche channel. However, distribution alone is insufficient. Insurers that invest in the operational foundations required to support high-volume embedded products, including AI-enabled workflow orchestration, explainability, and integration with existing systems, are better positioned to improve access and close long-standing protection gaps in the middle market.

70% of vehicle collisions are caused by inattention, including distraction, cell phone usage, and fatigue. Most of this risk develops silently, unseen by fleet managers and insurers alike, only potentially becoming visible once it has resulted in a claim.

That reality exposes a growing structural weakness in commercial motor insurance. If the majority of collision risk forms upstream of loss, underwriting frameworks built primarily on historical claims data are, by definition, incomplete. In a market grappling with rising severity, social inflation, and earnings volatility, this gap is no longer theoretical. It is material.

The industry's next competitive advantage will come from seeing risk earlier and acting on it before loss occurs.

Risk begins long before first notice of loss

For decades, underwriting has relied on lagging indicators such as loss runs, experience modifiers, and aggregated exposure metrics. These tools remain necessary, but they describe outcomes rather than causes. In commercial auto, collisions are rarely random events. They are typically preceded by identifiable behavioral patterns interacting with vehicle dynamics and environmental conditions.

Until recently, those precursors were largely inaccessible to insurers. Risk could be priced after the fact, but not meaningfully influenced in advance.

From descriptive telematics to predictive intelligence

Early telematics solutions represented an important step forward, providing visibility into speed, harsh braking, acceleration, and location. These signals improved transparency and gave insurers better behavioral proxies, but they remained descriptive. They explained what had already happened rather than what was about to happen.

Predictive artificial intelligence fundamentally changes that relationship with time.

By analyzing multiple contextual signals simultaneously, including driver attentiveness, following distance, vehicle movement, and road conditions, advanced AI systems can identify elevated collision risk as it forms. Crucially, this intelligence can be acted upon in real time, alerting drivers in the critical seconds before a potential impact. While that window is narrow, it is often enough to change the outcome entirely.

At Nauto, for example, AI models trained on more than 6 billion miles of global driving data have demonstrated the ability to detect imminent collision risk with over 99% accuracy, validated through independent research. In practice, interventions typically occur two to four seconds before the triggering event. Those few seconds frequently determine whether an incident becomes a near miss or a loss event.

This represents a shift from measuring perceived risk to actively influencing outcomes, and it has profound implications for underwriting.

What this changes for underwriting

Predictive behavioral intelligence introduces a new variable into the underwriting equation, real-time risk quality. Rather than inferring exposure solely from historical outcomes, underwriters can now access leading indicators of attentiveness, distraction, and behavioral discipline.

This capability is particularly valuable in portfolios with limited claims history, rapidly evolving operations, or exposure to emerging risk factors where backward-looking data provides limited guidance. Pricing becomes more responsive and more defensible. Fleets that demonstrate sustained behavioral improvement can be differentiated with greater confidence, while persistent risk signals can be addressed earlier through pricing, terms, or targeted intervention.

The result is a move away from portfolio-level averaging toward a more granular assessment of how risk is actually created.

For MGAs, predictive intelligence enables prevention to be embedded directly into product design, aligning delegated authority with real-world risk outcomes. For brokers, it strengthens the advisory role by grounding renewal conversations in objective, forward-looking evidence rather than retrospective explanation. Across the value chain, assumption is replaced with observation.

The economics of prevention

Prevention does not simply reduce claim counts. It changes claim outcomes.

Across fleets deploying Nauto's predictive AI, collision frequency reductions of between 40% and 60% are consistently observed. Importantly, the effect does not stop there. When collisions are avoided entirely, claims disappear. When incidents do occur, earlier intervention often reduces speed at impact, point of contact, and loss complexity, leading to materially lower claim severity.

This dual effect, fewer claims and less severe claims, alters loss cost trajectories in a way traditional risk controls rarely achieve. Secondary costs decline alongside primary losses. Vehicle downtime is reduced, supply-chain disruption is minimized, litigation exposure falls, and operational volatility softens. Claims that do occur are resolved faster and with greater confidence when supported by contextual video and AI-derived insight, reducing frictional cost and uncertainty.

At scale, these dynamics stabilize combined ratios and improve capital efficiency. In a market where margin expansion through pricing alone is increasingly constrained, prevention offers a durable alternative grounded in operational reality rather than actuarial optimism.

From recovery to partnership

This shift reflects a broader evolution in the role of insurance. Predictive AI does not replace underwriting judgment or risk management expertise. It enhances them. It allows insurers, brokers, MGAs, and fleet operators to share a clearer, real-time understanding of risk as it actually unfolds, rather than reconstructing it after the fact.

The insurers best positioned for the next decade will be those who underwrite behavior rather than history, reward prevention rather than recovery, and treat intelligence as something to be acted on, not archived. In an environment where risk is increasingly dynamic and unforgiving, resilience will belong to those who can see loss forming and intervene before it ever reaches the balance sheet.

