As property insurance markets continue to harden, brokers and agents face an increasingly challenging landscape when seeking coverage for clients with water damage history or aging infrastructure. Some properties have experienced such severe claims that they've become what some in the industry refer to as uninsurable or severely impaired, where the challenge is not about getting an affordable premium but simply being able to get insurance coverage in the first place.

However, innovative technology solutions are emerging that can help transform these high-risk properties into insurable assets, providing brokers with powerful tools to secure coverage and favorable terms for their most challenging clients.

The Scale of the Water Damage Problem

Non-weather-related water damage accounts for approximately $13 billion in annual property insurance losses in the U.S., making it one of the most significant drivers of property insurance claims, with claims over $50,000 doubling since 2015. In commercial and multi-unit residential buildings, the effects are even more severe. The average water leak in New York City costs between $65,000 and $95,000, with damage often cascading through multiple floors before detection.

A 26-story mid-town condominium experienced this firsthand when multiple water incidents led to their insurance premiums skyrocketing from $150,000 annually to $850,000. Properties with such claim histories find themselves in the excess insurance market, where top-tier insurers simply won't provide coverage, forcing owners to alternative carriers with significantly higher costs and reduced service levels.

Point-of-Leak Detection as Market Leverage

The emergence of reliable point-of-leak detection technology has created opportunities for brokers to present compelling risk mitigation cases to underwriters. Unlike flow-based systems that attempt to detect unusual water usage patterns across entire buildings, point-of-leak sensors are placed directly at high-risk locations where leaks typically originate.

These systems can detect water within seconds and initiate live operator calls to building management within a minute, providing specific location information such as, "There's a leak under the washing machine in Apartment 4J." This immediate response capability fundamentally changes the risk profile of a property.

The proof is in the performance data. For example, in 2024, ProSentry systems caught 3,610 leaks with zero false alarms and zero insurance claims. This track record demonstrates to underwriters that properly implemented detection systems can virtually eliminate water damage claims.

Implementation Blueprint for Brokers

When presenting smart leak detection solutions to clients and underwriters, brokers should focus on three key elements:

Comprehensive Coverage: Effective systems must provide building-wide protection, not just individual unit monitoring. Leaks can originate in neighboring units or several floors above, and standalone detectors are often unable to detect or alert to those risks. Insurers increasingly recognize that comprehensive monitoring that adapts to real-world building dynamics is essential for meaningful risk reduction.

Professional Monitoring: While consumer-grade products from big box retailers may offer basic detection, insurers increasingly prioritize full-building solutions that go far beyond app notifications. Systems with 24/7 monitoring services and live operator calls promote immediate response, often supported by building staff.

Automatic Response Capabilities: Advanced systems include automatic shutoff valves that can isolate water sources when leaks are detected. While complete building shutoffs aren't practical for some multi-tenant properties, strategic valve placement allows for isolated response - if a leak occurs, water to that specific area can be shut off while building staff responds.

Quantifiable Insurance Benefits

The insurance industry has begun formally recognizing these risk mitigation investments. Some major insurers now offer premium discounts or lower deductibles for buildings with comprehensive leak detection systems, representing industry acknowledgment that properly implemented detection systems significantly reduce claim risk.

Beyond premium discounts, these systems help properties escape the uninsurable or severely impaired designation. For example, the 26-story mid-town condominium, after implementing comprehensive detection, saw their next insurance premium reduced by $300,000, with their broker successfully arguing to carriers that "We've done our best to mitigate any kind of risk."

Comprehensive Risk Management Approach

Another recent example of implementing risk management to lower property insurance premiums is from a leading Atlantic City casino hotel. Facing a challenging $5 million premium increase over five years due to water damage claims, the hotel casino invested less than $70,000 in comprehensive monitoring systems and ultimately saved close to $5 million through increased carrier competition and improved terms.

The most effective approach combines water detection with broader building monitoring capabilities. Modern platforms can monitor for gas leaks, oil leaks, temperature fluctuations, humidity levels, mechanical malfunctions, and even unauthorized smoking or vaping. This comprehensive approach demonstrates to underwriters a commitment to holistic risk management and property protection.

Moving Forward in a Challenging Market

As insurance markets continue to tighten, brokers who can present clients with concrete risk mitigation strategies gain significant competitive advantages. What's more, some in the industry are already anticipating that we'll soon get to the point where insurance companies will require these systems whether or not a building has a history of water leaks.

Smart leak detection technology provides brokers with a powerful tool to transform previously uninsurable properties into attractive risks. By demonstrating risk management through quantifiable protection measures, brokers can secure coverage for challenging clients while positioning themselves as innovative solution providers in an increasingly difficult market.

The key is presenting these systems not as additional costs but as insurance enablement tools that open doors to coverage and favorable terms that would otherwise be impossible to achieve.

When geopolitical shockwaves hit, insurance systems face their true test — not only of capital or claims capacity but of operational resilience and leadership.

Ukraine's insurance industry entered 2022 under structural pressure: low market penetration, price-led competition, and fragmented digital infrastructure. Then came the full-scale war.

Yet the system didn't collapse. It adapted. Unevenly — but significantly.

Global Context: Insurance's Digital Decade

In parallel, global insurance is rapidly evolving:

• AI-first underwriting and straight-through processing are becoming industry standards.
• Usage-based and embedded models are gaining traction.
• New lines — cyber, climate, ESG — are outpacing traditional coverage.
• Insurtech and big techs alike are reshaping distribution and service delivery.

The global North optimizes for personalization and real-time responsiveness. But Ukraine faced a different imperative: survival, and then reinvention.

Ukraine: Insurance Under Wartime Conditions

Despite extreme disruptions, Ukraine's insurers remained operational — from frontline cities to displaced branch networks.

Key observations from 2022–2024:

• While overall premiums dropped, stability returned in auto and property.
• Claims settlement remained functional, with some delays.
• Customer priorities shifted toward reliability and actual coverage value.
• Risks extended beyond combat zones — supply chains, credit risk, and labor mobility posed additional pressure.
• Reinsurance capacity became harder to secure and required localized analysis.

The insurance sector's continued functionality became a form of national infrastructure — silent but vital.

ARSENAL Insurance: Case Study in Adaptation

At ARSENAL Insurance, we reframed the crisis as a transformation accelerator.

1. Portfolio Strategy Shift

From volume growth to risk-adjusted quality. Our underwriting models evolved to integrate both traditional indicators and real-time signals from dynamic environments.

2. Digital Transformation With Measurable ROI

We avoided tech vanity projects and focused on functionality:

• Automated policy issuance and calculators
• Back-end straight-through-processing architecture
• AI-driven claims triage and document verification

3. Empathy as Infrastructure

Clients often contacted us from basements, checkpoints, or in transit. We trained teams to provide clarity and reassurance under pressure. Tone and timing mattered as much as process.

