Over the past decade, carriers have modernized their core systems, stitched together integration layers, and deployed business process management (BPM) tools. These efforts brought efficiency and scalability, but they weren't built to support what may be the most significant shift in enterprise technology since the microprocessor: AI.

As AI moves from pilot to enterprise-wide production, regulators like the NAIC are focusing on governance and explainability, and many insurers are discovering that their current architecture simply wasn't designed for this new world.

Insurers don't need to throw away what they've built. But they do need a new layer of architecture, one that enables orchestration of automation, AI, and digital servicing independent of the core. This is driving a shift toward coreless modernization.

What Is Coreless Modernization?

To be clear, coreless doesn't mean going without a core system. It means liberating the enterprise from the limitations of the core. This virtualized layer acts like a semantic graph, allowing systems and applications to operate on real-time data across the enterprise without duplication or disruption.

While traditional legacy transformation often relies on expensive projects involving replacing and retiring core systems, coreless takes a different approach. It uses an event-driven orchestration layer, data fabric, and modular AI services to externalize business logic, workflows, and decision-making to a more flexible, intelligent layer while leaving the core system intact.

Core systems remain the system of record, but orchestration of servicing, underwriting, distribution, and engagement are able to be moved to the abstracted layer that's decoupled from the legacy constraints that typically slow innovation.

In effect, this creates a hollowed-out legacy environment, one where modern capabilities operate in sync with the core system, extending its utility without overloading it or requiring that it be replaced.

Why Now?

Three fundamental shifts are making coreless possible:

• Data Fabric Maturity: Insurers now have the tools to build a unified data layer across systems, without duplicating or displacing source systems. This makes it possible to expose business state and automate real-time workflows without overwhelming the core.
• AI-Driven Decision-Making: With agentic AI, intelligent automation can now handle more than just simple tasks. Complex underwriting, fraud detection, and case routing can run outside the core with full lineage, audibility and traceability.
• Composable Architecture: Agentic orchestration allows new journeys and products to be assembled in weeks, not years, without being bottlenecked by monolithic legacy dependencies.

These shifts aren't abstract trends. They are direct responses to mounting pressure across the insurance enterprise. Distribution leaders want faster partner onboarding. Product teams need to launch offerings in weeks, not quarters. Compliance officers and regulators demand auditability. CIOs are expected to scale AI safely without triggering full system rework. Traditional architectures can't keep up. Coreless gives insurers a way to break through without breaking what already works.

What Makes Coreless Different?

The key distinction is architectural: Coreless reinvention introduces a unified substrate for orchestrating AI, automation, and digital workflows without replatforming. Where BPM tools and application programming interface (API) middleware attempt to route tasks across siloed systems, coreless provides an explainable orchestration substrate that can:

• Ingest real-time business events to trigger AI and automation flows
• Log every decision for audit and NAIC compliance
• Scale horizontally without relying on BPM or synchronous APIs

A Blueprint for Reinvention Without Disruption

Every decade or so, enterprise technology brings a defining architectural shift. Mainframes gave way to client-server architectures. Legacy policy administration systems (PAS) evolved into cloud-based cores. This next shift is being shaped by AI.

Some insurers are already applying coreless principles in practice. One insurance carrier began orchestrating new workflows around its existing core systems, rather than within them. This shift allowed the company to significantly reduce policy issuance times and achieve sub-400 millisecond response times, all without rewriting foundational infrastructure or disrupting their core.

Composable. Co-existent. Designed for Flexibility.

The concept of coreless is built on the principle of separation of duties in every module, from decision-making engine to data fabric and AI orchestration. These can operate independently or together, depending on your needs. This means insurers can retain existing investments, whether that's a PAS, customer relationship management (CRM), or claims system, and still adopt AI capabilities to modernize intelligently.

With agentic AI, embedded experiences, and real-time orchestration on the rise, coreless modernization is becoming the new path forward. It's a pragmatic response to the realities of building, scaling, and governing AI-powered insurance operations in today's landscape. For insurers navigating legacy complexity while pushing toward digital agility, Coreless offers a viable alternative for modernization, one that complements what exists, while enabling what's next.

If you are trying to scale AI inside a legacy-bound stack, you're already behind. Coreless isn't the future. It's today. Start embracing it now.

As digitization reshapes every link along the insurance value chain, one essential component still lags: the payment experience. 

For policyholders, the payment process is one of the most frequent and tangible touchpoints with their insurance carrier. But outdated payment systems and non-specialized call center representatives often result in a frustrating experience for policyholders seeking an accurate understanding of their payments.