Prevention is no longer an aspirational ideal. It is becoming a defining capability, and increasingly, a prerequisite for sustainable underwriting performance.

The hybrid fronting model is gaining traction in the re/insurance market, driven by a shift toward deeper risk alignment and the rapid expansion of MGAs.

As of 2026, approximately 25 major fronting carriers now operate in the U.S., with new ones launching frequently. The surge is tied to the expansion of MGAs, the tightening of reinsurance capacity, the growing need for efficient capital, and the availability of insurtech-enabled data modeling. Gallagher Re reports that hybrid fronting carriers generated nearly $28 billion in gross written premiums by the start of 2025, a significant portion of the total $100 billion-plus MGA market. A 2025 TMPAA survey reveals that 19% of program administrators now use hybrid fronting models for their operations.

What is a hybrid fronting carrier, and why is this model taking off now?

DEFINING A NEW MODEL

Hybrid fronting carriers are fully licensed and regulated insurance entities that provide rated paper to MGAs and program partners while retaining a share of the underwriting risk on their own balance sheets. Their retained portion of risk is typically 5% to 30%, and rated insurance paper is provided to MGAs/MGUs, captives, and programs. Hybrid fronting carriers use reinsurance or alternative capital to cover the remaining risk and maintain strong capital partner relationships with private equity firms or insurance-linked securities (ILS) to fund growth and operations.

Unlike traditional fronting carriers, which pass all risk along to reinsurers, hybrid fronting carriers retain a meaningful portion of exposure. In doing so, they share the risk and the incentives with other stakeholders across the insurance lifecycle. That retained risk drives tighter alignment with reinsurers and capital partners and positions hybrid fronts as not just enablers, but committed participants in the value chain. In other words, hybrid fronting carriers have more "skin in the game" than traditional fronting models. Ultimately, this sharing of risk results in more disciplined underwriting and oversight.

The hybrid fronting carrier model is becoming a more attractive option in the MGA and specialist program space, especially for launching new, niche, or complex insurance products quickly.

KEY DRIVERS OF THE HYBRID FRONTING CARRIER

There are multiple drivers behind the rise of the hybrid fronting carrier model. At the top of the list is capacity. Traditional reinsurers have been reducing capacity in challenging insurance markets, such as California, Texas, and Florida, where NatCat claims, including hurricanes, floods, and wildfires, have led them to reduce their exposure to tail risks. Others are pulling out of these markets entirely. As a result, demand for alternative capacity is rising as traditional insurers shift risk appetite or exit markets.

Hybrid fronting carriers are also filling an expanding gap in the excess & surplus (E&S) insurance market by serving as a bridge between specialized, high-risk, or niche business produced by Managing General Agents (MGAs) and the risk-bearing capital of reinsurers.

Another key driver is the acceleration of MGA growth, not just in the U.S., but globally. MGAs are growing rapidly and seeking flexible partners to develop new products and expand capacity. Hybrid fronting carriers supply MGAs with the rated paper and shoulder some of the risk, enabling them to launch niche or specialist programs, like cyber risks or complex casualty lines, at speeds much faster than traditional carriers with legacy systems. They are essential for navigating "volatile and emerging risks" where traditional insurers may have pulled back. This is crucial to MGAs' success, where the first to market with an innovative product wins the race.

Alternative capital fills these gaps for E&S insurance organizations and MGAs left by shrinking traditional supply. For capital providers, such as private equity and ILS investors, the hybrid model provides an efficient, scalable way to enter insurance markets without partnering with a traditional, full-scale carrier. Additionally, it streamlines cross-border expansion for MGAs by managing complex local licensing and regulatory compliance. Private equity and ILS investors are interested in hybrid fronts as capital-efficient, lower-risk insurance platforms.

BENEFITS FOR ALL PARTIES

Who wins with this model? Basically, all parties involved benefit. Hybrid fronting carriers present significant benefits to MGAs, program managers, and reinsurers. A shared alignment of interests ensures better underwriting and oversight, faster market access, and greater capital efficiency.

Other key benefits include:

• Multi-year capacity stability: While traditional reinsurers are tightening capacity, hybrid fronts can access a broader investor base. MGAs can reduce their reliance on a small group of traditional reinsurers.
• Faster product launches and distribution: Hybrid fronting carriers provide the necessary AM Best or S&P rating and state licensing required for MGAs to write business immediately. This bypasses the multi-year process an MGA would otherwise face to become a standalone licensed insurer.
• Access to reinsurance and capital markets: Unlike traditional insurers, which can face rigid internal governance, hybrid fronts act as conduits to global reinsurance markets. This provides MGAs with a broader pool of capital to back specialized or niche programs.
• Higher transparency and potential for profit-sharing between stakeholders: Unlike traditional models, where data is exchanged in fragmented silos using PDFs or monthly reports, modern hybrid carriers are built on technology and use unified underwriting command centers, making them extremely transparent. Because the hybrid front shares in any losses, it has a financial incentive to perform the same level of due diligence as a standard carrier, including thorough analytic and exposure reviews.
• Lower cost base compared to traditional carriers: The best hybrid fronts operate as lean, technology-driven entities that delegate expensive operational functions to specialized partners.

