Despite more than a decade of awareness, underinsurance in business interruption (BI) policies remains stubbornly common—and costly. A Marsh study citing Chartered Institute of Loss Adjusters (CILA) data found that 40% of BI declarations were undervalued, with average underdeclarations of 45%. That was in 2012. Recent publications from CILA and the Insurance Institute of London (2024) suggest little has changed.

Insurers continue to see claims where declared gross profit is well below actual exposure, leaving policyholders exposed to reduced payouts, co-insurance penalties, or outright denial.

Why Declarations Miss the Mark

Five recurring issues explain most inaccuracies:

1. Terminology Confusion: In everyday accounting, "gross profit" has one meaning. But "insurable gross profit" is defined differently. For example, it usually leaves out things like investment income but adds back certain expenses that don't appear in normal accounts. If a business uses the wrong definition, its insurance declaration can end up way off the mark.
2. Optimism Bias: Owners assume best-case recovery scenarios, underestimating the duration and financial effect of disruption.
3. Documentation Gaps: If a business doesn't keep detailed or accurate financial records, it becomes very hard to show what the real losses are. Missing information weakens the claim and makes it less believable to the insurer.
4. Operational Blind Spots: Losses from supply chain interruptions, third-party dependencies, or contingent exposures are frequently overlooked.
5. Policy Misunderstandings: Many businesses don't fully understand the fine print in their policies. Things like what counts as extra costs, how savings are treated, or how long cover lasts can easily be misread—leading to claims that don't match what the policy actually covers.

Example of understatement: A mid-sized manufacturer declared its BI exposure based only on its own factory operations, overlooking its heavy reliance on a single overseas supplier. When flooding shut down that supplier, losses mounted well beyond the declared amount—triggering co-insurance penalties and a settlement that was less than half of the actual loss.

Overstatement: The Other Risk

While underdeclaration dominates discussions, overstating a claim can be equally damaging. Inflated or poorly substantiated submissions lead to:

• Frustration and mistrust from insurers
• Reduced settlements or outright repudiation
• Unrealistic expectations by policyholders
• Delays and costly disputes

Example of overstatement: A retail chain calculated its BI loss using projected sales growth from a planned expansion that never materialized. The claim—based on future revenue rather than proven historical trends—was challenged, leading to months of delay and a significant downward adjustment.

Insurers scrutinize every figure. If projections lack evidence or deviate from past performance, credibility suffers—and so does the claim.

Continuing Submissions and the Duty to Mitigate

Another area where claims often falter is timing. Many businesses wait until operations are fully restored before submitting their BI claim, presenting it as a single package. This approach creates delays, invites disputes, and risks misinterpretation of losses.

Best practice is to submit the claim on a continuing basis, updating the insurer regularly as losses and mitigation steps are incurred. Interim submissions allow the insurer to review, comment, and agree on methodology early, reducing the possibility of disputes at the final stage.

Policyholders also carry a clear duty to mitigate their loss. For example, if a critical machine component fails, flying in the replacement part from overseas may cost $50,000. If doing so shortens downtime and reduces the BI loss by $200,000, the insurer will cover the cost. However, if the part costs more to fly in than the BI loss it prevents, the insurer is unlikely to reimburse the expense.

Example: Fire in a Restaurant Kitchen

A mid-sized restaurant suffers a fire in its kitchen when a deep fryer malfunctions, damaging the cooking equipment and forcing the kitchen to shut down for repairs.

Rather than closing completely, the owners hire an external commercial kitchen across town to prepare meals. They then transport the food back to the restaurant, allowing them to stay partially open and continue serving customers.

• Effect if they closed: With no income, the restaurant could have lost $15,000 per week in revenue.
• Mitigation step: Renting the external kitchen and arranging food transport costs $5,000 per week.
• Insurance treatment: Since this reduces the size of the business interruption claim (the restaurant still generates revenue), the insurer covers the reasonable costs of the external kitchen.

This example highlights two key points:

1. Duty to mitigate – The restaurant took steps to limit its loss rather than shutting down completely.
2. Communication with insurer – By discussing the plan with the insurer before committing, the restaurant ensured the extra costs would be covered.

This example highlights why communication is critical throughout the process. Keeping the insurer informed ensures that mitigation measures are agreed to in real time, rather than disputed after the fact.

Whose Job Is It, Anyway?

That need for early engagement and clear communication leads to another common misconception: who is actually responsible for compiling the claim. Contrary to what many policyholders believe, responsibility rests with the insured, not the loss adjuster.

Adjusters may adjust upward where valid losses are overlooked, but their primary role is to challenge unsupported or overstated items. Notably, companies that under- or over-declare are often those that attempted to prepare the declaration themselves, without specialist knowledge of BI policy wording. Doing so not only increases the likelihood of error but can also jeopardize the credibility of the entire claim.