Modernizing the payment experience presents an opportunity to foster customer goodwill in the insurance industry. However, regulatory nuance and capital demands of financing premium payments make this an area where insurance carriers, managing general agents (MGAs), and insurance agents benefit from innovative technology and strategic partnerships. Insurance premium finance companies have evolved from an industry utility into allies helping insurers meet customer expectations and build competitive advantage.

The traditional, narrow view of premium finance has been purely functional, missing the broader strategic potential. Today, while premium finance companies deliver fully integrated, digital-first payment experiences, only a few are forward-thinking enough to incorporate the latest cutting-edge technology. Some carriers have explored in-house financing models, but most find partnering with the right third-party premium finance company delivers quantifiable results, including delivering payment innovation reliably and expediting the cash cycle.

Speed and Flexibility Without the Capital Burden

Financing premiums in-house requires considerable capital reserves. It also necessitates loan servicing capabilities, regulatory and financing expertise, and significant exposure to credit risk. For many insurers and MGAs, this is simply not a core element of their business model.

Leading premium finance companies provide a deep specialization in both financing and customer service. These firms are built specifically to handle the complexity and expectations of the premium finance process, from precise billing calculations and cancellation workflows to high-touch borrower support. Their service teams are trained to work with policyholders who may not be familiar with financing mechanics. This level of customer service is difficult to replicate internally without additional cost burdens and ensures that policyholders receive timely, expert support that reflects positively on the insurer's brand. In many instances, the premium finance company's customer support team becomes a main contact for the insurer's agents and customers. For commercial policies, their service specialization can be the difference between a closed sale and a missed opportunity.

Elevating the Customer Experience With Innovative Payment Solutions

Customer experience is a key differentiator in an increasingly competitive insurance market. Policyholders want to manage their policies and payments the same way they manage many financial aspects of their lives: online and on mobile.

Forward-thinking premium finance companies have responded with platforms that integrate seamlessly into the quote-to-bind process and policyholder portals. They incorporate technologies that have become expected in payment processing, such as electronic signatures, auto-pay and online account services.

Some premium finance companies further streamline the payment process by incorporating innovative solutions into antiquated methods. For example, they deploy secure, single-use QR codes on printed and emailed payment notices. These codes directly link to the customer's personalized, secure payment portal, thus eliminating the need to log in or manually enter account details. This noticeably reduces friction for customers who still receive paper correspondence or who prefer traditional billing formats, while maintaining security and compliance.

Another innovation is incorporating opt-in text message payment reminders with shortened, secure URLs. These messages allow policyholders to access their payment portals with a single tap, improving on-time payment rates while reducing cancellations due to missed installments. The convenience of mobile-first communications reflects how today's consumers prefer to interact with service providers of all kinds.

Building these cutting-edge, compliant financing capabilities internally is a resource-intensive project for insurance carriers, which distracts from an insurer's core objectives. Premium finance companies have already made these investments, including API-based integration with agency management systems, co-branded borrower portals, and automated document generation.

Final Thoughts: Rethinking Payment Processing as a Strategic Advantage

In an era where customer expectations are rising and digital transformation defines competitiveness, the payment and financing experience has become a strategic opportunity. Historically overlooked, this metaphorical "last mile" of the insurance process can be a key differentiator for carriers, MGAs, and agencies willing to modernize their payment processes.

insurance organizations gain more than capital support by affiliating with specialized premium finance companies. They gain access to turnkey technology, compliance expertise, and customer service infrastructure built specifically for the unique demands of insurance financing. These partners enable insurers to deliver an innovative payment experience without the financial and operational burden.

As the industry continues to evolve, those who rethink payment and financing as a core component of the customer journey will be best positioned to drive loyalty and compete at the speed of today's market.

In this ever-changing technological landscape, it's important to continuously reevaluate consumer payment options. Modernizing the payment experience benefits both insurers and their customers.

Across the insurance industry, carriers are quietly losing ground—not to market shifts or rising risks but to their legacy policy administration systems (PAS). These aging platforms aren't just inefficient; they hinder innovation, frustrate employees, and limit insurers' ability to respond to customer needs, regulatory change, and competitive threats.

Complex, Costly, and Inflexible

Legacy policy administration systems are often built on proprietary frameworks developed on top of traditional platforms. These custom-built architectures are typically rigid and complex, requiring specialized—and often costly—expertise to maintain or enhance. This makes adapting or evolving the system challenging without deep knowledge of the underlying custom framework. Every implementation, enhancement, or product launch adds cost, complexity, and reliance on niche skills. The result is spiraling budgets, rigid workflows, and delays in going to market.

