The insurance industry is facing pressure from all sides. Customer expectations are rising, as they expect the same sort of nearly instant responses they get when shopping or viewing bank balances online--creating problems for carriers that still rely on manual, paper-driven workflows. Yet regulators are requiring fairness and transparency even as the pace quickens. And newcomers with modern tech stacks are capturing market share.

This is the reality of today's insurance industry. Research published in Frontiers in Artificial Intelligence shows that AI is already transforming underwriting, fraud detection, and claims management. At the same time, customer churn is accelerating due to yearly rising rates, claim severity, and cost increases.

The stakes for success could not be higher. The technology to achieve success already exists, and its adoption is accelerating. Forward-leaning carriers are already leveraging it, gaining efficiency and customer loyalty. If organizations choose not to act, they risk being left behind by both the industry and, most importantly, the policyholders.

Reimagining Insurance in the AI Age

AI brings with it the unbound potential to elevate the way insurers operate. At the core is the ability to extract valuable insights from years of unstructured data. Take underwriting. Traditionally, it has been a data-heavy process; however, when an underwriter is augmented with AI, they can make faster, more accurate risk assessments. This accelerated process is enabled in part by the insurer's expertise and AI's ability to analyze vast datasets and identify emerging trends and patterns that may have otherwise remained buried in their records.

Claims processing is another area that can experience an efficiency boost by leveraging AI. Automated systems powered by AI can analyze claims data more efficiently, speeding up decision-making and reducing the time it takes to settle claims. In an industry where customer experience is paramount, a quicker claims process is one of the keys to both maintaining and improving satisfaction and loyalty.

Some major insurance carriers who have adopted this approach include:

• Lemonade: Using its claims handling AI agent, aptly named AI Jim, Lemonade can resolve simple property claims in seconds or minutes. While there are still claims that will need human intervention, Lemonade makes funds available one to two days after claim approval.
• Allstate: The claims process is often complex and riddled with insurance jargon. To avoid these complexities, Allstate automated communications with AI to improve efficiency, customer experience, and bring empathy back to the industry.

Another financial impact of AI-enhanced systems is their ability to help identify fraud by flagging suspicious patterns and anomalies. This detection ability is critical in protecting insurers from hemorrhaging profits due to fraudulent losses.

Where to Unlock Business Value With AI

For many insurers, daily operations are dominated by routine. This means agents and adjusters spend valuable hours navigating paperwork across siloed and legacy systems, leaving less time for meaningful customer interactions. By embedding AI into core processes, carriers have an opportunity to shift their workforce's focus toward higher-value activities that directly affect satisfaction, loyalty, and retention.

To ensure that this integration is done correctly, carriers should look for areas where AI-driven solutions will provide real returns on investments, including:

• Repetitive tasks: Today, adjusters spend hours on data entry and form processing. AI can automate intake, document classification, and updates, freeing time for customer service.
• Real-time decision support: Adjusters must piece together information from multiple sources while customers wait. AI can surface policy details, claim history, and regulatory guidance instantly during conversations.
• Cross-system orchestration: Staff often toggle between claims platforms, CRMs, and document repositories. AI can connect these systems and present a unified view, speeding up responses.
• Complex pattern recognition: Fraud detection still depends on manual review of anomalies. AI can flag suspicious patterns early, guiding investigators to high-risk cases more efficiently.
• Where humans & AI can collaborate: Employees are stretched thin between admin work and customer care. By handling repetitive tasks, AI empowers adjusters to focus on empathy, trust-building, and problem-solving.

By shifting the balance from routine to meaningful work, AI enables insurers to improve both operational efficiency and customer loyalty.

Meeting Today's Expectations

Customer expectations have reached a breaking point. Policyholders demand instant support, yet too often encounter rigid scripts instead of real solutions. A 2025 study by J.D. Power found that 57% of customers are shopping for insurance year over year, a staggering jump from 49% in 2024.

It's not a complex equation: Insurers with superior customer experience see higher returns by unleashing human expertise for relationships, judgment, and empathy—not by eliminating jobs. To build on positive customer experiences, insurers should leverage AI to instantly handle claims while meeting rising standards for compliance, privacy, and fairness. The path forward is clear: innovation depends on strategic human-AI orchestration.

Artificial intelligence has entered a new era. It's no longer just a statistical predictor crunching historical data. It's now a creator, planner, and autonomous actor capable of generating content, making decisions, and executing multi-step tasks. This leap from traditional AI to generative and now agentic AI has fundamentally changed the risk landscape. These new AI systems therefore demand a rethink of how we measure, manage, and insure the risk.

Traditional insurance frameworks, predominantly built on backward-looking data and well-understood failure modes, are not suited for systems that learn, adapt, and change behavior in real time. As AI becomes more deeply woven into business, infrastructure, and daily life, the question is no longer if it will fail but how and who bears the cost when it does.

To unlock the full potential of AI safely and at scale, the insurance industry must innovate. This is not just about transferring financial risk, but also about creating market incentives for trustworthy AI adoption. Insurers and risk managers will need to deploy new tools to quantify, price, and monitor AI exposure, ensuring that innovation and safety evolve together. This is urgent, as many AI risks are sitting silently inside existing policies, often unpriced, unmanaged, and waiting to materialize. The systemic risk posed by this silent coverage represents a significant, largely unmodeled aggregation exposure for carriers and creates uncertainty for the insured.

In the sections that follow, we explore how the AI risk profile is evolving and why a new generation of assurance and insurance mechanisms will be critical to building confidence in the intelligent systems that will increasingly shape our world.

The Evolving AI Risk Profile

AI systems have gone through three major generations, each more capable and complex than the last. With every step, the risk profile has expanded.

1. Traditional AI: Early AI systems were essentially statistical predictors. They learned patterns from structured data to forecast outcomes -- for example, credit scores, demand forecasts, and spam detection. Their risks were relatively stable and easy to quantify, mostly limited to data quality problems or model misspecification.
2. Generative AI: Generative AI (e.g. large language or diffusion models) doesn't just analyze data; it creates content. This creative power comes with new risk: producing plausible but false outputs (hallucinations), reusing copyrighted material from training data, or shifting behavior as APIs or retrievers change over time. Because these systems are composable (built from multiple moving parts) and dynamic (updated frequently), they can change behavior without warning.
3. Agentic AI: The newest wave, agentic AI, adds autonomy, reasoning, and tool use. Autonomy brings systemic risk: small local errors can cascade across an entire chain of actions, a phenomenon known as compounding uncertainty. When such systems fail, tracing the root cause or conducting causation analysis becomes extremely difficult due to opaque failure modes and information asymmetry.