The Forensic Accountant Advantage

Forensic accountants bridge the gap between accounting records and insurance requirements. They:

• Translate financials into insurance terms aligned with policy wording
• Quantify loss precisely through historical analysis, forecasting, and modelling
• Build credible submissions supported by data, documentation, and rationale
• Safeguard declarations by properly quantifying exposure and reducing the risk of penalties or claim rejection

Claims Preparation Costs: A Hidden Benefit

Many policies now include professional fees or claims preparation costs coverage. This provision reimburses the insured for professional fees incurred in compiling and substantiating a claim—including the services of forensic accountants.

Depending on the policy, limits for claims preparation costs can range from $25,000 to over $100,000, more than enough to cover expert involvement from start to finish. For businesses, this often means there is no direct cost to accessing professional support.

Yet too many businesses either overlook this coverage or attempt to "go it alone," exposing themselves to underpayment, disputes, or rejection of their claim.

Final Word

CILA's recent efforts to standardize policy wording are welcome, but the accuracy of BI claims still rests with the insured. Both understatement and overstatement carry significant risk—underpayment on one hand, repudiation on the other.

With financial stakes this high—and with claims preparation costs frequently covered—engaging a forensic accountant isn't just best practice. Submitting claims progressively, maintaining open dialogue with insurers, and taking well-documented mitigation steps are the keys to a faster, fairer settlement.

In short: Get expert help early, keep communicating, and mind the gaps.

Today's digital giants didn't just change the game, they rewrote the rules. They turned customer insight into capital, behavioral data into billion-dollar products, and user experience into enduring brand loyalty. They've built trillion-dollar empires by knowing their customers better than the customers know themselves.

It's mad to think that there's only a handful of these ecosystem drivers that include the likes of Amazon, Alibaba, Apple, and Google. But that's not the craziest part. What's incredible to me is that these ecosystems don't exist in insurance. After all, what these established ecosystems do well is simply to maximize the value of a customer by maximizing their value to a customer. This is achieved through continuous, data-driven innovation and activated through a well-orchestrated ecosystem of partners.

Now consider insurance: an industry that holds more data than most tech platforms could dream of. Not just consumer data but also operational, behavioral, environmental, and risk data. To top it all off, even more data is only an arm stretch away and available from connected cars, smart home devices, wearables, and IoT systems.

The insurance industry collects fresh, high-value insights from millions of interactions every single day, yet most of it sits idle, trapped in outdated systems, fragmented across silos, and rarely used to its full potential. This is a massive missed opportunity, and it's not a stretch to say that the sector really does have the opportunity to emulate e-commerce's proven, multitrillion-dollar, customer-centric business model.

However, the issue is far more than a technology change. This shift and the huge commercial upsides that accompany it require a business model and mindset change. Rather than seeing customers as policyholders, insurers must recognize them as the central product. By harnessing the extensive data sets at their disposal, the sector can create hyper-personalized experiences, optimize pricing strategies, and drive entirely new revenue streams.

This customer-centric shift isn't just about meeting consumer demand for digital services, it's about fundamentally reshaping profitability by applying a successful, established approach.

There are many ways that these business models drive growth through value creation. Building around the customer means you integrate experiences, partners, products and services around people. However, insurers are not typically built this way. Most are built in legacy policy administration systems with data models that sit atop, trying to abstract that policy-focused data into a customer cohort, drive insight and then reapply it back into experiences.

This is far too slow. Like a hot sales lead, customer data is a perishable asset. Its value fades fast if not acted on in the moment. Customers want buying insurance to be fast and frictionless, without being dragged through a long list of opaque, hard-to-answer questions. When we are in a claims process, we need insurers to see and respond to our data in real time. Even when we are being sold new coverage, we ideally need it a few clicks away, or, better still, embedded and in context. And when we move to experiences centered on helping us understand, navigate or even mitigate risks, we need that real-time, too. Tomorrow is nearly always too late.

But this isn't just about speed or seamless claims. It's about making sure the cover we receive fits our individual needs, now and as they evolve. It's about removing stress from the claims experience, not adding to it. And it's about transforming renewal from a transactional moment into a meaningful interaction.

That means having the data to offer genuine advice, based on how a customer's life has changed, or, better still, eaching out when a change is detected through partner data. That's what it means to value a customer - using insight to anticipate their needs, build trust, and position the insurer as a true partner - not just a silent presence that reappears at renewal time with a price hike.

In essence, insurers must massively increase their knowledge of their customers, not just acquire their data. Insurers must then act on this knowledge through embedded, adaptive, and risk-mitigating propositions that meet the demand of dramatically changed demographics, economics and lifestyles.

This requires a business model change, enabled by a new technology foundation and driven by an evolving culture. Core technology built for insurers - especially when built on MACH foundations and designed to function like a true ecosystem driver - can only realize its full potential if it's matched by changes in mindset, structure, and culture.