4. Risk Analytics for Unstable Contexts

We implemented adaptive exposure models, tracked region-specific loss curves, and aligned products with updated behavioral data.

Lessons for Emerging and Crisis-Affected Markets

Ukraine's case demonstrates that insurance systems can withstand systemic shocks — but only with:

• Leadership that prioritizes action amid uncertainty
• Investment in adaptive technology, not just digitalization
• Customer communication and support
• Localized, flexible underwriting frameworks

Most importantly: resilience isn't passive endurance — it's structured responsiveness.

Looking Ahead: From Continuity to Redesign

The Ukrainian market will not "return to normal." It will evolve — shaped by new expectations, tighter capital discipline, and renewed urgency around trust.

We see this moment as not recovery but reinvention.

As recently as a decade ago, most organizations and individuals thought of their insurance policies as just that—an insurance policy. In other words, they typically only considered insurance as a reaction to an incident, and not a way to reduce risk.

However, times are changing. In its latest national survey, "Risk Radar Report—Insurance Pulse Check," Church Mutual Insurance found that 63% of U.S. adults have reviewed their property coverage in the past six months. This is driven by increasing concerns about coverage gaps amid rising costs and severe weather.

The report indicates policyholders are looking for their insurers to clarify coverage and help them reduce risk. Ideally, insurance companies will establish a relationship with their customers that allows them to address potential problems before those problems lead to a claim.

Identifying and mitigating risks

The key to this relationship between insurance companies and their customers is in identifying and mitigating risks to the properties. In the report, more than half of consumers (56%) say they would explore ways to protect their property and avoid a loss.

Customers can't do this alone. They need the guidance of trained, experienced insurance professionals who can help them spot issues that could lead to damage from storms or other catastrophes. For example: curled or missing shingles, vegetation that is too close to the building (and thus a risk in the event of a wildfire), and clogged gutters that could prevent drainage.

But merely pointing out these risks isn't enough; insurance professionals should be taking the time to show their customers how the risks can lead to major property damage. This is where the numbers come in. Statistics speak volumes, and insurance companies certainly have data that show why risk management is so important. They need to use this data to paint a vivid picture for policyholders—and, indeed, the survey indicates consumers want and need these honest conversations with their insurance company representatives.

Review of coverage

Not only do consumers need to know how they can mitigate risk, but they also need—and want—a better understanding of what their policy covers. Additionally, they need to know the application of policy terms and conditions if they should need to file a claim.

Because of inflation and the rising costs of building materials, repairs and renovations cost much more than they did even 10 years ago. The result of this increase in costs is that many individuals and organizations may believe they hold the right amount of insurance, when in fact it is not enough. This often becomes apparent only when they are making a claim and are surprised to find they are underinsured.

But the survey signals consumers want to review their property insurance before they need to make a claim. This approach is a win-win for both the insurance company and the policyholder.

Both individuals and organizations are taking more ownership of their insurance policies than ever before. It is up to insurance companies, brokers and agents to meet them where they are, providing timely updates, appropriate options and cost-saving risk reduction strategies.

"Trust is the glue of life. It's the most essential ingredient in effective communication. It's the foundational principle that holds all relationships."

— Stephen M.R. Covey

Today, with the rapid advancement of AI technology, the role and methods of establishing trust in insurance transactions are undergoing profound changes. The insurance industry is in a period of technology-driven transformation, where AI applications not only improve operational efficiency but also reshape customer interactions and trust-building processes.

Rachel Botsman (Note 1), a expert in contemporary trust studies, explores in her book "Who Can You Trust?" how technology is reshaping human trust relationships. This includes how technology alters our understanding and demand for trust, how sharing economy platforms build trust through user ratings and feedback systems, and how blockchain enables trust without intermediaries. In the article "[Experiment 23] AI and Trust: The Future of the Insurance Industry" (Note 2), we propose a detailed framework for constructing and managing trust, examining how AI can establish and maintain trust with customers in the insurance sector. The content covers iterative exploration, the setup of user trust systems, how to ensure objectivity in subjective evaluations, and considerations for AI implementation. The integration of theory and practice offers new perspectives on "how to build trust in insurance transactions in the AI era."

Using examples from the sharing economy, such as ride-hailing and food delivery, we illustrate that platforms do not provide trust as a product. Customers do not inherently trust service providers but choose platforms for their convenience, speed, and efficiency, opting for new trust mechanisms over traditional ones. Transparency can address information asymmetry, but it is not synonymous with trust. Technology does not replace trust; instead, it can strengthen and inspire it. Technology disrupts traditional definitions and relationships of trust, paving the way for new ones.

Drawing on Botsman's definition of trust and the evolution of trust systems, we introduce the concepts of "trust risk" and "trust leap." We map these ideas to the information asymmetry in insurance transactions and explain why face-to-face and non-face-to-face sales differ significantly in customer acceptance, marketing, and service delivery. Rather than aiming to "rebuild trust," it is more accurate to recognize the contextual and personalized nature of trust.

We analyze how to build a trustworthy AI system through four key aspects: "core elements of trust, quantifying trust, technology and transparency, and continuing customer interaction." The best way to address a trust crisis is to make oneself "trustworthy," characterized by four traits: competence, reliability, integrity, and benevolence. From this, we deduce how agents can earn customer trust through "five layers of verification." In quantifying trust, we reiterate the core idea of the "trust formula": Trust = (Reliability × Honesty × Competence) × Time / Interaction. Using the "prisoner's dilemma," we demonstrate that the intent behind transparency is key to solving trust issues. With AI's assistance, continuing customer interaction allows trust to evolve iteratively.

1. Is Technology Replacing Trust?

Most of us in China have used ride-hailing apps like DiDi (provides services like Uber) or ordered food on Meituan (provides services like UberEat). We neither know the drivers nor the restaurants, so why are we willing to consume on these platforms? Is it because of trust?

DiDi's product is not trust but efficiency and transparency. It improves efficiency through optimized transportation services and transparency by displaying passenger ratings for drivers and the number of trips completed. The efficiency, convenience, and speed brought by technology do not enhance trust; instead, we sacrifice trust in service providers to gain these benefits. Transparency, while important, is not equivalent to trust but a means to achieve it. Product and service designs must prioritize transparency, but more crucially, the "intent" behind transparency and whether it aligns with consumers' expectations. For example, is the intent to sell products or to empower consumers to make informed choices? When transparency fails to align with intent, trust is hard to establish.