The critical challenge is that AI can now fail while doing exactly what it was designed to do. Unlike software bugs or cyberattacks, these failures emerge from within due to biased training data, drifting knowledge, or complex feedback loops. Managing such behavior requires continuous, evidence-based oversight rather than static, one-off testing.

From Checklists to Continuous Monitoring

For AI systems to be insurable or trusted in safety-critical domains, they must undergo rigorous, transparent, and repeatable AI risk management. That means moving from checklist validation to continuous monitoring, where systems are tested and challenged throughout their lifecycle. This risk management framework provides the necessary evidence and controls that underwriters will demand to price the exposure.

Best practice frameworks point to four foundations, which should be viewed as future underwriting criteria:

• Governance and Tiering: Treat the whole workflow from data pipelines to prompts and APIs as the governed unit. Tier systems not just by impact but also by autonomy (how much they act without human approval) and volatility (how often components change). Every modification should trigger a change-impact review.
• Design Standards: Start from intent: what is "failure" in business or operational terms? Translate that into measurable technical metrics, justify every heuristic (prompt templates, data filters, reward models), and document assumptions and known residual risks. Build guardrails and fallback plans from day one.
• Validation Uplift: Move beyond static benchmarks. Combine domain-grounded tests with adversarial evaluation and scenario stress-testing. Measure calibration and selective prediction; use red teaming to expose hidden vulnerabilities. Where LLMs are used as judges, demand statistical checks for bias and consistency.
• Monitoring: Deploy continuous monitoring across inputs, outputs, and dependencies. Track drift, fragility, and anomalous behavior. Establish clear service-level objectives for safety and accuracy. Keep humans in the loop for escalation and design rapid rollback and patching playbooks.

In this new landscape, model probe systems for blind spots, test procedural reliability, and pressure-test entire pipelines. The goal isn't just compliance, it's resilience: building AI systems that remain safe, and trustworthy even as they evolve. Experience in managing cyber risk means insurers can build on existing practices, but tools and methods will need to be adapted to AI systems.

The Case for AI Insurance: Turning Risk into Resilience

As AI systems become more autonomous and unpredictable, they test the limits of traditional insurance models. Losses caused by AI errors often don't fit neatly into existing policy lines like cyber, product liability, or professional indemnity, therefore creating coverage uncertainty. This often results in "silent coverage," which creates hidden liabilities, unpriced exposures, and uncertainty for both insurers and insured. This unreserved, unmodeled exposure threatens aggregation events and solvency for carriers.

From our perspective, it matters less whether AI risks eventually sit within existing policy lines, emerge as embedded features, or evolve into a new, standalone class of AI insurance. What matters is that AI risks are material and growing, creating significant exposure to portfolios and businesses alike. As such, they must be rigorously understood, quantified, and managed. Businesses adopting AI will need confidence that, when failures occur, clearly defined insurance coverage stands behind the technology if they decide to transfer the risk into the market.

To make AI risk insurable, the market will need innovative tools and pricing mechanisms that reflect how AI operates:

• Performance-Based Guarantees: Policies could trigger payouts if the AI underperforms (e.g., if its accuracy or reliability drops below a defined threshold). This mechanism could be structured as an endorsement on Product Liability or a custom Financial Loss policy.
• Usage-Based Insurance: Premiums can scale with AI activity (e.g., per API call, per decision), creating dynamic, real-time pricing that mirrors exposure levels.
• Premium Differentiation (Bonus–Malus): Safer systems should cost less to insure. Firms that can demonstrate robust governance, transparent validation, and effective monitoring would pay lower premiums. In contrast, opaque or unaudited systems would be priced prohibitively high or deemed uninsurable.

This market mechanism does something regulation alone cannot: it aligns financial incentives with technical rigor. Underwriters will demand strong assurance, continuous monitoring, and clear audit trails to minimize both frequency and severity. Post-incident protocols will help to contain financial losses. Like cyber, insurers and brokers will shape the standards for testing, validation, and operational oversight. By linking AI assurance to premium levels, insurance can become a catalyst for safer, more trustworthy AI adoption, rewarding those who invest in resilience and transparency while discouraging reckless deployment.

This article first appeared on Instech.

In the U.S. financial services sector, the rising costs of traditional B2C customer acquisition are self-evident. A 2023 report indicates that the B2C customer acquisition cost (CAC) has surged by 60% during the past few years.

Turning to a B2B2C growth model through partnerships, also known as alternative distribution channels, provides an avenue for organic growth, aligning with modern consumer preferences for simplicity, digital accessibility, and trust through brand loyalty. Partnerships not only offer startups an economical path to scale but also enable legacy incumbents to enter adjacent markets quickly, as trying to do it all in a vertically integrated model is becoming increasingly challenging.

Without executing a robust partnership strategy, startups like Trust & Will may not be able to amass 400,000 users, and Oscar Health might have been nipped in the bud. Likewise, legacy firms such as State Farm and USAA could have faced much more challenges in surpassing their competition, had they opted to go solo in tackling the market.

Of the various partnership models, the distribution partnership stands out due to its complexity and the absence of a definitive playbook. It is a collaboration where one sells another's financial products, with a primary focus on expanding sales reach rather than boosting operational or technological capabilities.

The advantages of a successful distribution partnership are manifold. Such partnerships not only ensure a cost-effective go-to-market approach but also create a competitive moat as a successful partnership typically requires three to 12 months to procure, unlike B2C counterparts, which can be established much more rapidly. There is no shortage of examples of a three-person marketing team putting out more quality content daily than $100MM companies put out in a month. A competitive moat, established through distribution partnerships, is critical for ensuring longevity in a highly competitive market, especially one where startups can iterate quickly.

However, this landscape has been changing. In recent years, the introduction of new technology and data has transformed the distribution partnership model within the financial services industry. Yet, the outcomes have been mixed at best.

While distribution partnerships are common in the tech and legacy financial service spaces, emerging financial service companies have grappled with leveraging the potential of B2B2C channels. Many high-profile partnerships have fizzled out prematurely, while others remain lackluster. A significant proportion of fintech or insurtech firms that embraced the B2B2C strategy have either floundered or pivoted.