• No more silos. Everyone in your organization needs to be customer only, not just customer first. Teams must look and act more like agile software development squads than artificial clusters of mixed functions. Actuaries, developers, product owners, experience designers, data engineers, etc., must all work together constantly with clear goals and outcomes.
• Change must become a constant, and roles must move from operational management to customer experience improvements. Claims handling becomes claims optimization.
• Experimentation needs to rise dramatically. Learning fast means never failing. Where all data is mined as a perishable asset and acted on, this includes ways of making people's understanding, use, and experience better, as well.
• Technology must become an enabler of new, unimagined futures, not just an operating entity and IT constraint. Any line of insurance and even complementary non-insurance products needs to be managed and operated from one core platform. There can be no IT bottlenecks or downtime for any reason. Where interoperating partners aren't just about application programming interface (API) models, the issue is about how quickly those partnerships can be applied to experience outcomes.

All of this needs to happen in a business model where the time to generate value from new insights is attainable in minutes, not days.

There's a compelling commercial imperative behind all of this. Happy customers, who easily self-serve through digital interactions and access human support when it counts, are more satisfied and more loyal. Ultimately, they form more trusted relationships and will buy and do more with their insurer.

When your customers buy multiple things from you, their value over risk will start to look far more interesting. We aren't just talking about "multi-car" type propositions, as useful as they can be, we are talking about insurance portfolios or relationship products.

Take life insurance, a market set to transform over the next 18 months to five years. It suffers from increasingly low relevance and low penetration rates. Lifestyles, demographics and life stages have changed dramatically. The propositions this market offers should change as well, adapting as people's financial and health profiles change. Current products, sold once and then engaged when someone dies, need to give way to more holistic protection and life models.

Perhaps underwriters and actuarial roles will finally be fused with customer experience and analytics functions, creating holistic models that when combined will stretch far beyond "policy" thinking.

However, as a result of this need for technological and business model shifts, insurers with their current legacy and modern legacy footprints will struggle. As it is, adaptivity is too slow and expensive. New insurers are emerging, and the market dynamics are now forcing legacy insurers to change -- from regulation asking them to treat customers better, all the way through to new digital and intelligently orchestrated experiences.

Insurance has a new battleground. Deeper relationships, more loved products and services, generating more value through more propositions. This has to replace price-led competition.

The value chain model is broken. Ecosystems aren't optional, and customers aren't things you bolt on to your technological core. They should sit at the center, and everything should interoperate around them.

The reality is that even if you want to operate as a "value chain" business, your best way of minimizing costs and maximizing distribution still lies in being able to value customers and service them in any channel, 24/7, in an increasingly intelligent and personalized manner.

This is the new commercial battleground for insurers. It seems most don't realize it yet. But the emergent competitive forces are beginning to bite, and we see many new emergent forces acting on the industry. Shareholders will start to see this gap, along with capital investors who are already diving in.

We are in exciting times for an industry long held at a tipping point.

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MIT's NANDA Project—established to help drive AI integration in enterprise settings—recently released its mid-year report. The key finding is stark: Despite $30-$40 billion in enterprise investment into generative AI, 95% of organizations are getting zero return.

From the report: "The core barrier to scaling is not infrastructure, regulation, or talent. It is learning. Most GenAI systems do not retain feedback, adapt to context, or improve over time."

This admission departs sharply from the GenAI industry's long-held narrative that scale—more infrastructure, more training data—is the key to success. Thus, Big Tech has funneled over $500 billion into new AI datacenters over the past two years, betting that technical expansion alone would lead to better outcomes.

Blaming the technology and the technology alone for the 95% failure rate would be a mistake. Organizational realities must also be considered.

The Garbage Can theory—a seminal framework introduced by Michael D. Cohen, James G. March, and Johan P. Olsen in the early '70s—sees organizational decision-making as a random, chaotic process where problems, solutions, and decision-makers mix like garbage in a can. Decisions are often made not through linear analysis, but when a pre-existing solution (a technology, a pet project) goes looking for a problem to solve, and they connect at the right moment.

In "organized anarchies"—such as the insurance enterprise—decisions surface more from political realities, business urgencies, happenstance, and fragmented routines than from structured analysis.

MIT NANDA's findings reveal that AI pilots frequently reflect this "garbage can" environment. Rather than deploying disciplined, contextualized programs, organizations launch generic AI tools with unclear goals, disconnected stakeholders, and insufficient governance. High failure rates stem from this context vacuum: Solutions chase problems but lack clarity on objectives or pathways for integration.

Where measurable success emerges, automation is tightly linked to specific workflow tasks—especially in finance, HR, and operations. In these areas, context and routine enable AI to deliver quantifiable savings and efficiencies, making back-office automation a financial standout.

In contrast, customer-facing applications often attract investment due to hype but rarely deliver robust returns. These projects suffer most from the garbage can effect: fragmented pilot teams, fluctuating requirements, and poorly defined goals.