Returning to our example, we do not know if a DiDi driver is skilled, familiar with the city, or truly reliable. However, these concerns are alleviated by the platform's control mechanisms (rating systems, optimized routes, real-time vehicle tracking, emergency alerts, etc.), which reflect alignment in transparency intent between the platform and users. While these mechanisms limit drivers' freedom, they increase predictability for passengers, fostering a sense of security.

Thus, technology does not evolve trust by providing more information but by restricting our freedom (Note 3). Monitoring technologies make the unknown more predictable, so the focus should not be on rejecting technology but on ensuring it does not replace trust. Instead, technology can strengthen and inspire trust. Traditional definitions and relationships of trust have been disrupted by technology, and new ones are being established.

2. The Definition and Evolution of Trust

In the insurance industry, trust is not only the foundation of transactions but also the bond that sustains long-term customer relationships. To understand trust and how to build it in the AI era, let us first "deconstruct" trust.

Botsman defines "trust" as a bridge between the known and the unknown, a relationship of confidence we establish with the unfamiliar. This explains why trust is intertwined with fear, hope, and expectation, and why its violation leads to frustration and backlash, such as when internet platforms exploit technology for price discrimination. Trust has evolved from local trust (relying on acquaintances and communities) to institutional trust (relying on corporations and governments) and now to distributed trust (relying on technology platforms and algorithms), as seen in the earlier examples of internet platforms.

As shown below, the uncertainty between "people or things" and "unfamiliar people or things" is "trust risk," and the process of bridging this gap is the "trust leap." Technology is making trust-building increasingly reliant on transparency, reliability, and user feedback systems provided by technology, narrowing the trust risk gap and accelerating the trust leap. For example, when people shift from traditional insurance services to AI-driven platforms, this shift depends on the platform's transparent operations and reliable services, thereby addressing the information asymmetry inherent in traditional insurance sales.

However, as mentioned earlier, users do not choose platforms because they trust them but because platforms create new rules of trust, enabling users to prioritize convenience and efficiency and complete transactions in non-traditional, autonomous ways. Thus, trust is context-dependent and highly subjective, particularly evident in insurance sales. For instance, "face-to-face sales" by agents and bancassurance differ significantly from "non-face-to-face sales" via telemarketing and online channels in terms of customer acceptance, marketing, and service delivery.

In the past, we often spoke of "rebuilding trust," but this perspective is insufficient and institution-centric. Instead of focusing on rebuilding trust, we should recognize its contextual and personalized nature and consider how technology can enhance it.

3. Building a Trustworthy AI System

How can we build a trustworthy AI system? We analyze this through four aspects: "core elements of trust, quantifying trust, technology and transparency, and continuing customer interaction".

3.1 Core Elements of Trust

Botsman argues that the best way to address a trust crisis is to make oneself "trustworthy." "Trustworthiness" has four traits (see image below): competence, reliability, integrity, and benevolence. Competence refers to the ability to fulfill promises; reliability is consistency over time; integrity encompasses honesty, fairness, and alignment of intent; and benevolence represents empathy and care.

These traits form the foundation of trust in individuals or organizations. In the insurance industry, they are equally applicable. Insurers must demonstrate competence in risk assessment, claims handling, service consistency, and customer care to earn trust.

Similarly, as insurance agents, how can we showcase these traits to earn customer trust? Building trust requires passing the consumer's "five layers of verification." When an agent conducts cold calls or visits to complete a sale, how can they gradually build trust from a starting point of "zero trust"? What processes and mechanisms are involved? First, we must view the problem from the consumer's perspective, not the agent's or institution's. In practice, beyond what agents can do themselves, we consider using AI as an agent's assistant (copilot) to serve consumers via online and telecommunication channels, aiming to pass the "five layers of verification." Below is an example of how AI and agents can collaborate to build trust:

FIVE LAYERS OF VERIFICATION

1. Identity Verification:

Who am I? Who do I represent? Who recommended me? Integrity AI uses technology like facial recognition and big data analysis to quickly verify the agent's identity, ensuring authenticity and credibility. The agent should clearly introduce themselves, provide credentials and company information, and offer referral details to enhance trust.

2. Qualification Verification:

What are my credentials? What certifications do I hold? What endorsements do I have? Competence AI can access online databases to verify the agent's certifications and training history, providing real-time results. The agent should display their qualifications and training records, share official endorsements, and strengthen consumer trust.

3. Data Verification:

Is consumer data obtained compliantly? How is it obtained? Are sensitive details hidden? Integrity AI can automatically monitor data collection and processing for compliance, ensuring all consumer information is obtained and used legally and transparently. The agent should explain the data collection process and legal basis, detail privacy protection measures, and demonstrate safeguards.

4. Motivation Verification:

Why am I calling or visiting? Will I provide follow-up service? Is there a complaint channel? Benevolence AI can analyze the agent's calls and behavior patterns to assess the genuineness of their intent and provide details on follow-up services and complaint channels. The agent should clearly state the purpose of contact and future service plans, offer detailed complaint procedures, and ensure quick resolution for consumers.

5. Process Verification:

Is the call recorded? Is the process reliable? Reliability AI can monitor the entire call or visit, provide recording functions, and document the process for later verification. The agent should inform consumers about recording arrangements, explain the purpose, and provide complete records to enhance trust.

Therefore, when institutions develop responsible AI to support agents (as users), they must understand that when we ask machines to perform tasks, we only need to focus on AI's competence and reliability. But when machines make decisions for us, we must also consider AI's integrity and benevolence — what we often call ethics. Moreover, just as platforms "exchange" passengers' trust demands by restricting drivers' freedom, we must consider using AI to supervise the entire transaction process, which protects both parties and facilitates the transaction.

However, while competence and reliability can be objectively evaluated, integrity and benevolence are subjective perceptions and emotional alignments, making them harder to structure and quantify.

3.2 Quantifying Trust

After understanding the core elements of trust, we explore how to quantify them. In [Experiment 23], we proposed methods to structure and quantify trust, enabling the creation of a structured trust framework. Below is a demonstration of building a "trust framework":

In [Experiment 23], we gradually explored and concretized the process of "trust AI-ification," distilling the "Trust Formula: Trust = (Reliability × Honesty × Competence) × Time / Interaction." This showcases AI's potential in handling complex concepts while revealing the multifaceted factors involved in building a trust framework, helping us further quantify and digitize trust. For details, readers may refer to the original article.

3.3 Technology and Transparency

As mentioned earlier, transparency is a key factor in building trust, but it is not synonymous with trust. More importantly, it is the intent behind transparency. In the AI era, when we "outsource" trust to algorithms, we must understand the machine's intent (i.e., the institution's intent behind developing the machine). Institutions must ensure consumers or users understand the intent behind algorithms.