Partnerships undoubtedly present numerous advantages, such as reduced customer acquisition costs, improved customer experiences, and mutual revenue opportunities. These advantages are clear in theory, but what are the underlying causes of the notable failures in practice? Let's explore some of the frequently overlooked challenges.

Challenge 1. Confusion Over Sales Strategies

One of the primary challenges that partnership leaders face in the financial services sector is the ambiguity in distinguishing among various distribution partnership strategies.

In the scope of distribution partnerships, terms like co-selling, cross-selling, and reselling often lead to confusion due to their subtly different interpretations arising from a lack of consensus on their exact definitions.

Consider co-selling, a strategy that has recently garnered attention for its advantages for complex products or services. In essence, co-selling unlocks value via synergistic collaboration between partners, working together to meet a common customer need throughout the sales process, a challenge beyond the capabilities of a single partner alone.

Reselling is generally traditional, standard, and straightforward, mirroring a vendor-buyer relationship. Co-selling, in contrast, involves a deeper, more integrated partnership that extends beyond conventional frameworks.

Cross-selling is another common form of distribution partnership, where complementary products are offered to an existing customer base. An example of this is the bundling of travel insurance with travel bookings, a notable success in cross-selling. However, most successful cross-selling today is confined to simple and commoditized products that have short sales cycles with low contract value, which yield lower margins for both parties involved.

Additionally, it's critical to differentiate co-selling from cross-selling. Cross-selling primarily involves offering supplementary products through the existing delivery mechanism, while co-selling is about two companies working together to cater to their shared customer base through innovative, previously non-existent delivery mechanisms.

The partnership between Lemonade and SoFi also serves as an instructive example. Despite their seemingly complementary products – mortgage and homeowner's insurance – the anticipated cross-selling actually requires a co-selling approach due to the significant gaps in education, experience, and customer expectations to purchase insurance versus mortgage. Both products are complex and require a longer sales cycle. A simple embedded insurance solution could not fill such a gap. This became evident when the partnership unwound in 2023 after two years of underperformance.

Understanding these nuances is fundamental to nurturing successful partnerships in the financial services industry.

Challenge 2: Absence of a Robust B2B2C Partnership Framework

The success of a B2B2C partnership strategy is often jeopardized from the onset by misaligned expectations. Partnership leaders regularly confront frustration when stakeholders perceive the process as a simple "plug and play," rather than recognizing it as the complex collaboration process it is. Success thus hinges on developing a shared understanding and strategic alignment among all stakeholders, such as partners' leadership team, operation team, and internal teams, to ensure commitments are made and resources are effectively optimized from the get-go.

To navigate these challenges effectively, a comprehensive framework is required, one that not only addresses the overarching issues but also tackles specific problems such as:

Partnering with companies that have little customer profile or needs overlap, leading to little gains beyond initial excitement.

Engaging with channels lacking authentic sales or marketing incentives, which proves futile, despite surface-level compatibility.

Over-reliance on revenue sharing without adapting it to distinct distribution partnership models might not adequately motivate partners seeking more support for their unique challenges. Moreover, those without experience in selling specific products may not see the projected benefits as tangible. Generally, revenue sharing tends to be more effective in reselling scenarios than in other contexts.

Lack of incentive alignment, risking half-hearted commitment internally and externally, causing project delays or directional chaos.

An aligned strategic partnership framework is essential. Without it, pinpointing the root causes of B2B2C challenges becomes nearly impossible. Teams may struggle to determine if issues arise from inadequate marketing, product issues, business development missteps, or fundamentally unviable channels. Blame games may start, often leading to the partnership's dissolution. 

Take the SoFi and Lemonade partnership as an example: Despite their status as fintech and insurtech leaders in the B2C space with exceptional growth at the time, the absence of a solid partnership framework left the insurtech's business model misaligned and interests unmet, rendering the partnership a victory only on paper.

Challenge 3: Not Establishing Clear Expectations and Full Commitment

An internally aligned partnership framework and a solid business case are essential, but setting clear expectations and ensuring full commitment from motivated partners are equally important. As the saying goes, "it takes two to tango." A partnership, much like a marriage, aims to collaboratively create innovative solutions with lasting motivation. The distribution of contributions and benefits in these agreements is rarely quantitatively symmetrical, and results typically emerge over time.

Commitment and trust are the cornerstones of lasting partnerships. It's worth mentioning that partnerships differ fundamentally from typical sales interactions; there isn't a pre-packaged product awaiting a purchaser. Instead, the partnership involves a process of continuing, collaborative execution. Clear expectation setting, coupled with fostering deep commitment not just from executives but also from those stakeholders on the ground, is essential for effective execution and aligning both long-term goals and short-term resource investments. 

"Reflecting on our journey, I've seen a recurring challenge," stated Ara Agopian, CEO of SolarInsure. "While our channel partners' executives often share our excitement about the partnership, that enthusiasm doesn't always reach the teams on the ground. This mismatch is typically rooted in external market pressures and a shortfall in product knowledge and training. Historically, this misalignment of commitment has led to several failed partnerships, stemming from our own miscalculated expectations and a lack of engagement from our partners. We've since refined our partnership strategy, now ensuring the commitment requirements are clear from the outset in our contracts."

Partnerships marked by transparency, collaboration, effective communication, and robust commitment stand a much greater chance of success. Conversely, concerns over intellectual property, methods or similar trust-related issues can almost completely undermine such endeavors. Effectively, setting expectations is a critical process for both assessing and motivating partners, a step without which true commitment is rarely attainable.

The collaboration between Allstate and Nationwide is an excellent example of this. The misalignment of expectations among top executives and the underestimation of the technological integration effort contributed to its failure, highlighting how such discrepancies can threaten the success of even the most established brands.

Challenge 4: Misalignment in the Commoditization of Customer Relationships

Even in the presence of a robust partnership framework, a compelling business proposal, and clear expectations and commitment, the financial service industry faces a significant challenge: aligning the commoditization potential of various customer relationship models. The concept of "commoditization potential of customer relationships" – essentially, the ability to monetize customer relationships – is influenced by various factors. These include the length of the sales cycle, the degree of product commoditization, the expected customer lifetime, the average value of contracts, and the complexity of the product. Failure to effectively navigate these aspects can often lead to partnerships that unfortunately do not yield significant outcomes.

Strategic planning and execution are critical in partnerships, especially when there are differing perspectives and strategies on commoditizing customer interactions, even for the same client base.