The lesson is not that AI lacks potential but that organizational learning and context are prerequisites for meaningful automation. The prevailing narrative in AI casts it as a source of algorithmic precision, promising to banish organizational mess. But the garbage can will abide. The deeper challenge of AI adoption is organizational, not technological.

Deployed naively, AI becomes just another item in the garbage can—an expensive tool in search of an application, championed by some departments and ignored by others. The outcome: fragmented initiatives and wasted investment.

The best results always come when humans and AI collaborate, with humans providing context and ethical nuance, and AI bringing data-scale and pattern recognition. Ultimately, the strategic imperative is not simply to "implement AI" but to orchestrate its confluence. Consider these three recommendations:

• Ask: "What does it improve, and by how much?" Focus on business outcomes before technology. Pick a metric and desired result, first.
• Frame problems, not just solutions. Rather than asking "What can AI do?" define critical business problems, then determine how human-AI collaboration can address them.
• Create deliberate choice opportunities. Design forums—cross-functional teams, innovation labs, strategy sessions—where problems and solutions connect intentionally, reducing randomness and supporting strategic adoption.

Human catalysts—those with fusion skill sets—are the drivers. Investments in training and culture change should always exceed spending on the technology itself.

For the longest time, the basic approach to developing an AI was for the humans to teach the machine everything they could, then have the software take it from there. That approach worked. It's how IBM's Deep Blue defeated world chess champion Garry Kasparov in a six-game match in 1997 and how Google's DeepMind's Alpha Go defeated arguably the world's top Go player in five games in 2016. 

Then the scientists had a different idea: What if they let the AI learn entirely on its own, without regard for any human preconceptions, after just being given the rules of a game? That worked even better. By playing millions of games against itself, what DeepMind called Alpha Go Zero learned Go so well in three days that it defeated Alpha Go in 100 straight games. 

DeepMind then went the next step and developed an AI that hadn't even been taught the rules of Go. It trounced Alpha Go Zero. 

DeepMind is taking that sort of approach with hurricane forecasting. Rather than use the traditional approach — feeding massive amounts of data to supercomputers loaded with physics equations that spend hours and hours calculating forecasts for storms — DeepMind left out the physics equations piece, as well as all other guidance. Basically, DeepMind says: Here is all the data we have on hurricanes. You figure out what it means for future storms. 

The approach has shown promise with earlier storms, and DeepMind's AI just nailed the forecast for Hurricane Erin, outperforming both the official, supercomputer-based forecast and other commonly used models.  

Let's have a look at how far the AIs have come, so very fast, as well as where they can go from here. 

The promises of the deep learning approach first showed up on my radar not quite two years ago. In September 2023, I wrote a commentary lauding what advancements in supercomputing and satellite imagery were doing for forecasting. Just a month later, I found myself writing about AI models that, according to the Washington Post, had shown during that hurricane season that they "portend a potential sea change in how weather forecasts are made."

Now, Ars Technica reports that Google's AI outperformed the official forecast and numerous other of the best physics-based models on both intensity and the storm track, even after the other models were corrected for known biases. 

The article notes that the outperformance occurred with predictions reaching out to as much as three days ahead, while the most important forecasts are those three to five days ahead, because that's when many key decisions about evacuations and other preparations are being made. 

"Nevertheless," Ars Technica says, "the key takeaway here is that AI weather modeling is continuing to make important strides. As forecasters look to make predictions about high-impact events like hurricanes, AI weather models are quickly becoming a very important tool in our arsenal.

"This doesn't mean Google's model will be the best for every storm. In fact, that is very unlikely. But we certainly will be giving it more weight in the future.

"Moreover, these are very new tools. Google's Weather Lab, along with a handful of other AI weather models, has already shown equivalent skill to the best physics-based models in a short time. If these models improve further, they may very well become the gold standard for certain types of weather prediction."

Let's hope that the AIs continue their remarkable progress and, if so, that the public comes to trust them. A lot of damage and injury could be avoided.

In the meantime, fingers crossed that this year's hurricane season stays relatively quiet. 

Cheers,

Paul

The property and casualty (P&C) insurance industry is approaching a tipping point in 2026. With climate risk intensifying and social inflation pressuring loss costs, insurers are grappling with mounting challenges while also exploring innovative strategies to stay competitive and sustainable. The landscape demands a transformation in how insurers assess, price, and manage risk.

Climate Risk Is No Longer a Future Concern

The frequency and severity of natural disasters are escalating, with 2024 and 2025 witnessing record-breaking events — from wildfires in North America and Europe to cyclones and flooding in Asia-Pacific. These catastrophes are not just more frequent; they are affecting regions previously considered low-risk, undermining the validity of historical models.