Fronting carriers play a crucial role in enabling MGA distribution by providing regulatory paper, compliance frameworks, reporting mechanisms, and risk-sharing structures necessary to launch new programs.

TECH-FIRST HYBRID FRONTING CARRIERS WILL WIN

However, with this model comes new complexity. Hybrid fronting carriers often manage numerous MGA relationships, demanding coordination of complex capacity flows, diverse systems, multi-territory compliance, and increasing calls for reporting clarity. These operational and regulatory demands frequently exceed the capabilities of older, legacy carrier infrastructure.

To thrive in this environment, hybrid fronting carriers require more than rated paper, risk, and capital. They'll need modern infrastructure that can support:

• Real-time digital dashboards provide immediate visibility into MGA, program, and portfolio performance, enabling faster, data-driven underwriting decisions and proactive intervention on underperforming segments. Because hybrid fronts have their own capital at stake, they need this transparency to effectively manage their own risk.
• Data transparency for reinsurers, regulators, and capital partners alike fosters trust, ensures compliance, and strengthens long-term capacity relationships.
• Automated bordereaux processing and streamlined delegated authority workflows reduce operational overhead, boost accuracy, and ensure audit readiness. Further, using a single, shared platform eliminates the need for manual bordereaux reporting, giving hybrid fronting carriers better data and a single source of truth for their reinsurance partners.
• Integration with reinsurers, TPAs, and third-party systems that streamline operations, accelerate speed to market, and enhance collaboration across the entire value chain.
• Scalable platforms that accommodate multi-entity, multi-jurisdiction operations — supporting rapid growth across regions, lines of business, and regulatory regimes without losing control.

With hybrid fronting carriers, technology isn't a support function—it's part of their very structure as an organization. It's an enabler of profitable growth, faster onboarding, and regulator-ready reporting. Carriers that invest early in building this connected foundation will be best positioned to scale. And those that can scale will be the most successful as they court private equity and ILS investors while bringing innovative MGAs and risk products to market.

Many insurers are in an AI pilot purgatory, where promising experiments rarely scale into everyday operations. The models perform adequately, and the business cases hold up. The primary barrier is data architecture. Systems built for reporting and analytics simply cannot support the demands of production AI.

Core insurance decisions depend on synthesizing information from multiple sources. Underwriters assess application data, loss histories, external data, and regulations to evaluate risk. Claims handlers review photos, repair estimates, medical notes, and witness statements to settle cases. Investigators pull together scattered, sometimes conflicting information to pursue recovery. Transforming this expertise into AI capability requires data architecture that supports learning, generation, and contextual requirements.

Why traditional architectures fall short

Organizations have long separated day-to-day transaction systems from analytical warehouses. This division supported dashboards and compliance reporting effectively. However, AI blurs these boundaries because it learns from historical patterns to make real-time operational decisions.

When AI evaluates a new claim, it needs current policy data, historical loss patterns, regulatory requirements, and market conditions simultaneously. It needs real-time transactional data integrated with comprehensive historical context.

Unstructured documents create an even larger hurdle. Applications, claims notes, legal filings, and reports hold the most valuable intel for decision making. Many architectures treat this as a mere storage challenge. AI needs to understand documents at a much deeper level - identifying key elements, mapping connections, and pulling meaning in real time alongside structured records.

This matters most for complex workflows. When AI processes legal documents, filings, or investigation files, it can handle work that once demanded years of specialist knowledge. Document intelligence must sit at the same level as core transactional and analytical data in the architecture to enable this.

What AI-ready data architecture requires

Getting data ready for AI means building four specific capabilities that work together. Each closes a critical gap between how traditional systems operate and what AI applications need to work in production.

1. Bridging architecture and data silos

AI applications need access to policy systems, claims platforms, finance, and external data without long delays. This means real-time operational data alongside historical context, structured tables alongside document content, internal data alongside external feeds. This doesn't mean consolidating everything into one repository or platform. Focus on connecting data where it lives, with clear tracking of its path and origins. This architecture enables AI to navigate existing systems securely with proper lineage and control.

2. Capturing and using expert knowledge

Every time an underwriter overrides an AI suggestion or a claims handler adjusts an estimate, that action contains valuable knowledge. The capability to capture, curate, and organize expert feedback into training datasets separates competitive AI from generic tools. Raw corrections alone aren't sufficient. The architecture must support structured approaches that validate expert feedback, enrich it with context and reasoning, and organize it into training datasets that prevent bias while maximizing learning signal.