The effectiveness of a partnership hinges on aligning these models. Some companies focus on long-term cycles with infrequent but high-value interactions, while others prioritize more regular engagement. Additionally, approaches to client relationship management can range from tightly controlled to more relaxed. These disparities can create substantial obstacles to successful collaboration.

For example, life insurers have persistently sought to collaborate with fiduciary RIAs to cross-sell life insurance products targeting affluent clientele. Despite numerous attempts over the years, only a few have achieved significant scale. Even with products targeting the same clientele as RIAs with concrete use cases, many insurers struggle to appropriately "commoditize" advisor and client relationships and offer the support required, which fundamentally differ from life agent-customer relationships. Such misalignment can render the partnerships ineffective.

Challenge 5: Divergent Business Models

Expanding on the previous point, a sufficiently distinct customer relationship model often implies a different business model which necessitates a deliberate effort to bridge the gap. For example, a common challenge in partnerships arises when SaaS solution providers collaborate with financial product or service vendors. Theoretically, the broad customer base of SaaS companies appears to complement perfectly with the high-value contracts typical of financial products, suggesting an ideal match. However, in reality, this combination often results in complexity rather than simplicity.

Several factors contribute to these challenges. For instance:

• Many SaaS providers lack specialized expertise or an appropriate marketplace for effectively cross-selling financial products, hindering seamless integration.
• The introduction of third-party financial products involves risk, with potential liabilities that SaaS providers are often reluctant to take on.
• The differing business models of these entities imply different operational cadence: SaaS providers usually operate rapidly, while financial product or service providers need more deliberation and understanding.
• SaaS entities generally function in a less stringent regulatory environment and may be resistant to additional legal constraints.
• Additionally, SaaS companies might not fully understand the financial nuances and the commoditization possibilities inherent in their non-SaaS counterparts, leading to misaligned expectations and strategies.

Understanding and overcoming these complexities demands a nuanced and tailored approach to partnership strategies between varying business models. When executed with precision, these strategies can lead to substantial and meaningful success.

For example: Trust & Will, a SaaS solution in digital estate planning, exemplified a B2B2C partnership strategy by partnering with digital term life insurance distributors. In a highly competitive space with low entry barriers, Trust & Will distinguished itself by embracing a B2B2C approach since 2021, one of the first among its competitors. The team developed a robust partnership framework that gained internal support. This strategy was well executed in the subsequent years. By leveraging their digital capabilities and understanding of customer relationships nuance, they crafted an incentive structure perfectly aligned with the needs of term life distributors seeking differentiation. This strategic partnership with term life distributors contributed to Trust & Will's remarkable growth, further establishing its leadership in the digital estate planning domain.

The promise of B2B2C in financial services remains strong, but success requires more than ambition and alignment on paper. It demands surgical precision in partner selection, a shared commitment to execution, and a deep understanding of the underlying dynamics, from cost recovery models to business model compatibility. As the industry continues to evolve, those who approach B2B2C partnerships with discipline, clarity, and a framework rooted in operational realism will be the ones to convert potential into durable, scalable growth.

Note:

Other success cases: SasID and NAR (National Association of Realtors), IHC specialty benefits with USAA; New York Life and AVMA, Allstate's historical expansion due to partnering with Sears Roebuck & Company, Petco and Nationwide 23.

Underwriting has always been the heart of insurance. But that heart is now beating faster. For decades, underwriters have assessed risk one policy at a time. Today, they're under mounting pressure to process increasing volumes, respond to brokers faster, and still maintain profitability.

At Send's INFUSE webinar, my fellow panelists Tom Nasso, CUO, from Falvey Insurance Group, and Dan Walsh, CUO, from Equinox, shared compelling perspectives on the drivers behind this transition. Their insights crystallized that the future of underwriting lies not in individual policies but in portfolio-level intelligence powered by data and technology, for the majority of risks.

Why Shift from Policy to Portfolio Underwriting?

Tom articulated this evolution precisely: "Portfolio underwriting and the use of data to drive underwriting is the result of the market's need to address speed, efficiency, and decision-making."

This observation captures the fundamental challenge facing underwriters today. The modern underwriting environment demands both speed and precision - traditionally competing priorities. Portfolio underwriting provides the framework to achieve both. Rather than navigating submissions in isolation, underwriters can identify trends across their entire book, monitor performance metrics in real time, and detect emerging risks before they materialize into losses.

At Aurora, we've advanced this concept by embedding algorithmic intelligence directly into the underwriting workflow. Modern technology can automate the manual steps of case underwriting - risk assessment, rules application, pricing calculations, and quote generation - enabling underwriters to focus on strategic portfolio management, broker relationships, and value-creating decisions rather than repetitive administrative tasks.

How Algorithmic Underwriting Works

During the webinar, I talked through this process in action. When a broker submission is sent in, systems can automatically extract critical details and enrich them with supplementary data points: location-specific information, exposure analysis, and peril modeling. This enriched dataset flows through rules and pricing engines, which generate consistent, auditable assessments and quotations within seconds.

This represents algorithmic underwriting - leveraging automation to handle the computational heavy lifting for complex underwriting, while empowering underwriters to excel at what truly differentiates them: cultivating relationships, optimizing portfolios, and executing informed strategic decisions that drive sustainable growth.

Insurers Are Already Seeing Significant Cost Reductions

The question of ROI naturally arises with any transformative technology. The empirical results provide a compelling answer. Carriers implementing algorithmic underwriting solutions are realizing significant expense reductions in trading operations.

That level of efficiency doesn't just cut costs - it fundamentally unlocks organizational capacity. You no longer need hundreds of underwriters to manage growth; you need skilled professionals focused on high-value decisions, supported by technology that enables scalable growth.

But efficiency is only part of the story. The real value lies in improving performance. When underwriters maintain real-time, granular visibility into portfolio performance, they can identify emerging patterns and intervene before trends deteriorate into losses. This operational agility transforms automation from a back-office efficiency tool into a genuine, profitable growth catalyst.

Legacy Systems: Still the Barrier to 'Smart Underwriting'

Transformation and change, however, is rarely frictionless. Both Tom and Dan highlighted persistent challenges: entrenched legacy systems, inconsistent data, and the cultural resistance that accompanies new technology.

At Aurora, we encounter these barriers regularly. Many carriers want to move forward but hesitate, concerned that their data isn't ready. My counsel is simple: start anyway. Begin laying the foundation. Use technology and AI to clean, structure, and organize your existing data assets. Simultaneously, establish robust processes to capture new data from inception - ensuring it's clean, structured, vast, and granular. Even if it isn't perfect, the progress you make today will determine how effectively you can use automation tomorrow.