Impact on P&C Underwriting:

• Traditional catastrophe models are under strain, often failing to capture emerging patterns in climate volatility.
• Reinsurers are tightening terms and increasing pricing, leading to cascading effects across primary insurers' balance sheets.
• Geographic diversification is no longer a foolproof strategy. Risk zoning must become hyperlocal and dynamic, factoring in real-time climate intelligence.

In response, insurers are investing in climate-tech partnerships to refine modeling, using satellite data, AI-powered weather forecasting, and climate scenario testing to redefine risk pools and set more accurate premiums.

Social Inflation and Litigation Trends

Another less visible but equally threatening pressure is social inflation — the rising cost of insurance claims due to increased litigation, larger jury awards, and changing societal attitudes toward corporate accountability.

What's driving it?

• Plaintiff-friendly legal environments and third-party litigation financing.
• Higher medical costs and longer case durations.
• Juror sentiment increasingly siding with individuals over institutions.

These factors are particularly pronounced in liability and commercial auto segments, where loss ratios are deteriorating despite premium hikes.

Insurer responses include:

• Expanding policy exclusions or tightening terms and conditions.
• Enhancing claims triage with analytics to identify fraud or high-exposure cases early.
• Collaborating with legal experts to track regional litigation risk indicators and adjust reserves accordingly.

Evolving Regulatory Expectations

Governments and regulatory bodies are also stepping in, demanding that insurers play a bigger role in climate adaptation. In the U.S., NAIC climate risk disclosures are becoming more stringent. In the E.U., Solvency II enhancements include stress testing for environmental risks.

Insurers are expected to:

• Integrate ESG (environmental, social, and governance) risk assessments into underwriting.
• Demonstrate long-term solvency resilience under various climate scenarios.
• Offer inclusive insurance products, especially for vulnerable populations.

This shift is pushing insurers to adopt a dual mandate: protecting their own solvency while supporting societal adaptation to climate change.

Technology as a Strategic Imperative

In the face of these mounting challenges, digitization is not just about efficiency; it's a lifeline. Advanced technologies are enabling the P&C sector to build resilience and adaptability into their core functions.

Key enablers include:

• Geospatial Analytics: Delivering risk intelligence for property underwriting and claims by combining satellite imagery with AI.
• Predictive Claims Models: Reducing costs and enhancing accuracy in reserving by predicting litigation probability and settlement values.
• Blockchain and Smart Contracts: Particularly in commercial lines, streamlining policy administration and minimizing disputes.

More carriers are turning to parametric insurance models that trigger payouts based on predefined events (e.g., wind speeds, rainfall thresholds), reducing uncertainty and administrative burden.

Redefining Customer Engagement

As climate risks grow and insurance becomes more expensive, consumer trust is at stake. Policyholders expect transparency, fairness, and proactive service — especially after experiencing a catastrophic loss.

Insurers must evolve from payers of claims to partners in resilience:

• Offer risk mitigation tools like smart home sensors or wildfire defense services.
• Deliver digital-first claims experiences with real-time tracking and automated loss assessments.
• Communicate coverage limits and exclusions clearly to prevent disputes at the time of need.

Personalization — driven by behavioral data and lifestyle insights — will be the hallmark of customer loyalty in 2026.

The Road Ahead

The P&C insurance sector in 2026 is being reshaped by macro forces beyond its control — but not beyond its influence. By embracing innovation, transparency, and climate-conscious practices, insurers can transform these risks into opportunities.

Key priorities for insurers moving forward:

• Rebuild pricing and risk frameworks to reflect future climate, not just the past.
• Tackle social inflation with advanced claims analytics and legal insights.
• Drive digital transformation to enhance agility and customer experience.
• Prepare for regulatory shifts by embedding sustainability in enterprise strategy.

Those who respond with bold, data-driven strategies — while staying grounded in the principles of fairness and protection — will define the future of property and casualty insurance.

In the ever-evolving landscape of the insurance industry, 2025 marks a transformative year where artificial intelligence (AI) and data analytics have emerged as indispensable tools in redefining how risk is understood, assessed, and managed. This shift is not just incremental—it's foundational, changing the DNA of insurance products, operations, and customer experiences.

From predictive underwriting to hyper-personalized policies, the integration of smart technologies is enabling insurers to become more agile, customer-centric, and resilient in a rapidly changing risk environment. Let's explore how AI and data analytics are reshaping the concept of risk in the modern insurance landscape.

The Age of Predictive Risk Management

Traditional insurance models largely relied on historical data and actuarial tables to price risk. But in 2026, these models are being outpaced by predictive analytics powered by real-time data and machine learning algorithms.

Using vast amounts of structured and unstructured data—from IoT devices, social media, telematics, wearables, and third-party sources—insurers are now predicting not just what might happen, but when and why. This allows for real-time, dynamic risk modeling that is far more nuanced and accurate than ever before.

For example, AI models can now detect subtle behavioral cues from driver telematics to assess real-time accident risk. Health insurers, too, are using biometric data and lifestyle tracking to anticipate chronic illnesses, enabling earlier interventions and better risk pricing.