3. Managing context data for AI

Experienced underwriters or claims adjusters don't evaluate evidence in isolation. They build a growing understanding as new information arrives, drawing inferences, applying rules, and tracking reasoning. AI needs the same ability: to maintain and evolve understanding throughout a process. Context is this accumulated understanding that an AI system builds, stores, and shares as it works through a process. AI requires context as a managed data type with its own lifecycle, access controls, and transformation rules.

4. Creating data environments for AI development and testing

Moving AI from pilots to production deployment requires infrastructure that can provision realistic environments on demand. The data architecture must support replicating production data with appropriate privacy controls and generating synthetic data for edge cases. As AI programs scale across use cases and product lines, the ability to spin up multiple isolated environments lets teams work in parallel without interference. Provisioning environments quickly with realistic data, then tearing them down when complete, becomes critical infrastructure for scaling AI operations.

Competitive reality

AI will transform insurance operations. Organizations that address these data foundations build compounding advantages: faster decisions, greater accuracy, reduced leakage, and teams freed for higher-impact work.

Start with use cases where the business case is clearest. Focus on the data and capabilities those require, and build incrementally on current investments.

Thirty percent of companies will eliminate remote work this year, and 83% of CEOs globally expect a return to full-time office work in 2027, according to two recent reports. Many insurers will be among those heading back to the status quo pre-COVID. 

But a lot of employees are pushing in the opposite direction. They not only want flexibility on where they work. They want flexibility on when they work. 

We hear all the time about the hundreds of thousands of insurance industry employees reaching retirement age and about all the difficulties in attracting the talent needed to replace them, so I suggest we don't dismiss the desire for time flexibility out of hand. Yes, it runs counter to the management reflex that wants to bring everyone back to the office so they can be seen and managed as a cohesive group. But insurance desperately needs an influx of talent, and, as the saying goes, you attract more bees with honey than vinegar — or, more bluntly, beggars can't be choosers.

Clearly, many parts of the insurance process can't happen whenever an employee chooses to work. Agents and brokers need to be available, for instance, whenever a client needs them. But many underwriters and claims representatives could do their work based on a caseload, rather than on office hours, especially now that generative AI can track down so much of the data for them. 

Whether to offer more flexibility, not less, is worth a thought.

My interest in the topic of flexibility was piqued by a smart column by Matthew Fray at Quartz (which supplied the statistics I quoted in the first sentence). It says:

"Work-life balance has overtaken salary and compensation as the leading priority cited by 65% of office workers globally, up from 59% four years ago, said Peter Miscovich, co-author of the book The Workplace You Need Now, and the executive managing director and global future of work leader at JLL, the commercial real estate giant.

"Employees increasingly value control over when they work such as start and stop times, protected focus blocks, and predictable personal-time boundaries, more than additional workplace location choice, Miscovich said."

I realize I have a bias about flexibility, having worked remotely and pretty much on my own schedule since I left the Wall Street Journal in the mid-'90s. The productivity of a writer is also awfully easy to track. You either produce, or you don't. Even at the WSJ, where I worked office hours, if I went a couple of weeks without a byline, I might get a call that began, "Pa-uu-ll, this is your faaaaather. I'm just calling to make sure my son is still employed." (Thanks so much, Dad.)

But I do think flexibility attracts and retains top talent and is possible in many parts of insurance processes. I'm thinking, in particular, of claims and underwriting. An experience manager knows what a claims rep or underwriter should be able to handle, not just based on the number of cases but on their complexity, so it should be possible to let them work largely on their own time in their own place. I'm sure other processes can allow for at least some additional measure of flexibility, too.

People should still come to the office for socialization purposes. Training of newbies probably needs to be largely done side by side. And anything that requires frequent interaction between employees obviously needs to be done in the same place at the same time — Zoom eliminates some of the need for being in the same place but by no means all. 

I realize that, in many types of jobs, there's a fear that employees will slack off if they're not under close supervision, and that surely happens. But we also see how compulsive people can be about keeping up with their email and other work even during off-hours, so I'd bet some people — especially the talented and ambitious — would work even harder if motivated by more flexibility. 

I harken back to an interview I did with Scott McNealy, at the time the CEO of Sun Microsystems, in 2001. In the days before everyone had a laptop they carted to and from work, McNealy had spent quite a bit of money buying home computers for his 40,000 employees. He caught some grief for the expense but seemed to me to have a pretty good justification.

"I do not want somebody at 10 o'clock at night who can't sleep, who wants to work because there's nothing good on TV, to not have the full capability to do everything he needs to do to get the job done," McNealy said. 

Worth a try?

Cheers,

Paul