And then there's culture. As Dan said during the session, this isn't just about tools - it's fundamentally about trust and organizational alignment. Underwriters and brokers need to experience automation as an enabler rather than threat. Success requires cultivating confidence that technology augments human judgment rather than replacing it.

Looking Ahead

Underwriting has always existed at the intersection of art and science. What's changing now is the scale and speed at which the science amplifies the art.

Automation, data, and algorithmic systems are enabling underwriters to make better decisions, faster, while freeing them to focus on the human aspects that have always mattered most: relationships, intuitive judgement, and experience.

The next chapter of underwriting isn't characterized by machines taking over. It's defined by human-technology collaboration that makes underwriting demonstrably smarter, faster, and more efficient - for underwriters, brokers, and clients alike.

The recent VIU by HUB Personal Insurance Marketplace Report and Rate Guide shows how broader economic trends affect premiums. Notably, the findings demonstrate that standard home and auto rate slowdowns are linked to cooling inflation, while rising rates for homeowners in disaster-prone areas stem from the increased frequency and cost of natural disasters.

With that data in mind, let's break down three of the biggest takeaways from our report:

1. Consumer expectations are rising amid rate fluctuations

Consumer expectations for comprehensive, affordable coverage are rising amid continuing rate fluctuations. While rate hikes may be stabilizing, volatility persists. Tariff announcements earlier this year created short-term disruption, but pricing has begun to level out; auto insurance premiums are moderating slightly, with the average increase closer to 10%.

Not surprisingly, on the property side, homeowners in catastrophe-prone areas still face steep increases. Regions at risk of wildfires, hurricanes, hail and convective storms will likely continue to see double-digit rate growth.

Flood policy counts are also rising as both public and private coverage evolves. As risks expand beyond traditional flood zones, pricing is increasingly tied to localized data.

All these factors have contributed to consumers feeling frustrated not only by rising costs but also by the complexity of an evolving insurance landscape. Insurance is no longer just an add-on to a home or car purchase – its cost makes it a major financial decision. This frustration is driving greater demand for clarity on coverage and costs, supported by neutral, expert guidance.

2. Embedded insurance shows customers that businesses care

Because insurance now occupies a larger share of consumer budgets, it's further affecting consumers' ability or willingness to make discretionary purchases. As a result, business leaders are asking: How can we help customers easily secure adequate, cost-effective coverage they feel good about?

The answer: enable customers to access insurance options through a licensed digital brokerage, either at or after point of sale, by embedding that brokerage's platform and expertise into a brand's sales process. At VIU by HUB, we call this brokerage as a service (BraaS). By partnering with a licensed brokerage, businesses can offer their customers multi-carrier solutions, paired with expert guidance, as part of the customer buying journey.

Think about it: when someone buys a car or applies for a mortgage, they're excited about their new car or home - insurance isn't top of mind. They're navigating a life event, and when it comes time to find insurance coverage, they likely need help. Embedding insurance options and expert advice before, during or after that purchase process is a convenient follow-through solution. For example, we recently worked with a top global automaker to integrate a digital insurance brokerage experience into its customer journey, giving customers a fast, trusted way to compare rates and receive licensed guidance across auto, home, motorcycle, renters and more.

The BraaS model places insurance within trusted shopping experiences and supplements it with live advisors, easy-to-understand choices and seamless operations. This doesn't just reduce confusion. It elevates customer satisfaction, increases revenue and brings long-term value to businesses seeking loyalty and repeat transactions.

3. The blend of human insight and AI efficiency benefits everyone

Artificial intelligence is evolving quickly and reshaping nearly every industry. Insurance brokerages are exploring how AI deepens understanding of customer needs to improve service, without sacrificing the critical element of human dialogue. We believe that, in the near-term, AI is most valuable when it enhances rather than replaces the human experience. For example, AI can be used to accelerate service by flagging life changes or analyzing documentation. It can also be used to give consumers more options and accessibility, such as after-hours service. But the role of empathy, judgment and trust cannot yet be replicated by algorithms. The future isn't human vs. AI – it's humans and AI. Our interactions use technology to support and enhance the human connection where it makes sense and in a way that consumers prefer.

What Comes Next

There's good news: price stabilization is beginning to take hold across some insurance sectors. Carriers are re-engaging in disaster-prone markets, even as rising claims costs, weather-related losses and increased repair costs remain real challenges.

The biggest shifts are consumer-driven. The insurance experience of tomorrow will be defined not by carriers' direct efforts, but by businesses meeting consumer expectations for convenience, clarity, compassion and capability. Embedded omnichannel brokerages are a powerful way to better serve those needs, enabling customers to access trusted insurance solutions as part of making the biggest purchases of their lives.

To better understand how insurers are addressing climate-related changes, the National Association of Insurance Commissioners (NAIC) requires insurers to file a Climate Risk Disclosure Survey that provides a discussion of how companies are addressing these risks and opportunities and integrating them into their governance structures, strategies and risk management.

We have reviewed many of these surveys. What is quite clear, albeit not surprising, is that insurers recognize that they face increased risks from chronic and long-term changes in climatic patterns but, importantly, they are well organized to deal with continuing and impending threats.

What is also clear from the disclosure, and perhaps less appreciated, is that insurers also foresee transformative opportunities. Specifically, these opportunities are expected to come from policies with risk control and loss prevention services. These policies help clients mitigate and build resilience to physical and climate risks. They also see underwriting opportunities in evolving and emerging technologies and industries.

Since insurers are investors as well as underwriters, they also see opportunities in allocating capital toward the energy transition and decarbonization efforts that occur in response to climate change.

The risks are well known but can be managed

Insurers are exposed to a wide array of physical and transitional risks stemming from climate change.

The physical risks arise from the increased frequency and severity of extreme weather events. These events, in combination with population growth and various socioeconomic factors, are likely to keep payouts climbing.

In addition to physical risks, insurers are faced with potential governmental responses to climate change that create conflicting policies across jurisdictions. This could lead to restrictions on insurers' ability to manage exposures and mandated coverages. There could also be higher compliance expenses, and costs for managing additional regulatory standards, especially those that require more disclosures.

Together these risks cannot be downplayed because they can have significant financial implications for insurers. However, the risks are well known, and managements have the right strategies and tools to deal with the issues.