Hyper-Personalization of Insurance Products

The "one-size-fits-all" approach is quickly becoming obsolete. Thanks to AI-driven customer segmentation and behavioral analysis, insurance in 2025 is increasingly tailored to individual lifestyles, preferences, and risk profiles.

Usage-based insurance (UBI) for auto, pay-as-you-go travel insurance, or real-time-adjusted health policies are just the tip of the iceberg. Smart homes equipped with IoT sensors offer property insurers insights into how risk fluctuates over time, enabling micro-adjustments to premiums or coverage on the fly.

This not only improves customer satisfaction by offering transparency and fairness but also ensures better alignment between risk exposure and insurance coverage, reducing adverse selection and fraud.

Claims Processing Gets an AI Makeover

Claims management, historically a manual and paper-heavy process, is now being revolutionized by AI and automation. In 2025, the average claims cycle is significantly shorter thanks to robotic process automation (RPA), AI image recognition, and natural language processing (NLP).

Take, for instance, an auto accident claim. AI tools can analyze photos of vehicle damage, match them to repair estimates, and process payouts within minutes—all without human intervention. Virtual assistants, powered by NLP, handle routine customer queries, schedule inspections, and provide status updates.

Beyond speed, AI also helps reduce fraudulent claims by identifying anomalies or unusual patterns in real time, flagging suspicious activity for human review. This drives down loss ratios and builds more trust with policyholders.

Dynamic Underwriting and Real-Time Pricing

The role of the underwriter has evolved from a periodic evaluator of risk to a continuous manager of it. Thanks to AI, underwriting is no longer a static function. Instead, it is a living process, informed by real-time data and adaptive learning systems.

Underwriters in 2025 are equipped with intelligent dashboards that integrate multi-source data feeds—climate models, market trends, cyber threat intel, etc.—to adjust risk scores dynamically. AI suggests optimal pricing strategies and recommends policy changes, minimizing exposure while maximizing profitability.

In commercial lines, particularly for complex risks like cyber insurance, AI is helping insurers offer real-time risk assessments and conditional coverage models that change based on threat landscapes or company behavior.

The Rise of Explainable AI in Insurance

As AI models become increasingly complex, the demand for transparency and regulatory compliance grows. Explainable AI (XAI) is a key focus in 2026, helping insurers understand and justify decisions made by algorithms.

Whether it's denying a claim, adjusting a premium, or flagging a high-risk policyholder, insurers must now provide clear, human-readable explanations. This is crucial for customer trust, regulatory compliance (especially under data protection laws like GDPR or India's DPDP Act), and internal governance.

XAI frameworks are embedded in most insurance platforms, ensuring every decision is auditable and fair—an essential step toward ethical AI deployment in risk management.

Mitigating Emerging Risks With AI

The 2025 risk environment is marked by volatility—from climate change and geopolitical instability to cybercrime and supply chain disruptions. Insurers are turning to AI not only to assess but also to mitigate these emerging risks.

For example, AI-powered climate models help property insurers predict flood zones and wildfire risks with unprecedented precision, allowing for risk avoidance strategies. In cyber insurance, machine learning monitors clients' digital infrastructure for vulnerabilities and offers real-time recommendations to harden systems.

Thus, insurers are no longer passive responders to risk—they are becoming active partners in risk prevention and resilience.

Ethical and Workforce Implications

As smart technologies take over routine tasks, the role of human workers is evolving. The insurance workforce in 2025 is increasingly focused on strategic, ethical, and creative responsibilities—interpreting AI insights, ensuring fairness, and maintaining the human touch in digital experiences.

However, there are also challenges. Data privacy, algorithmic bias, and the digital divide raise ethical concerns. Insurers must invest in responsible AI governance and continuous upskilling of their workforce to balance innovation with integrity.

Final Thoughts

Smart insurance in 2025 is not just a digital facelift—it's a fundamental rethinking of how risk is perceived, priced, and managed. AI and data analytics are enabling insurers to shift from reactive loss payers to proactive risk partners.

The winners in this new era will be those who combine technological prowess with ethical foresight and human empathy. In doing so, they won't just reshape risk—they'll reshape trust in the insurance industry for generations to come.

International broking isn't a workflow. It's a field of tension. What matters isn't just what gets done but how pressure moves—who absorbs it, who deflects it, who delays it, who misreads it. It's not a chain. It's a net. And to understand why things hold or break, you need to see how that net is structured. That's why I started thinking about adjacency. Not in the social sense but in the structural one. Who is connected to whom? How tightly? With how much strain?

Most systems are described by function. This person issues quotes. That one tracks claims. Another handles renewals. But function hides friction. It doesn't explain why one delay ripples across five countries, or why silence in one actor causes overload in another. That's what adjacency captures. The weight between actors. The frequency of exchange. The risk of misunderstanding. When you model the system this way, pressure becomes visible. Saturation becomes measurable. And the broker's role becomes clearer—not as a manager of tasks but as a regulator of saturation.