Transparency can rebalance information asymmetry. As shown below, in the prisoner's dilemma, if one player knows the other's decision, trust becomes irrelevant. As Diego Gambetta said, "If we had unlimited computational power to map all possible contingencies, trust would not be an issue."

Insurers must ensure their AI systems operate transparently and clearly communicate the intent behind these operations to ensure users (including agents and customers) understand and accept them. For example, transparent algorithms and open feedback mechanisms can help customers better understand insurance products and services, thereby enhancing trust in the company.

While technology can improve transparency, trust remains context-dependent and highly subjective. It cannot yet be fully automated through technology or resolved solely through compliance and regulation. Thus, the most suitable model remains human-machine collaboration.

3.4 Continuing Customer Interaction

Trust is a dynamic process requiring customer interaction and feedback mechanisms to iteratively "upgrade" it. Insurers can regularly collect surveys and feedback to adjust and optimize services and products, ensuring they meet user (agent and customer) needs and expectations.

Focusing on customers, businesses can achieve continuing interaction through regular feedback collection, personalized communication strategies, round-the-clock support, loyalty programs, and community engagement. AI plays a pivotal role here, improving efficiency, personalizing services, and deeply analyzing customer data to better understand and meet needs.

For example, AI can analyze vast amounts of customer feedback to identify common issues and improvement opportunities, enabling faster adjustments. Natural language processing and causal AI can categorize open-ended feedback and propose solutions. AI can also create detailed customer profiles with smart tags, driving personalized recommendations based on context, preferences, and goals.

AI-powered chatbots can provide 24/7 support, handling queries or complaints instantly and escalating complex issues to human agents, improving overall efficiency and satisfaction.

AI can also design personalized rewards programs based on purchasing behavior, predict churn risks, and take preventive measures to retain customers. Additionally, AI can monitor community interactions, identify active users and common problems, and support offline event planning.

For the Chinese insurance market, Oliver Wyman proposes that building multi-layered trust relationships is central to realizing the blueprint for the life insurance sector (see image below).

This image shows how insurers can enhance customer interaction and service quality through multi-layered trust relationships. This multi-channel, multi-layered approach aligns closely with the key strategies discussed here for building and maintaining trust in insurance transactions in the AI era. For example, insurers can use AI to achieve seamless communication with customers, complete the five layers of verification, and build trust frameworks. Customers can access professional services through multiple channels and modalities, enjoying personalized communication and recommendations.

Conclusion

Trust is a complex, multi-faceted concept involving psychological, behavioral, and technological factors. Botsman defines trust as "a relationship of confidence with the unknown," explaining why it encompasses fear, hope, and expectation. To build and maintain this relationship, we must understand its core elements: competence, reliability, integrity, and benevolence. Additionally, the five layers of verification — identity, qualification, data, motivation, and process — are critical steps in constructing trust. Through trust frameworks and formulas, we can quantify and optimize the trust-building process.

Trust is at the heart of insurance transactions. Through previous articles, we have explored trust from various angles to comprehensively understand and apply these concepts. For example, in "After 3.5%: What's Next for the Insurance Industry?" (Note 4), we noted that agents must shift from "selling insurance" to "selling good insurance," with future technology serving as AI assistants to intervene in sales behaviors and correct shortcomings of purely human approaches. The strategy is: Use AI pre-sale to identify needs, ensuring customers buy with clarity; use AI during sales to reduce misrepresentation, ensuring customers buy with confidence; and use AI post-sale for service, ensuring satisfaction and referrals.

In "How AI Empowers Insurance Sales" (Note 5), we emphasized that AI assistants will replace traditional tools, with future transactions involving interactions between AI assistants representing consumers and agents. The focus will be on whose AI is stronger, more user-aware, and more trusted.

In "Vertical AI-Driven Insurance Sales Innovation: Unlocking Industry Pain Points and Strategic Turning Points" (Note 6), we explored AI's potential to identify and solve insurance pain points, proposing immersive experiences and strategic turning points to break trust barriers. Killer apps can address long-standing pain points and dismantle trust barriers.

In "From Insurance to Tech: The Transformative Power of Shifting from "Push and Pull" to "Self-Drive"" (Note 7), we discussed the real issues in insurance, noting that it is a long-term transaction requiring methods to rebuild consumer trust and uphold long-termism, ultimately creating a quality sales environment.

In "Smart Customer Experience: AI's Innovative Applications in Insurance Services" (Note 8), we highlighted that AI-era services are value-centric. AI helps businesses better understand needs and deliver personalized, intelligent services. The "customer autonomy" concept shifts service paradigms toward value, giving adhering businesses a competitive edge.

Ultimately, trust lies with each of us — we decide whom to trust. Healthy skepticism is constructive, driving continuous improvement. As Warren Buffett said, "It takes 20 years to build a reputation and five minutes to ruin it. If you think about that, you'll do things differently." By exploring trust from multiple angles, we better understand how to build and maintain trust in the AI era, standing out in a complex market. Future insurers must continuously monitor technological advancements and customer needs to remain competitive and achieve sustainable growth.

References:

1. Trust in the Digital Age: Why It Matters Now More Than Ever – Rachel Botsman

https://www.youtube.com/watch?v=cdOGqZz6Lqc&t=2961s

2. [Experiment 23] AI and Trust: The Future of the Insurance Industry

https://mp.weixin.qq.com/s/cLpa0BSKSkWeb3zlrsjqrQ

3. The Obsolescence of Trust – Hubert Beroche

https://medium.com/urban-ai/the-obsolescence-of-trust-b33c5d9fe76b

4. After 3.5%: What's Next for the Insurance Industry?

https://mp.weixin.qq.com/s/3ScD_QlhKeLHEeu0UwDe5g

5. How AI Empowers Insurance Sales

https://mp.weixin.qq.com/s/pmEvWEl-Ytp5hqHtP8ZQzA

6. Vertical AI-Driven Insurance Sales Innovation: Unlocking Industry Pain Points and Strategic Turning Points

https://mp.weixin.qq.com/s/dcXt07m4WqAmPmGb0Bjthg

7. From Insurance to Tech: The Transformative Power of Shifting from "Push and Pull" to "Self-Drive"

https://mp.weixin.qq.com/s/1OXxl1cWumi56EaWOoKi3w

8. Smart Customer Experience: AI's Innovative Applications in Insurance Services

https://mp.weixin.qq.com/s/gzcUTvdfX3xM1E5YNaEBEA

In 2025, underwriting is at a pivotal moment. The traditional image of underwriters buried under mountains of paperwork is fading, replaced by a new reality: a deluge of data. Today's professionals are navigating an overwhelming flood of information from disparate sources—IoT sensors, social media, satellite images, and traditional reports. All while grappling with outdated, siloed systems. The challenge isn't the lack of data, but rather making sense of it. Underwriters are tasked with extracting valuable insights to accurately assess risk, but the sheer volume and fragmentation of data often hinder, rather than help, their efforts.