Compounding these issues, many older systems lack standardized debugging tools. Logs are fragmented, and troubleshooting often requires manual searches across multiple components and microservices.

Another challenge is PAS providers that offer only partial modules. This pushes insurers to adopt multiple systems written in different languages, which rarely integrate cleanly and then require additional data lakes, middleware, and maintenance layers. As a result, transactions can't be easily traced across systems. This blocks efficiency and limits technologies like AI and predictive modeling.

The true cost isn't just technical debt—it's missed opportunities. As one insurance executive put it, "Every enhancement or product rollout feels like a battle, sapping both budget and morale." The impact extends across underwriting, claims, billing, and service—dragging down the customer experience and hampering growth.

The Competitive Divide Is Widening

The industry is at an inflection point: Modernization is no longer optional, it's a competitive necessity. Insurers that modernize gain real-time access to data, faster product deployment, and greater agility to respond to regulation and market shifts. Cloud scalability, API-first design, and embedded analytics enable them to tailor experiences and drive operational excellence.

Consider a mid-sized carrier running a heavily customized legacy PAS. When new regulations demanded fast product adjustments, rigid workflows and hard-coded rules made a timely response impossible. Product timelines stretched into quarters. Competitors with modern platforms capitalized.

This scenario is common. Carriers without modern systems face costly delays, limited insight, and reduced responsiveness. The fallout: missed revenue, agent frustration, and customer churn—all of which undermine competitiveness.

Capabilities Insurers Need to Stay Agile and Compliant

While policy administration systems have long been "sticky" due to high replacement costs and the risk of operational disruption, today's pressures from artificial intelligence (AI), regulatory complexity, and speed to market are forcing insurers to reconsider the efficacy of their legacy systems.

A modern PAS must enable seamless communication across all core insurance functions—from rating and underwriting to broker and client portals, reinsurance, actuarial reserving, billing, claims, and regulatory filings. The key to achieving this is an open, configurable platform that unifies these disparate components into a single, integrated system.

Such platforms should be built on industry-standard programming languages and frameworks. This broadens the developer pool, accelerates development cycles, reduces maintenance complexity, and future-proofs the system for ongoing innovation. Configurability and scalability become essential, enabling insurers to adapt quickly in a landscape marked by rising claim costs, workforce shortages, and shifting regulatory requirements.

Auditability and governance are equally crucial. Modern PAS solutions embed version control and traceability into every system change—from rating rules to workflow configurations. This ensures transparency and simplifies compliance management with built-in audit trails.

Integration readiness is another vital attribute. API-first architectures allow smooth, real-time connectivity to essential services such as payment gateways, agent portals, reinsurance systems, and AI-driven engines. This design supports rapid deployment and flexible plug-and-play capabilities.

Finally, a truly modern PAS delivers unified workflows that provide a 360-degree view of the policyholder. With real-time analytics available at both macro and micro levels, underwriters, claims teams, and operations can make faster, smarter decisions, streamline processes, and improve customer experiences.

Overcoming Barriers to Modernization

Despite the clear benefits, some insurers still hesitate—wary of cost, time, and complexity. Historically, PAS upgrades were multi-year projects with big budgets. But that's changing. Newer market entrants offering end-to-end platforms are dialing down the risk by eliminating implementation fees and reducing the reliance on niche developers. With modern tech stacks and prebuilt integrations, carriers can launch faster and cheaper than ever before.

Modernizing PAS is no longer just a technology upgrade. It's essential to business growth, customer retention, and long-term survival in the rapidly evolving insurance landscape. Ultimately, the question isn't if insurers must modernize—it's how quickly they can act. The competitive divide is real, and despite the time and capital outlay, those who invest now will lead while those who delay risk being left behind.

Recent global research has highlighted a huge talent issue in the insurance industry. Just 9% of the insurance workforce is in digital and tech roles, compared with 47% in sales and marketing. This imbalance leaves the sector overly reliant on manual processes, hindering the adoption of crucial capabilities such as data and analytics.

As data, AI, and climate risk continue to transform the landscape, this lag in digital capability is likely to have serious, far-reaching consequences.

Meanwhile, insurance employers benefit from strong career loyalty, with low turnover and high satisfaction—which is great for long-term planning, but now creates urgency to upskill long-serving staff in digital, product design, AI, and other emerging areas.