I began sketching this model because so much of my daily work felt reactive but patterned. Problems didn't arrive randomly. They clustered. They recurred in the same configurations. A late local quote always triggered the same escalation path. A client silence always led to double communication loops. I wasn't just handling issues. I was trapped in a structure that kept redistributing the same pressure, slightly out of phase. That realization changed how I viewed broking. I stopped thinking in terms of performance and started thinking in terms of system behavior.

So I started mapping. Not to map people but interactions. Each actor in the system became a node: the client HQ, the local client, the master broker, the local broker, the master insurer, the local insurer. Then I assigned each a state. Not a rating but a vector of current conditions: interpretive clarity, communication load, response time, information coherence. And between each actor, I mapped the strength and weight of their connection. Some links were active and dense. Others were latent or volatile. What emerged was a pattern not of workflow but of structural exposure.

The adjacency matrix became a way to see how pressure might travel. It showed me where a shock—like a delayed quote or a misinterpreted clause—would move next. It showed me which links had no buffer, where signals would distort, where trust was brittle. More importantly, it showed me where I was sitting in that map: often at the junction of multiple high-weight, high-volatility links. Not by accident. By design. Brokers are where the pressure pools.

If we don't see that, we misinterpret our own experience. We treat overload as a personal failing. We treat delay as someone else's problem. We act as if the system is breaking, when it's behaving exactly as built. The adjacency model gives language to that. It doesn't assign blame. It shows flows. And in those flows, you can intervene strategically.

What does that mean practically? It means we stop trying to speed everything up. Instead, we start redistributing attention. If one node is overwhelmed, we soften adjacent links. If a local broker is under-engaged, we increase narrative contact before the next placement. If an insurer is slow, we don't escalate immediately. We look at what weight we've asked them to carry and whether that weight is legible. Pressure isn't the enemy. Disproportionate pressure is.

This changes how we train teams. We stop treating coordination as admin and start treating it as structural regulation. We ask brokers to track state, not just tasks. To notice when narrative begins to slip. To anticipate which links will hold under ambiguity and which will collapse. That kind of awareness can't be programmed. It has to be developed through structural thinking.

It also changes how we talk about performance. A renewal that happens late but with aligned expectations may be a better outcome than one that's fast but misaligned. A claim that takes time but builds trust may protect the account longer than one that's settled quickly but leaves narrative damage. When we model adjacency, we're not tracking efficiency. We're tracking coherence. And coherence is what makes systems stable.

Most of all, it reframes the broker's identity. We're not just project managers or service agents. We are interpreters of saturation. We don't control the actors. But we shape how they interact. And when we do that well, the system flows even under pressure. When we don't, the system still moves. But it drifts. And drift is harder to detect than failure.

So this is why I model. Not to simulate reality. To sharpen perception. The matrix isn't the work. But it reveals the hidden shape of the work. It reminds me that what holds a program together isn't the documents, the trackers, or the milestones. It's the logic of adjacency—the way pressure moves between people, and the broker's role in absorbing just enough of it to keep the structure intact.

When you start thinking like this, things look different. Silence becomes signal. Delay becomes weight. Misunderstanding becomes distortion. And the broker stops being the one who makes the system work. The broker becomes the one who keeps it from breaking. That shift—from control to containment, from management to interpretation—is where the real work happens. And the more we can make that legible, the better our systems will hold.

Paul Carroll

It was almost exactly a year ago that I attended a gathering you helped put together in Atlanta for a group that helps universities and insurers collaborate on research concerning climate risk, so this feels like a great time to catch up. What would you say are the major advances in the past year in making the world more resilient, and in the insurance industry’s efforts on that front?

Francis Bouchard

Things are starting to coalesce. As someone who's been active in this space almost exclusively for four years, I'm starting to see some real positive signs. Some of that is from insurers themselves, who are leading efforts on risk reduction opportunities, whether through IBHS [the Insurance Institute for Building & Home Safety] or other standards.

I see more industry activity—concrete, real activity—than I've seen at any other time in the last four years. Kudos to those companies that are really starting to look at these challenges in new and different ways. I see more and more non-insurers looking at insurance as a viable part of the solution and wanting to create an environment where homes and communities are insurable.

There are discussions happening with builders that weren't happening a year or two ago. There are discussions happening with architects that weren't happening a year or two ago. This system-level awareness that's growing is really encouraging because this is not an insurance problem—it's a risk problem and an insurability problem.

Many sectors are accountable for reducing risk before a home presents itself to an insurance company to be insured and priced. The fact that meaningful discussions about what other players in the value chain could do to reduce the risk of these homes is wildly encouraging. Some of that's happening in the context of the California rebuild, while some is happening with organizations trying to coalesce stakeholders to pursue a national or larger-scale solution.