Success ultimately depends on understanding the data and its context to close the right business, faster. Organizations that use AI and process orchestration to surface hidden risks and address blind spots can gain an advantage in assessments.

The data integration dilemma

Despite the availability of data, many insurers still operate with a fragmented view of applicant risk. According to McKinsey research, the commercial P&C insurance industry has underinvested in technology, leaving staff to spend between 30% to 40% of their time on administrative tasks, such as rekeying data.

Underwriters lose valuable analysis time chasing down documents across systems and sifting through emails, spreadsheets, and PDFs. While experienced underwriters often know what to look for and can quickly identify gaps, many of these professionals are nearing retirement. The National Association of Mutual Insurance Companies projects that up to 50% of current underwriters will retire by 2028, and fewer new professionals are entering the field. Employers must find ways to retain that institutional knowledge to set up new hires for success.

Where AI can move the needle

AI is beginning to close the gap between what is theoretically possible in underwriting and what is practically achievable when partnered with a human expert.

Consider a common life insurance scenario: An underwriter receives an attending physician statement spanning 1,500 pages or more. A person may need hours or days to review such a document. However, AI can scan the document in seconds, extract relevant details, flag anomalies, and present a clear summary for human review.

Beyond efficiency, AI brings consistency across a staff of varying experience levels. It supports newer underwriters in making well-informed decisions by flagging missing information, spotting patterns from previous cases, and suggesting next steps. The collective use by staff also helps fine-tune the AI tools, making them more effective over time.

However, highly regulated industries, including insurance, must implement AI responsibly and transparently. The most effective approach embeds AI into defined process workflows where every action is traceable, explainable, and auditable.

Real-world applications of underwriting modernization

Aviva was founded in 1996, but its roots go back over 320 years and encompass more than 750 insurance institutions. Each absorbed acquisition brought its own products, systems, and silos for the company to deal with. Aviva's pension business felt this most acutely until it automated over 160 processes and consolidated 22 legacy systems into one streamlined interface. However, the company didn't want the expense of ripping and replacing each system. Instead, Aviva opted for a unified platform, robotic process automation, and digital signature tools, which now executes more than 3.8 million automated transactions every year with a 99% success rate. Pension transfer requests that once took weeks can be completed in hours.

Streamlining back-office functions had a direct impact on front-line service, with the call center benefiting most immediately. Agents no longer had to dig through each legacy system to answer customer questions because the platform offers them a 360-degree view of all their products. Customer service response times were nine times faster than the old setup, and query calls dropped by 90%. The company also saw a 40% reduction in operational costs.

CNA, one of the largest commercial insurers in the U.S., is known for its high-touch service. But managing its multinational business proved difficult across time zones, languages, and disconnected systems. Underwriters faced delays digging through emails, Excel files, Lotus Notes, and SharePoint just to find the information they needed.

To solve this, CNA launched a low-code, cloud-based platform that unifies the entire servicing process, infusing it with data integration and automation. Now, whether in Shanghai or Los Angeles, employees access the same real-time data. Since the platform's launch, CNA saw a 60% reduction in processing time for thousands of local insurance transactions and between 20 and 30% annual growth in the number of international policies it manages.

In each case, underwriters now focus on higher-value work—like client engagement, pricing strategy, and program innovation—rather than chasing down data across systems.

Underwriting at a crossroads

Underwriting is evolving from an industry powered by static data and institutional memory to one based on real-time intelligence and system-wide visibility.

This evolution enables underwriters—regardless of experience level—to work more efficiently, make better decisions, and operate more consistently. Well-designed tools do not just automate manual tasks; they expand what underwriters can achieve.

Insurers leading this transformation are already seeing results: faster quote-to-bid cycles, deeper risk insights, and better customer outcomes. Ultimately, these improvements translate to closing more of the right business, scalability for market expansion, and providing the right tools for underwriting success.

I'll be quick this week as long as the U.S. seems to already be slipping into a looong Fourth of July weekend. I'm about to start packing my car and don't want to delay anyone else from doing so, too.

I just want to note that the "insurtech spring" possibility that I raised in a commentary in March seems to be well under way, based on recent funding rounds, a high-profile IPO and the continued stock market success of the high-profile, full-stack insurtechs. 

While I think my earlier analysis holds up pretty well, the more recent evidence points to some important trends about where insurtechs and, more broadly, innovation in insurance is heading.

First, the evidence:

• Pitchbook reports that VC investing in insurtechs surged 65% in the first quarter. Admittedly, that's up from a rather small base, but it still reflects enthusiasm, especially for innovation in claims and underwriting, after a long series of declines. Pitchbook singles out Assured, a claims automation platform that secured $23.3 million in a Series B round.
• Slide Insurance went public in June and quickly achieved a market cap north of $2 billion. It's a bit of a one-off, given that it's mostly picking through policies that Florida's Citizens Property Insurance wants to shed after years of having to support the state's dysfunctional homeowners market. The company is also controversial because its CEO and his wife, also a company officer, took home more than $50 million in 2024 at the startup, while Florida homeowners are struggling. Whether Slide has separated the wheat from the chaff remains to be seen, but the enthusiasm for an insurtech IPO is undeniable.
• The marquee names among insurtech carriers continue to do well after years of struggle. Lemonade stock price is almost 2 1/2 times what it was in October. Hippo is up 86% since then. Root's price is 3.2 times where it was in October. 

As I said in March, that AI is leading the current round of innovation means VC investments don't have to be huge. While those developing the large language models for generative AI will spend some $320 billion this year on capital expenditures -- Nvidia chips aren't cheap -- the rest of us get to basically plug into AI as we would a wall socket.  

Still, even if the insurtec investment numbers aren't overwhelming, my mantra for decades has been, "Nobody is as smart as everybody," so I believe that even the biggest, smartest insurance companies shouldn't assume they're going to get everything right in this fertile stretch. Venture capitalists certainly won't assume that, so they're going to finance startups that seem to have an unusual insight into a market or a technology. The smart insurers will keep their eyes wide open and buy from, partner with, or simply acquire those with promising propositions that internal teams didn't pursue. 

Pitchbook is right to highlight claims and underwriting. Generative AI can provide huge gains there, starting with operational efficiency and moving well beyond. 

I'd add agencies and brokerages to that list. They're already using generative AI to radically improve productivity and are branching out into agent copilot sorts of scenarios, but I'm sure some smart entrepreneurs have ideas to add to the mix. 