Using a combination of quantitative and qualitative analysis, research has identified "pacesetters"— industry leaders that excel in their financial, innovation and talent outcomes and achieve sustained success.

After analyzing millions of data points from the top 200 global insurance companies, we can see how this is playing out in insurance. The research unearthed clear data that some insurers aren't just rising to this talent challenge—they're lighting the way forward for the rest of the industry.

What differentiates these leaders is their embrace of intelligent adaptability—the capacity to evolve quickly and strategically.

But what does that look like in practice?

A late, but strong, embrace of advanced tech

While many insurers remain cautious about AI investment, the industry's pacesetters are forging ahead—recognizing that lagging on adaptability is a risk they can't afford. For these leaders, adopting new technologies isn't optional; it's foundational to staying competitive in the 2020s and beyond.

That marks a significant shift from even a year ago, when most of the top-performing firms had only vague notions about "doing something with ChatGPT." Today, those same leaders are mandating its integration across the board—from enhancing customer experience to transforming back-office training.

A new approach to job definitions and pathways

Training is a crucial focus because the demographic of those committed to long-term careers in insurance is primarily made up of Gen Z, who have grown up with the internet as a default part of their lives. In response, pacesetters are redefining what a career in insurance looks like in the 2020s and 2030s, creating more flexible, adaptable pathways for talent.

At the heart of this new career proposition is a shift in how progression is framed—emphasizing continuing support for developing technological familiarity and proficiency. Importantly, pacesetters aren't aiming to replace large portions of their experienced workforce. Instead, they're carefully designing diverse career paths that make transitioning into tech-focused roles not only manageable but genuinely attractive and motivating.

The aging out and specialized employee problem

It might not seem like a problem, and the highly skilled veteran might not see it that way either, but when critical knowledge is locked away in a single team or even just one person's head, it becomes non-transferable—and runs the risk of being lost when that individual leaves.

Research shows that insurer pacesetters are tackling this challenge in two innovative ways. To prevent the scenario of "Only Janice knows how to do this, go ask her," some employers are experimenting with tech-enhanced onboarding. This includes using virtual reality headsets to provide immersive, step-by-step walkthroughs of core processes early on, allowing new hires—or those moving between roles—to organically build their skill sets and understand how they can contribute beyond their immediate job description.

Some pacesetters are going a step further by capturing the unique specialist knowledge of key or aging staff in "digital twins"—AI-driven models designed to reason and respond with expert-level insight in niche, highly valuable areas of market-specific insurance solutions.

New customer acquisition solutions

Finally, the data reveals how insurance pacesetters are transforming product access by embedding insurance offerings into adjacent industries such as automotive, mortgages, and other B2C products.

What's driving this shift? Not only pacesetters but the entire market have experienced significant declines in renewals over recent years, largely due to consumer pushback against price increases. In response, insurers are striving to present attractive offers as early as possible and intensify efforts to reduce churn and retain customers. These new channels and tactics have only become viable by recognizing that traditional approaches no longer suffice, necessitating fresh, data-driven strategies.

Six steps to intelligent adoption

For insurers looking to become intelligently adaptable, six key steps emerge from this research that should be put into practice:

1. Establish a cross-functional talent intelligence center of excellence (COE). Unite people analytics, HR, digital transformation, and business leadership under one roof. This COE will serve as the foundation for navigating disruption, forecasting future capability needs, and embedding talent strategy into enterprise-wide planning.
2. Identify your most critical talent challenges. Whether it's modernizing underwriting teams, scaling AI expertise, or building pipelines for innovation in product development and customer experience, use AI-driven talent intelligence to analyze roles, skills, and organizational patterns to uncover gaps and opportunities.
3. Develop solutions using the four "R" framework: Recruit, Retain, Reskill, and Redesign. Tailor these approaches to your organization's unique priorities, ensuring that planning and execution involve close collaboration across HR, operations, IT, and business leaders.
4. Embrace cross-functional planning and execution. Align efforts across all relevant teams to ensure a cohesive, integrated approach that supports business goals and drives transformation.
5. Measure, iterate, and improve continuously. Track progress against clear benchmarks, adjust strategies in real time based on data, and create a feedback loop that fosters learning and refinement.
6. Act with urgency and learn from the pacesetters. The insurance workforce is rapidly evolving, and traditional roles and skills are quickly becoming obsolete. Follow the lead of the industry's pacesetters who are finding, cultivating, and retaining the emerging skills they need to thrive in a changing world.

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.