I'm encouraged because people are talking, more people are acting, and people are starting to see the connection points more clearly than perhaps they had before.

Paul Carroll

What other programs, similar to IBHS’s FORTIFIED, are making strides in promoting resilient construction?

Francis Bouchard

I'd point to the LA Delta Fund, dedicated to the 12,000 homes burned in the Eaton fires. It focuses on closing the gap between what insurance proceeds will pay for and what it takes to achieve a truly resilient construction level. We often debate who should bear this cost—consumers or insurers. This organization has found a way to attract both return-bearing capital and philanthropic capital to create a blended capital fund that pays the difference—the delta—between insurance proceeds and the cost of resilient construction. They are close to launching the fund and beginning to facilitate a much higher level of resilient reconstruction in LA following the fires.

This initiative is, in many ways, epic. It's never been done before, certainly not at this scale. The fact that they can raise money from markets indicates that the interest in ensuring resilient rebuilding extends well beyond the insurance sector.

Paul Carroll

Any other examples leap to mind?

Francis Bouchard

There's the Triple-I project with PwC in Dallas that is aligning stakeholders to facilitate the rebuilding or retrofitting of homes to the IBHS standards. This is another concrete example of insurers coalescing to change the risk profile of a community.

Then you have individual firms pushing the envelope. Milliman is doing an immense amount of work, with Nancy Watkins focusing on the WUI [wildland-urban interface], where the interaction between communities and wildfire is the most extreme.

Mercury Insurance is engaging with communities about what it takes to convince them to take steps that would make them insurable. We're starting to see a shift from thought leadership to community engagement.

Paul Carroll

What industry-academia research projects have generated the most interest, and where do they stand?

Francis Bouchard

Nothing has been launched yet, as we are still waiting on a funding announcement from the NSF [National Science Foundation] and corresponding funding from industry partners. We’re cautiously optimistic about the NSF and think industry funding will follow. 

The project that generated the most interest last September was a platform to facilitate dialogue between the atmospheric science community and the insurance underwriting community and help both sides better understand the value and use of available data sources. Considering the recent changes and, in some cases, wholesale dismantling of government departments or capabilities, this issue has become even more pressing and will likely appeal to numerous companies.

Dialogue is already occurring in multiple forums. We're hoping to coalesce these discussions and create a trusted pipeline of information flowing between federal data sources and the insurance sector.

Another well-received proposal focused on improving decision-making by narrowing uncertainties and addressing them differently. This proposal will likely garner attention from the insurance industry as companies seek to systematically understand and address uncertainties from weather, policy, and FEMA perspectives. The uncertainties simply accumulate.

The community-based catastrophe insurance project is another initiative we'll likely pursue. This topic is particularly ripe given the need for more innovative risk-bearing solutions.

Paul Carroll

What about developments at major insurance industry players?

Francis Bouchard

We [Marsh McLennan] recently announced our participation in a carbon trading mechanism to derisk the issuance of carbon credits. You're seeing more insurers and brokers focusing on this as a way to facilitate the projects that generate the credits.

There's also a more macro-level shift emerging—a growing awareness around shared accountability for the insurability of homes. The debate today typically centers on the technical nature of pricing risk. What we're trying to do is use this notion of insurability to reframe the conversation.

The right question isn't about pricing; it's about understanding the thousand decisions made that led to a home having its particular risk profile. We in the insurance industry are not the end-all, be-all. We are simply reflecting the thousand decisions made prior to receiving the submission.

Focusing on insurability allows us to enlist other critical players in the housing space to adopt this same, shared accountability approach. Non-insurance professionals often expect mind-numbing analytics and modeling. When you simply ask, "What can you do to reduce the risk that a house faces when it's finally built?", people respond with, "Oh, that's it? That's doable." And it should be doable.

When you aggregate this approach across every player in the value chain, you create transformative results. You get architects incorporating resilience, developers considering wildfire protection, fully certified contractors who understand requirements, and properly prepared supplies that don't cause delays.

When all these stakeholders understand their role in reducing risk, it makes our role significantly easier.

Paul Carroll

Thanks, Francis

About Francis Bouchard

Francis Bouchard is an accomplished global public affairs professional who has served as an advisor, catalyst and contributor to a series of climate resilience and insurance initiatives. He is currently the managing director for climate at Marsh McLennan, and earlier served as the group head of Public Affairs & Sustainability for Zurich Insurance Group, where he focused on aligning the group’s government affairs, sustainability and foundation activities. He originally joined the insurance sector in 1989 and since has held a series of industry-focused advocacy, communications, sales, citizenship and public affairs roles, both in the U.S. and in Switzerland. Francis also chairs the board of directors of SBP, a national non-profit focused on disaster resilience and recovery, serves on the board of the climate-focused insurtech incubator InnSure, and is a member of the advisory council of Syracuse University’s Dynamic Sustainability Lab.