But insurers also see underwriting and investment opportunities. They are expected to come (1) from policies with risk control and loss prevention services that help clients mitigate losses and build resilience, and (2) from coverages for emerging technologies and industries. Since insurers are also investors, they see opportunities in allocating capital toward energy transition and decarbonization efforts.

Mitigation and resilience

With higher climate-related losses in the future, mitigation will be critical to maintaining coverage availability and affordability. To do that the insurance industry will evolve from one that simply pays claims to one that has a critical role in helping policyholders reduce losses.

Hence, the key opportunity will come from designing policies with financial incentives to change behavior. This will be done not just for individuals and corporations, but also at the community level. Some resilient home incentives we expect to see expanded in the future are fire-resistant improvements, and elevated buildings in flood zones, among others.

In addition to offering products to reduce losses, insurers also see opportunities in designing coverages to enhance the ability to recover and adapt after events occur. Thus, insurers foresee the continued development of parametric insurance tied to climate indices (e.g., rainfall, temperature, and wind speed) that enables rapid payouts post events.

In addition to underwriting products, insurers see opportunities in offering comprehensive risk control and loss prevention services. These services include evaluations, technical information, consulting solutions, and educational resources to help clients reduce exposure to physical and climate risks.

Coverage of the latest technology

Insurers (as well as many others) expect that in response to climate changes there will be a gradual transition to clean energy technologies, infrastructure, and processes which will require insurance coverage.

These are some examples of what insurers note in their surveys:

• Renewable Energy Insurance: Coverage of onshore and offshore wind farms, solar farms, green hydrogen facilities, battery storage, hydroelectric plants, and energy conversion risks.
• Climate Technology Insurance: Coverage for climate technology developers, EV charging stations, and suppliers of solar panels and photovoltaic inverter solutions.
• Green Building & Construction Insurance: Product tailored for LEED®-certified construction, green upgrades in buildings, and construction projects focused on climate patterns like flood control, waterproofing, and fire safety.
• Electric Vehicle (EV) Insurance: Developing products and services to meet EV owners' needs, including specific EV policies, roadside charging coverage, and discounts for hybrid/electric vehicles.

On the investment side, insurers see climate change as an opportunity to generate attractive, risk-adjusted returns by deploying capital toward the global transition to a low-carbon economy, supporting emerging climate technologies, investing in green and municipal bonds, and enhancing community and infrastructure resilience.

Insurers anticipate that opportunities will continue to arise related to innovative technologies and solutions across a wide range of asset types. Among others, this includes support for climate change infrastructure, clean transportation, green buildings, pollution prevention, and sustainable water and wastewater management.

Conclusion

There is a natural tendency to view climate change negatively for insurers. But the world is adapting, albeit at a sometimes inconsistent pace, and the insurance industry has the opportunity to take a leadership role in this transition.

The bottom line is that insurance is a risk transfer business. Greater risks can lead to higher growth and enhanced returns if properly managed.

The Co-Op's recent cyber attack, costing the organization an estimated £206 million, is an urgent reminder that no amount of cybersecurity spending can guarantee total protection. Despite substantial investment in defensive technologies and training, a single sophisticated social-engineering attack was enough to trigger a major financial loss.

For many UK businesses, particularly SMEs, the Co-Op incident raises a concerning question: if a national giant with advanced defenses can fall victim, what chance do we have? Yet, according to industry data, one in three SMEs still operates without any cyber insurance. The misconception that strong cybersecurity alone is sufficient continues to leave firms dangerously exposed.

The evolving threat landscape

Cyber threats are no longer static, nor are they confined to traditional realms such as ransomware or data theft. We're now seeing the rise of AI-powered phishing campaigns, adaptive malware and real-time dark web trading of stolen credentials. These tools evolve faster than most defensive technologies can keep pace with.

Human-centric manipulation tactics are becoming more sophisticated and more complex to spot thanks to readily available Gen-AI tools. Malicious actors no longer need to breach technical barriers when they can simply influence an employee to grant access. The recent surge in Business Email Compromise (BEC) attacks exemplifies this trend: a persuasive message, an urgent tone and a brief lapse in vigilance can bypass even the most advanced security systems.

As these social engineering techniques evolve, conventional perimeter-based security measures are increasingly inadequate. The most vulnerable component is rarely the software itself, but rather the individual operating it. Stopping the breach is only the first step; protecting the business's financial stability that follows is just as critical.

Lessons from the Co-Op

The Co-Op incident illustrates a harsh truth: resilience can be incomplete without a financial safety net. Despite its advanced infrastructure, the organization lacked comprehensive cyber cover. That absence meant the full cost of response, recovery and reputational damage fell squarely on its balance sheet.

This is not an isolated case. Many businesses, large and small, still view cyber insurance as optional or redundant, something to consider after implementing technical controls. But as the Co-Op's experience highlights, even the best security architecture can't always account for human error, insider threats or sophisticated deception.

Cyber insurance is there, in the event of a breach, to support a business in minimizing the impact of associated losses. Best-in-class policies can cover everything from forensic investigations and legal costs to lost revenue, data restoration and communications support. In an environment where the average UK cyber incident now costs £10,830 for SMEs and well into the millions for larger firms, that safety net can be the difference between recovery and collapse.

The Trojan horse problem

Think of cybersecurity as the walls of a fortress, essential, strong and well-maintained. But history shows that many fortresses have fallen not because the walls were weak, but because someone unknowingly let the enemy in. A single misplaced click, a compromised supplier or an outdated plug-in can act as a modern-day Trojan horse.

Even companies with specialized IT departments, advanced monitoring systems and extensive backup plans are susceptible. The combination of opportunism, psychology and technology poses a threat that goes beyond simple external factors. Furthermore, when an incident happens, continuity, trust and cash flow are all at risk in addition to data.

Cyber insurance: a partnership, not a replacement

The narrative shouldn't be cybersecurity or insurance, but more like cybersecurity and insurance. The two are partners in resilience, not rivals. A well-designed cyber policy complements technical defenses by absorbing the financial shock that follows a breach, while also providing incentives for strong security controls through lower premiums and enhanced underwriting confidence.

Leading providers work closely with clients to provide risk management support, staff training and incident response planning. There are also policies that champion a collaborative model to ensure businesses aren't just insured, but genuinely prepared.