I imagine parametric and embedded insurance will see considerable insurtech innovation, too. Some of the innovation may have to happen at the incumbents. The appeal of parametric, for instance, is often as part of a stack -- a policyholder wants to get some money immediately, knowing that working through a claim on, say, crop failure will take a long time -- and major carriers are well-positioned to offer that sort of coverage. But insurtechs will surely find ways to augment what carriers are doing both with parametric insurance and with insurance embedded into purchases that the incumbents don't currently recognize as opportunities. 

So here's to an insurtech summer -- just with less heat and humidity, please.

Now I'm off to pack my car on my way to what should be a great time with my many siblings and our families at the New Jersey shore. I hope you have a great loooong weekend, too.

Cheers,

Paul 

P.S. If you're interested in a deeper dive on the rationale for an insurtech boom, here is a very smart piece by Teddy Himler of Optimist Ventures, who is the one who put me on to the "insurtech spring" idea in the first place. 

Paul Carroll

I’ve been intrigued by the Internet of Things since I first heard the term, maybe 15 years ago. How have its capabilities and uses evolved in the insurance industry?

Nga Phan

IoT has become one of the major trends driving technological innovation, and the intersection with insurance has been particularly fascinating, as it addresses the longstanding challenge of identifying concrete, tangible value from IoT devices.

At Bolt, we've found that IoT integrated with insurance provides that valuable opportunity. We've implemented water sensors with HO3 insurance, coupled with comprehensive prevention and protection services. This approach shifts the paradigm from simply repairing or replacing after damage occurs to preventing and protecting against potential issues. This represents a fundamental change in how insurance can work in the connected age.

Paul Carroll

I think there’s a ton of potential for sensors that can alert property owners to leaks and perhaps even automatically shut off water. How does your program work?

Nga Phan

We focus on water damage specifically for homeowners insurance. This involves attritional water losses – damage that occurs due to water issues.

Water damage accounts for approximately 40% of the premium, which many people may not realize. Once water damage occurs, it can actually represent the majority of losses for a home or property.

Paul Carroll

How have water sensors developed over the last five to 10 years, and what advancements do you anticipate in the next three to five years?

Nga Phan

Sensors for water leaks and other applications continue to improve each year. They are more powerful, cheaper, and easier to handle, deploy, install, and extract data from. They're also easier to replace and integrate with other workflows within homes or with insurance carriers.

These advancements collectively ensure that prevention technology becomes more scalable. For our program specifically, the decreased cost and simplified deployment means more homes can access this technology. Homeowners are more compliant in deploying sensors and maintaining them throughout the policy life.

This creates opportunities to integrate with more devices and incorporate their data directly into policy underwriting. We're witnessing the scalability of this technology and program right now, benefiting from many years of advancements in IoT technology. 

Looking forward three to five years, devices will likely become even smarter while continuing to decrease in cost. They will probably collect more diverse data about the home, expanding the scope of monitoring beyond leak detection to include water flow and humidity tracking—essentially capturing leading indicators of potential loss events.

Future devices will transmit data more in real time and in larger volumes, leading to better insights into water flow patterns or leak event patterns. This will enable more comprehensive preventative programs for carriers. By analyzing their portfolio data, they might see that properties of certain types or ages or with specific pipe types are more prone to particular kinds of damage.

Carriers could also combine this rich sensor data with environmental metadata. Homes in specific areas of Delaware, Florida, or elsewhere might show certain damage patterns that can be prevented. The combination of more powerful sensors collecting and sharing more data in real time with other data types will provide opportunities to apply intelligence toward more preventative programs.

Paul Carroll

Help me visualize this. What does participation in your program look like from the homeowner's and carrier's perspectives?

Nga Phan

Let's say you own a home in Florida. We know that in Florida it is really hard to get homeowners insurance. Having a program like this, which reduces attritional water damage, gives carriers more options to provide coverage within that area. As a homeowner, I now have access to homeowner insurance even in places where it's hard to find.

Secondly, because of this program, I am able to receive reduced rates, making protection more affordable for me as a homeowner. Because deployed sensors must be kept active, I know that I am being actively protected and that big losses will be prevented due to the alerts I receive and the associated emergency response provided as part of the program.

If a loss does occur, because of the alerts and prevention measures, that loss will probably be smaller than it would otherwise be. That's another benefit to me as a homeowner.

To the carriers, there are several advantages. They continue to be able to participate in markets they might otherwise avoid because they can now make it economical to cover these areas. This means they've got larger access to potential new policyholders, which is a way to drive growth.

We’ve seen a significant reduction in losses, with up to 55% total premium impact. More than 40% of that comes from avoided loss events, and the remainder is driven by reduced severity, which can be as much as 28%. These are tangible, verified results that carriers can expect when incorporating this program into their underwriting strategy.

Carriers are also going to be viewed as more innovative, improving their brand image and relationship with customers because they're now participating in prevention. This translates into a better customer experience overall and will lead to better renewal rates and retention for the carriers.

Paul Carroll

What does the home water monitoring system look like physically, and how does the information flow from the sensors to insurance carriers? The ones I’m most familiar with look sort of like hockey pucks and can be set down anywhere that there might be a leak.

Nga Phan

That is exactly it. The sensors are easy to set up with just a few clicks, thanks to the WiFi enablement and an app that provides real-time, interactive guidance.

The system creates a regular stream of data being shared with the manufacturers through the apps and WiFi network. This data is also shared with the carriers.

On the back end, all these events and data are integrated into the policy administration system and underwriting workflows for the carriers in real time.

One of the most compelling things is that carriers can be confident about compliance. Before, if a carrier offered a discount for having a water sensor, they had no way to verify if customers were actually using it, keeping it updated, or ensuring it worked effectively.

With this program, carriers can confirm the sensor is active, in use, and doing its job. This visibility is enabled by our direct data feed that connects all components in the system. The data flows from the sensors to the apps, device manufacturers, our platform, and ultimately into the carrier's operations. 

The program has proven to be economical for carriers to invest in. Because carriers experience lower loss ratios, they are able to realize significant savings. Even when they pass these savings to insureds in the form of discounts and cover the cost of the devices, they still come out ahead.

Year one is typically about breaking even. However, starting from year two and throughout all subsequent years, carriers actually experience a positive return on investment.

Paul Carroll

If carriers can provide these sensors for free and still come out ahead, that’s a big change. A few years ago, the economics only worked if the cost of deployment was matched against the whole range of costs from a loss – the cost to the insurer, the deductible paid by the policyholder and the hassle factor of recovering from major water damage.  

Nga Phan

Yes, we've reached a tipping point. The economics have become positive, allowing carriers to subsidize the device cost, which is crucial for scaling this technology.

