This article explores how the emerging "Vibe" paradigm—rooted in intuition, emotion, and seamless interaction—is redefining human-machine collaboration. Extending beyond development, we propose Vibe Insurance: an AI-native model that reduces friction, builds trust, and transforms transactional processes into empathetic, user-centric experiences. 

In a world shaped by generative AI, Vibe Insurance reimagines not just what technology can do but how it should feel. Unlike conventional insurtech solutions focused on automation or efficiency, Vibe Insurance centers on emotional resonance, trust-building, and fluid interactions—bridging the gap between digital precision and human warmth.

Figure 1: Andrej Karpathy's post on X

The literal meaning of "vibe" refers to a sense of atmosphere or feeling. When combined with terms like "coding," it forms the phrase "Vibe Coding"—a concept introduced by Andrej Karpathy earlier this year (Figure 1). While the term is gaining attention, its translation and interpretation in different languages remain fluid. Instead of focusing on a literal translation, many highlight its defining features: It is intuition-driven, free-form, and centered on creativity. As a result, it is sometimes referred to more descriptively as "intuitive coding," "freeform coding," or "spontaneous coding."

Some interpret Vibe Coding as a new development paradigm: Developers focus on application functionality and architecture design, while AI coding agents assist in writing the actual code. This interpretation highlights a new "human-machine collaboration" model, involving clear module division, precise prompt design, and iterative testing and refinement.

But "Vibe" isn't limited to coding. For example, when filling out online forms, traditional static, linear, and generic tools—such as dropdown menus, radio buttons, and one-way workflows—often feel tedious and frustrating, sometimes even causing users to abandon the process. The alternative is "Vibe Survey." Other extensions include Vibe Design and Vibe Marketing.

The concept of "Vibe" represents atmosphere, freedom, intuition, flexibility, and creativity. It emphasizes breaking away from mechanical interactions, striving for more natural and fluid experiences, dynamic process adjustments, and even the ability to perceive emotions. Compared with traditional methods, Vibe is faster, more efficient, and capable of meeting personalized needs while delivering a pleasant user experience.

People don't fill out forms to meet the demands of a company or individual; they do so because they have a need—whether for a job, a service, or a connection. This process is essentially about "matching." Surveys match potential customers with products, job applications match candidates with ideal roles, and event registrations match consumers with their preferred activities.

In this sense, Vibe is a mindset, a "human-centric" methodology. Its goal isn't to pursue speed but reducing unnecessary friction in user interface (UI) design, or human-AI coding collaboration, thereby enhancing user experience (UX) and achieving seamless integration between the virtual and real worlds.

Darren Yeo, in his article "The Hype and Risks of Vibe Coding," writes about Vibe and design: "For now, I'll keep those vibes in check and continue to treasure what remains valuable to me. Because at the end of the day, design isn't just about speed—it's about humanity." Indeed, the focus of evolution is never speed but "humanity."

Large language models (LLMs) are making this vision (Vibe Everything) a reality. With the right models and prompts, we can present content in a Vibe format to users. Achieving this isn't about retrofitting static products with AI features but rethinking the experience users desire when performing simple tasks like filling out forms.

This represents a natural evolution of interaction between AI-native products—those built with AI capabilities from the ground up—and users in the age of generative AI (GenAI). It discards rigid rules in favor of algorithm- and model-driven interactions, enabling dynamic workflows, multi-role collaboration, multimodal formats, and multi-channel touchpoints.

For example, if a user says, "Your interface is great, but the price is too high," the LLM can identify: "Positive: UI design; Negative: Price sensitivity," and respond with a thank-you message from the design team and a discount coupon. Prompts must define clear objectives (e.g., role + task instructions), and contextual memory ensures interaction consistency. LLMs are ideal for realizing Vibe interactions, transforming mechanical processes into warm conversations—whether in forms or code, natural language becomes the new human-machine interface.

However, challenges remain. For instance, freeform outputs may deviate from expectations, contextual memory limitations can disrupt interactions, and emotional/affective cognition is still underdeveloped. Other issues include reasoning for complex problems, latency, multimodal processing, security, and privacy. Despite these limitations, technologists are gradually improving these models through human-AI collaboration and fine-tuning for vertical-scenarios.

Emotional/affective cognition is essential to natural interactions and user engagement. However, current technology has significant gaps, including insufficient multimodal fusion for emotion recognition, poor contextual emotional coherence, and weak generalization across different cultures and individuals. In the Vibe interaction paradigm, user demand for anthropomorphic interactions (questioners) and the solutions provided by tech teams (solvers) forms a dynamic cycle, reshaping the foundation of human-machine collaboration.

This dynamic cycle resembles a "spiral causality diagram of demand-driven innovation." When people ask, "Why can't this be simpler/smarter?" (questioners), it exposes technological shortcomings. Engineers then develop tools to address them (solvers). As people enjoy the benefits of these innovations, they naturally ask, "Can it be even better?" This creates a self-reinforcing cycle of technological advancement. From touchscreen phones to voice assistants to emotion-aware devices, each breakthrough redefines how humans interact with technology.