The road ahead for SMEs

For UK SMEs, the takeaway is clear. Cyber resilience is not just a technical issue; it's a financial and strategic one. With margins already stretched by inflation and economic uncertainty, few small businesses could absorb even a fraction of the Co-Op's losses.

Yet, too many still dismiss cyber insurance as an unnecessary expense. In reality, it can be the final line of defence, the parachute that ensures survival if prevention fails. As threat actors continue to evolve faster than any software patch can keep up, combining robust cybersecurity with comprehensive insurance is no longer optional. It's essential.

When it comes to cyber risk, perfection doesn't exist, only preparation does – that preparation must include both protection and recovery.

The unseen forces of AI and cloud computing are never out of the news, yet behind the headlines lies a story of growth and innovation as tangible as bricks and mortar. The heavy computing power required by AI workloads, and the growing global demand for AI technologies, have seen a building boom take place around the world as developers scramble to build the facilities required to meet these needs.

According to market research, up to $7 trillion will be spent on data centers by 2030 – a huge sum driven largely by technology companies in the US and China, while Europe lags a few paces behind. The tech industry's big three, Amazon, Microsoft and Google Cloud, accounted for almost two-thirds of global cloud revenue in Q2 2025. Combined with Chinese companies such as Alibaba and Tencent, their capital expenditure budgets for 2025 reach hundreds of billions of US dollars, much of it geared toward the industrial scale infrastructure and dependable energy sources that high-performance AI and cloud computing now demands.

The latest Allianz Commercial report, The Data Center Construction Boom, explores the extent of this global buildout and questions whether the building bonanza can last. Despite the expansion, several factors could limit growth, including the surging costs of construction. These have escalated dramatically from $200-$300 million to projects exceeding $20 billion. According to Allianz Commercial construction experts, average-sized facilities now cost between $500 million and $2 billion. Along with higher construction prices, the complex nature of data center construction and operation requires specialized insurance coverage for risks such as power supply concerns, faulty workmanship, fire or natural catastrophes.

Data centers are fueling the construction industry

A global buildout is underway to construct the infrastructure needed to support the digital economy. The US will be the largest market for data centers, covering about two-thirds of the total global data center power demand, with 81 gigawatts (GW) by 2028, while China's data center market is building out equally aggressively. Greater Beijing alone now accounts for roughly 10% of global hyperscale capacity. Europe is trailing behind the two superpowers but is experiencing a 43% annual increase in pipeline activity, with London and Dublin as the largest markets (each with over 1GW capacity), followed by Amsterdam, Frankfurt, Paris, and Milan.

The bigger data centers have a huge footprint. The scale of a $20 billion+ facility can involve tens of thousands of workers on site at peak times, with significant equipment and building supplies moving in and out. Timings can be tight. This requires expert coordination, as any missteps or faulty workmanship can lead to potential losses or costly delays.

Data centers' unique risk profile

Building a data center is a complex, multi-disciplinary undertaking, which presents a multitude of risks. One of the main issues is the soaring power demand that threatens to outpace grid capacity and infrastructure. The electricity demand from data centers worldwide is set to more than double by 2030, to around 945Twh. This is slightly more than the consumption of the whole of Japan today, with its population of 124 million.

To avoid power issues, which are the main source of significant outages, with 45%, data center operators are increasingly seeking to reduce their reliance on the grid by generating their own power onsite, including renewables, gas, and even potentially small nuclear reactors.

Fire, heat, and water are also significant risks for data centers, potentially leading to severe property damage or business interruption losses. Lithium-ion batteries are increasingly being used as backup electricity storage. The fire risk associated with these batteries is well documented, particularly in relation to electric vehicles and charging infrastructure.

Large data centers have increased cooling requirements that could drive up water and electricity demand. This may alter the risk profile of data centers and could contribute to an increase in construction and insurance costs. Clients need to work with an experienced team of underwriters who know the business and can support the project from beginning to end, including multi-year coverage and policy extensions as needed.

To read the full Allianz report, please visit https://commercial.allianz.com/news-and-insights/reports/data-center-construction-risks.html

Over the past two decades, building technology for complex, regulated industries, I've seen how legacy systems can persist long after the problems they were designed to solve have evolved. Workers' compensation is one of those systems. Built for a different era, it now faces a modern workforce shaped by hybrid schedules, shifting risk profiles, AI integration, and rising expectations from both employers and workers.

Workers' comp needs today go beyond incremental upgrades, calling for a strategic shift. The tools we rely on must reflect how people actually work: connected, digital, intelligent, and adaptable. That means platforms built on cloud-native infrastructure, automation that reduces rework and lag, intelligence that drives decisions, and real integration across every part of the claims lifecycle.

A System Under Pressure

While overall claim frequency has continued its long-term decline, dropping 5% in 2024 alone, according to the National Council on Compensation Insurance (NCCI), this doesn't necessarily signal improved efficiency. In fact, claim severity rose 6% in the same year, pointing to greater complexity and resource demands per claim. The trends vary widely by industry. NCCI data shows remote office workers continue to see lower claim rates, while sectors like private education have experienced increases, particularly related to workplace violence. Restaurant-related claims declined in both 2022 and 2023, whereas other hospitality segments remained unchanged.

This uneven landscape challenges systems that were never designed for such variability. The result is delayed resolutions and missed opportunities to prevent disputes or speed recovery.

The Case for Cloud-Native

Legacy infrastructure, while dependable in its time, now limits the adaptability required to meet today's evolving demands. In contrast, cloud-native platforms enable continuous improvement, universal access, and scalable performance. They reduce the friction of system upgrades, allow faster deployment of new capabilities, and offer enhanced data security and disaster recovery, essential for industries handling sensitive health and personal information.

When cloud-native architecture is combined with AI-driven automation, the impact becomes transformative. Claims processing can be streamlined end-to-end, accelerating settlements and improving accuracy. Regardless of role or organization, stakeholders across the claims lifecycle gain real-time access to shared data, enabling faster, more confident decision-making. Automated, auditable workflows ensure that key actions are completed without delay, minimizing errors and improving the overall experience for injured workers. Only a modern cloud foundation makes this secure, coordinated collaboration possible.

How Automation Unlocks Productivity

According to McKinsey, insurers that adopt end-to-end claims automation can reduce operational costs by up to 30% while improving claim settlement times and customer satisfaction.