Paul Carroll

Some leak prevention systems use automatic water shut-off valves, but they’re expensive, especially if you’re retrofitting a building rather than including them in the original construction. Where do you stand on shut-off valves?

Nga Phan

Today, we have not incorporated shut-off valves, but that’s actively being researched by us for the proper adoption and economics. The technology around that was still expensive a couple of years ago, which would have made the economics more challenging and prevented the broad rollout of our program.

That's why we went with water leak sensors first. It's important to prove out the concept. 

Now that we have proven the economics, I think it will be very powerful to combine the current approach with shut-off valves in places where it makes economic sense.

Paul Carroll

What is the current deployment scale of your program, and what expansion plans do you have for the coming years?

Nga Phan

Currently, we have 25,000 devices deployed across our network. As I mentioned, these installations have resulted in a 55% pure premium reduction for carriers driven primarily by avoided loss events, which account for over 40% of the reduction, with the remainder coming from reduced severity when losses do occur. These are verified results that we’re confident can scale across more carrier partners. Our immediate focus is bringing more carriers into the program. We're also expanding our sensors to monitor additional types of events, including freezing, fire, and theft.

Commercial property represents another significant area of expansion for us. We began with attritional water damage and have proven our model's effectiveness and scalability. This established playbook now allows us to extend into other event categories and implement additional types of sensors.

Paul Carroll

A device made by Whisker Labs, called a Ting, prevents fires by monitoring for electrical problems in a building. As it’s been widely deployed, the device has been shown to be able to spot problems in the grid, too, including ones that have led to major wildfires. Could broad deployment of water sensors provide the same sort of benefits to the whole system?

Nga Phan

I think so. With water, understanding the flow for all homes in a particular area enables detection of surges and allows for implementation of region-wide alerts.

While responding to one house at a time might be manageable, addressing multiple homes simultaneously presents challenges. Anticipating potential issues affecting numerous households allows for more efficient mitigation actions. You could analyze correlations between outside temperature and the temperature of main pipes across homes. This application of big data helps identify larger risks affecting a greater number of properties.

By analyzing patterns from everywhere—combining factors like pipe age, temperature, and humidity—we can predict the likelihood of incidents before receiving an actual leak alert. This represents a shift from macro to micro focus, enabling more personalized risk prediction for specific properties.

Paul Carroll

I assume the learnings could then be fed back to municipal regulators and builders to improve construction practices.

Nga Phan

Absolutely. The potential applications are significant for the industry.

We are extremely excited about this initiative. We embarked on this journey as a proof of concept because we had the intuition that it would provide meaningful impact. Now we have proven it works.

The economics and the scale are there. The program is real and tangible, and both the insured and the insurers are reaping the benefit. We cannot wait to scale this up with more carriers and more end consumers.

Paul Carroll

That sounds great. Best of luck. 

About Nga Phan

As head of product, Nga is responsible for leading bolt’s product development in support of the company’s long-term growth plans. Her role is instrumental in steering the strategic direction of the company’s product offerings, focusing on innovation and enhancing the insurance market. Nga has nearly two decades of experience in executive leadership roles and advising C-suite leaders at enterprise SaaS companies, including Salesforce, ServiceNow and Risk Management Solutions. She also spent time at Bain & Co., where she contributed significantly to the telecommunications, media and technology practice. A transformational leader in the technology and insurance sectors, Nga’s deep expertise in product management, strategic planning, and operational leadership has led her to make significant contributions across organizations, having driven product innovations that transformed customer service experiences, including leveraging generative AI. Nga holds an MBA from MIT Sloan School of Management and a BA in economics from Bates College. She lives in the San Francisco Bay Area and enjoys exploring the outdoors and spending time with her family, which includes two canine children.

As macro volatility, climate events and litigation risks continue to challenge pricing stability, insurers are responding with a sharper lens and rewarding the well-prepared.

This is the era of precision risk, where even two companies in the same sector can see drastically different renewal outcomes depending on how well they present, quantify and control their exposure. Rising capacity and heightened carrier competition — particularly in property and cyber — have started to temper rate increases, especially for insureds with robust risk management programs.

The commercial insurance landscape showed mixed signals in the first quarter of 2025. Rate guidance ranged from -20% to +15% across major lines, with results largely influenced by risk profile, loss history and industry classification. Certain lines — specifically cyber, workers' compensation and D&O — saw decreases. Others — notably commercial auto and umbrella liability — encountered capacity constraints and sharp price increases, particularly for higher-risk clients.

Consider cyber: Organizations with strong controls, documented internal processes and alignment between IT and risk teams are seeing more favorable renewals, with modest rate decreases of up to 10% and in some cases more substantial decreases (over 50%), depending on how the organization was presented to the marketplace in prior years. Underwriters are rewarding maturity, not just software. Companies that pair solid cyber hygiene with sophisticated modeling and vulnerability scoring have more options, like higher sublimits and broader terms.

Meanwhile, lines like commercial auto and umbrella and excess are less forgiving. First quarter rate increases in these segments typically ranged from +5% to +15%, with steeper spikes in high-risk sectors like transportation and construction. In these lines, a weak risk story — whether from outdated safety protocols or limited data — almost guarantees above-average increases.

Underwriters Want Answers, Not Just Applications

For the policyholder, the message is clear: Insurers expect more. Not more paperwork, but more insight. They're asking:

• How current are your property valuations?
• What business continuity plans back your business interruption limits?
• Can your safety programs be backed by outcomes, not just intentions?
• How are you addressing location-based CAT exposure or litigation risk?

Insurers are under pressure to improve underwriting performance, control loss ratios, and manage capacity. Years of continuing catastrophic weather losses — like this year's California wildfires, expected to result in as much as $50 billion in insured losses — have made profitability harder to maintain. Add to that social inflation, unpredictable litigation and widening gaps in coverage, and underwriters can't afford to treat all submissions equally.

They are triaging submissions more aggressively and rewarding those who come prepared with well-supported documentation, including safety protocols, updated valuations, business continuity and disaster recovery plans and clearly articulated risk mitigation efforts. This is particularly true in distressed or volatile sectors like transportation, real estate and healthcare, where the gap between well-managed and high-exposure accounts continues to widen.

The Well-Prepared Renewal Playbook

In a segmented market, what you do between renewal cycles matters as much as what happens during them. Risk leaders seeing the best outcomes in 2025 are doing the following:

• Start early. Starting the renewal process early creates the breathing room to shape a stronger outcome. It allows time to revisit property valuations, update exposure data, clarify loss narratives and address underwriter questions. Even 60 to 90 days earlier can make a meaningful difference in how your submission is received.
• Quantify the right data. Underwriters are applying stricter triage, prioritizing submissions backed by meaningful metrics, including CAT modeling, updated total insured values (TIVs), measurable risk control strategies backed by loss data, cyber risk scoring and business interruption calculations. Accounts with strong safety protocols, up-to-date valuations and clear mitigation stand out.
• Align across departments. The strongest risk presentations reflect input from across the business, including IT (for cyber), HR (for EPL and benefits-related exposures), finance (for valuation and liquidity) and operations (for safety programs and continuity planning). Businesses that bring cross-functional alignment to the table are showing insurers that they've done their homework and that their insurance strategy is backed by broader operational readiness.
• Be open to structural changes. Nuanced markets require flexible thinking. Structures like captives and parametric solutions are no longer reserved for massive enterprises. They're viable tools for managing cost, volatility, or coverage gaps, especially in CAT-exposed or litigation-prone lines.
• Set internal expectations. Even well-managed risks may face increases in high-pressure lines like commercial auto or excess liability. Historical data, market insights and program benchmarking can be used to explain why a change is happening to stakeholders, and how your strategy is mitigating its impact.

2025 Is a Test and a Turning Point

This year won't reward the default submission. It will reward organizations that demonstrate control, strategy, and responsiveness. C-suites and risk managers who treat insurance as a year-round process instead of a last-quarter task are finding themselves in stronger negotiating positions, with more stable programs.

The gap between well-managed and distressed risk is widening. It's no longer just about loss history. It's about how effectively the business tells its risk story, how well its internal systems support that narrative and how early it engages with the market.

Across every line of coverage, the first quarter of 2025 showed that organizations with accurate valuations, active mitigation efforts and a clear risk strategy are outperforming peers. As the year progresses, volatility will remain a constant. But for the well-prepared, so will opportunity. In this market, readiness is leverage.

The property insurance market has reached a point of stabilization after years of volatile rate hikes and financial losses. However, external threats—especially those posed by natural disasters—continue to challenge property owners and insurers alike. The increasing frequency and severity of extreme weather events, coupled with inflationary pressures and shifting geographic risk zones, have made risk management more critical than ever.

Managing property risk is no longer about reactive solutions; it requires a proactive, data-driven strategy. From accurate property valuations to leveraging advanced event monitoring technologies, advisors must encourage property owners and businesses to take decisive action to mitigate vulnerabilities and secure insurability. 

Here's what your property stakeholders need to focus on to navigate the evolving risk landscape.

Accurate property valuation: A foundation for proper coverage

One of the most significant issues to emerge in property insurance over the past several years is undervaluation. Many properties have been historically underinsured, leading to inadequate coverage when disasters strike. In high-risk areas, rising property values have made this problem even more pronounced.

For instance, a building insured for $3 million may now require $4 million-plus for reconstruction due to inflation, supply chain disruptions and escalating costs of materials and labor. Without regular property valuations, policyholders may find themselves facing significant out-of-pocket expenses if a claim exceeds their coverage limits.

To mitigate this risk, advisors should instruct property owners to:

• Conduct property valuation reviews and updates to reflect true replacement costs
• Work with insurance brokers and valuation experts to determine accurate market rates and trending factors
• Maintain clear documentation of valuation methodologies to justify insured values

By staying ahead of valuation shifts, property owners can reduce the likelihood of cost increases while ensuring they have adequate coverage in place.

The rising threat of natural disasters and secondary perils

Catastrophic weather events are reshaping risk landscapes across the U.S.. In 2024, there were 27 climate-related events that each exceeded $1 billion in insured property losses. Regions once considered safe are now experiencing severe storms, wildfires and flooding, making preparedness a necessity rather than an option.

Tornado Alley, for example, has shifted eastward, while cold snaps now extend into Southern states. Florida and California continue to face increasing risks from hurricanes and wildfires, respectively, exacerbating insurance costs in those regions.

Given this, advisors need to encourage property owners to integrate advanced risk modeling and predictive analytics into their disaster preparedness plans. These tools can help assess potential vulnerabilities and guide risk mitigation efforts, such as:

• Reinforcing building structures to withstand harsh weather
• Upgrading roofs, windows and drainage systems to minimize storm damage
• Implementing wildfire-resistant landscaping and maintaining defensible perimeters

For property that constitutes a business and generates revenue, continuity insurance and a disaster recovery plan are paramount.

The role of smart technology in risk prevention

The emergence of smart home and commercial property technologies is changing how insurers assess and price risk. Property owners who invest in these solutions are more likely to secure favorable coverage terms and lower premiums.

Some of the most effective smart technologies include:

• Water leak detection systems: Water damage is one of the most common non-catastrophic claims, with leaks from plumbing failures or faulty appliances accounting for billions in losses annually. Smart sensors can detect abnormal water flow and alert homeowners before minor leaks escalate into major damage.
• Security and surveillance systems: Many burglaries occur without forced entry, often due to unlocked doors or weak security measures. Smart locks, surveillance cameras and alarm systems reduce theft risk and provide insurers with verifiable proof of security measures.
• Fire and electrical monitoring: New technology can now detect electrical arcing behind walls, a common precursor to house fires. Advanced remote fire monitoring and suppression systems can reduce both loss frequency and severity.

Insurance providers increasingly require these measures as eligibility criteria rather than the basis for optional discounts. As a result, it's important that property owners are aware that failing to adopt risk-mitigation technologies may make it harder to obtain affordable coverage.

The growing cost of insurance and alternative risk solutions

Homeowners and businesses alike are grappling with rising insurance premiums, particularly in catastrophe-prone areas. In Florida, for example, the average annual homeowners' insurance premium is four times the national average, making it difficult for many property owners to afford coverage.

In response, advisors should encourage clients to consider alternative risk solutions, including:

• Captive insurance: Business owners may explore forming captive insurance entities, allowing them to assume some or all of their own risks in a structured and financially beneficial manner over time.
• Parametric insurance: Unlike traditional insurance, which reimburses losses after a claim process, parametric policies provide predefined payouts when certain conditions—such as wind speeds or earthquake magnitudes—are met.

These alternative solutions require careful planning and financial analysis, but they can provide viable options when traditional insurance becomes cost-prohibitive or unavailable.

Modern risk management

Managing property risk in 2025 requires a multi-faceted approach that goes beyond traditional insurance. The increasing frequency of natural disasters, rising insurance costs and emerging global threats demand strategies to safeguard client homes, businesses and assets in an unpredictable world.

By prioritizing accurate property valuations, investing in smart risk mitigation technologies and exploring alternative insurance options, property owners can position themselves as lower-risk policyholders. This not only enhances their insurability but also ensures long-term financial stability.