By automating routine administrative tasks, professionals can redirect their time and energy toward higher-value work: applying judgment, building trust with injured workers, and resolving complex issues more effectively. Industry adoption is already underway. A recent survey by Guidewire and Celent found that over 60% of P&C insurers invest in claims automation technologies, prioritizing automated triage, intelligent document processing, and workflow orchestration. These tools reduce cycle times, eliminate rework, and enable consistent handling of high-volume claims. Some insurers have reported measurable gains: 20–30% faster settlements, 15% fewer processing errors, and stronger claimant satisfaction scores.

Making Data Work Smarter

Workers' comp generates immense data, but far too little informs real-time decisions. The next evolution involves applying intelligence to that data in practical ways. Predictive alerts can flag claims that are likely to escalate. Benchmarking can help leaders allocate resources more effectively. Pattern recognition can pinpoint the root causes of common delays.

More innovative data use means fewer surprises, more consistent outcomes, and better support for injured workers and the teams managing their claims. New actuarial models like Mixture-of-Experts (MoE), highlighted by the Casualty Actuarial Society, show how blending different predictive approaches can improve forecasting for claim frequency and severity. In parallel, academic research points to the value of connecting claims data with nontraditional sources to generate more accurate and timely loss estimates.

Why Integration Matters

Workers' comp involves a broad network of employers, TPAs, healthcare providers, attorneys, and vendors. Without integration, information gets trapped in silos, increasing errors and reducing transparency. Integrated systems, by contrast, allow for synchronized data, faster updates, and a more consistent experience for every stakeholder.

This also enables better accountability. When the entire claims process is connected, it's easier to track what's working, identify inefficiencies, and continually improve.

Looking Ahead

The way forward is clear. We need to build a workers' comp system that reflects the realities of today's workforce and the demands of tomorrow's economy. That means designing platforms supporting clarity, speed, coordination, and trust across every step of the claims process.

The system's pressures aren't easing, and expectations are rising. However, the opportunity ahead of us is significant if we align our strategy with the tools and insights already available.

We're not waiting for the future of workers' comp, we're building it now. The time for cautious upgrades is over. Let's deliver systems worthy of the people they serve.

Following last week's elections in the U.S., it seems that for every two pundits you find three opinions about what the elections tell us about the prospects for the midterms coming up in a year. But seemingly everyone has settled on a theme that politicians must and will hit hard: 

Affordability. 

That word is everywhere, and not just among Democrats, who used it to win elections for governor in New Jersey and Virginia, for mayor of New York City and for a host of down-ballot offices, including in areas thought of as Republican strongholds. President Trump has vowed to wrest ownership of the affordability theme from Democrats, and a high-profile acolyte of his running for governor of New York says affordability will be her watchword, too — as does the current governor, who will represent the Democrats. Everybody loves affordability.

No matter who wins the arm wrestling match, you can be sure that billions of dollars in ads will run in the next 12 months hammering home the need to make goods and services more affordable in the U.S.

That represents an opportunity for the insurance industry. 

If you want to understand more about how hard politicians will push on affordability for the next 12 months, here are two good articles: 

• The Atlantic explores how Democrats turned affordability into victories across the board, adapting the core theme for a wide variety of offices in a host of different political environments. The article warns that the winners now have to live up to their ambitious promises, which will likely be hard to do. "Politics isn’t just about the words you put on your bumper stickers," the article cautions. "It’s about what you do if the bumper stickers work." But the writer predicts that affordability will show up in just about every imaginable form in Democratic campaigns across the country.
• The New York Times documents how Trump, and by extension the whole Republican party, has repeatedly claimed that everything is already affordable. Even though economic cheerleading by the Biden administration and the Harris campaign failed to convince voters in the 2024 presidential election, Trump has strained to create an alternate reality where grocery prices are down (they're up 3.1% in the past year), where inflation has disappeared (it's 3% in the past 12 months and has been rising, albeit slowly), where gasoline prices are plunging toward $2/gallon (they're down a penny since Trump took office, at $3.08/gallon, and show no signs of plunging), etc. He will surely continue making up numbers — he always has  — but since last week's elections, the Times says, Trump has declared that "affordability" is "a new word" and has vowed to claim it for his own. Buckle up.

I think insurers can ride this wave.

That may seem like a stretch at a time when rising car prices and disrupted supply chains have combined with some dangerous driving habits to send auto claims soaring and when natural disasters have exacerbated similar inflationary issues for homeowners insurance. There are obvious limits to what the industry can do to make insurance more affordable. Rates have to be sustainable. 

But policyholders are already sensitized to rising insurance premiums, and the drumbeat will continue. Just in the past week, the National Association of Realtors reported that the average age for a first-time home buyer in the U.S. has soared to 40, from 33 in 2020 and 28 in 1991, and insurance premiums are part of the problem. The flood of advertising and attention by politicians in the next year will, if I'm remotely correct, heighten the concerns. So the industry can either ride the wave or be swamped by it. I vote for riding, both by creating messaging about helping policy holders manage costs — and then by doing it. 

For carriers, maybe the best approach is to emphasize the Predict & Prevent model as much as possible. Homeowners insurers can subsidize sensors that detect the potential for fires and water leaks, or even offer them for free if that makes economic sense — as it increasingly does. Carriers can alert people to anything about their properties that increases exposure to wildfires, floods or storms, while offering discounts if the homeowner takes appropriate action. Even outside the insurance equation, carriers could offer advice on preventive maintenance or anything else that could help people manage down costs. 

Auto insurers could emphasize programs to help people drive safely (and lower premiums), to prevent theft, to get the car under cover as a hailstorm approaches, and so on. Anything to show customers that we're on their side as much as we can be.

Claims departments and third-party administrators could work even harder than they already are to expedite payments — and let the world know about their efforts — because people are feeling financial pressure. If there's a way to advance even a partial payment, that would help some people a lot.

Agents and brokers are where the rubber meets the road. They can pay even more attention to financial anxieties among clients and help them maintain the coverage they need while minimizing increases in premiums. 

Those are just some very rough ideas. I'm sure you have better, more sophisticated ones than I do.

My point here is just to note that affordability is going to be a hot button for at least the next year, if not longer. We should get in front of the issue.

Cheers,

Paul

P.S. There's an old joke about a young politician asking an aging veteran what the secret of his long-term political success has been. 

"Sincerity," the old politician replies. 

"Once you learn to fake that, you've got it made."

To be clear, I'm not suggesting faking anything. A) It's the wrong thing to do. B) The industry couldn't get away with it. 

I'm saying insurers should do whatever they can to address the affordability problem — then take all due credit.