Figure 2: The demand-innovation spiral in mobile phone technology

As shown in Figure 2, the evolution of mobile phones vividly illustrates the dialogue between human needs and technological innovation. From Motorola's "just make calls" brick phones to Nokia's "texting and cameras" feature phones, to the iPhone's "smart and connected" touchscreen revolution, each generation meets current demands while quietly paving the way for the next breakthrough. Today, as people expect devices to "understand emotions and show warmth," affective computing is opening a new chapter. While phones can't yet interpret frowns or voice tremors, they can infer needs from usage patterns. The best innovations, from functionality to emotional resonance, always respond to humanity's deepest desires.

Given the universality of Vibe as a methodology, what is "Vibe Insurance"? Unlike conventional insurtech solutions focused on automation or efficiency, it centers on emotional resonance, trust-building, and fluid interactions—bridging the gap between digital precision and human warmth. We believe that establishing a new paradigm of human-machine interaction in insurance—Vibe Insurance—requires combining emotional intelligence with dynamic workflow design, reshaping user trust and service experiences through AI-native interactions.

• For users, it reduces mechanical friction, enabling "seamless" experiences.
• For businesses, it rebuilds service value chains through emotional intelligence and trust quantification.
• For technology, it balances data-driven precision with human-centric warmth, achieving "algorithms with empathy."

Vibe isn't just a technological innovation but a mindset revolution—"intuition-driven, experience-first." When Vibe becomes the foundation of design, human-machine interactions will no longer be constrained by rigid rules but will evolve into creative, warm, and algorithm-driven exchanges. From Vibe Coding to Vibe Insurance, the core principle remains: "Reduce mechanical friction, let interactions flow naturally." Whether engineers collaborate with AI on code, users fill out dynamic forms, or policyholders engage in emotionally intelligent insurance planning, Vibe transforms cold processes into warm conversations.

The future of Vibe Everything hinges on balance. We must navigate the boundary between AI's "simulated emotions" and "avoiding overreliance." The ultimate goal of technology isn't to replace humans but to bridge the virtual and real worlds in a more humane way, using natural language as the universal interface. Vibe will redefine how we coexist with the digital world.

References and Notes:

1. Andrej Karpathy: "There's a new kind of coding I call 'vibe coding,' where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g., Cursor Composer w/Sonnet) are getting scarily good."

2. Cassius Kiani (April 1, 2025), Freeform Update: Why Vibe Surveys Beat Static Forms, https://every.to/source-code/freeform-update-why-vibe-surveys-beat-static-forms.

3. Darren Yeo (March 9, 2025), The Hype and Risks of Vibe Coding, https://medium.com/user-experience-design-1/the-hype-and-risks-of-vibe-coding-0d1e1ccd71d7.

4. ESCP Business School (Feb. 17, 2025), Artificial Intelligence and Emotional Intelligence: The New Frontier of Human-AI Synergy, https://escp.eu/de/news/artificial-intelligence-and-emotional-intelligence.

5. David E. Nye's "demand-innovation spiral," from Technology Matters: Questions to Live With. The core idea: "New technologies never emerge in a vacuum but respond to the flaws of existing ones—yet every solution becomes the incubator for new demands, creating a self-reinforcing cycle."

6. For explorations of affective computing in insurance, refer to LingXi Technology's articles:

• Emotional Intelligence Breakthrough: How Emotional Prompts Define Next-Gen Insurance Planning https://mp.weixin.qq.com/s/VTZ5S6hOlcRfWY75iSj3OQ.
• The Dual Faces of AI: Role-Playing and Emotion Recognition https://mp.weixin.qq.com/s/4dmkTNjcyUF3pwxsrjgxAw.
• The Paradox of AI Companionship: The Delicate Balance Between Emotional Support and Dependence https://mp.weixin.qq.com/s/loX_Yr3ItXgD0tq81uC7Gw.
• Experiment 23: AI and Trust—The Future of Insurance https://mp.weixin.qq.com/s/cLpa0BSKSkWeb3zlrsjqrQ.

Claims professionals habitually spend hours sifting through hundreds of pages of medical records for every single claim. Now, thanks to generative AI that sorts and flags key information up front, claims professionals can skip the document grind and focus on what matters: making smart calls and avoiding expensive slip-ups.

However, this miraculous time-saving efficiency isn't without its challenges. Along with the ability to rapidly process and extract meaning from vast collections of complex documents, many organizations have stumbled using AI for document processing by setting unrealistic expectations, leading to widespread disillusionment when the technology fails to deliver.

Three specific challenges directly affect the success of AI document systems: workforce adoption issues, compliance risks, and cost concerns.

First, workforce adoption issues arise when employees, without proper expectation-setting, experience immediate frustration. This causes them to conclude, "This isn't working," at the first sign of error, often resulting in abandoned projects before the AI system can demonstrate its value. Second, in highly regulated processes, errors can trigger significant legal and financial consequences that create substantial risk. Third, organizations frequently underestimate the operational costs of running sophisticated AI models at scale.

These challenges are particularly evident in highly regulated insurance processes that involve complex and lengthy documentation with significant compliance requirements but can be avoided with understanding of the technology's limitations and wise usage and mindful oversight of the programmed skillset.

Take Medicare Set-Asides (MSAs) managed by Medicare secondary payer compliance companies. MSAs are complex financial arrangements primarily used in workers' compensation and liability claims to allocate funds for future medical treatment. Handling MSAs demands analysis of extensive medical records, billing statements, physician recommendations, and prescription histories.

Claims professionals invest 15 to 20 hours manually reviewing an average of 300 to 500 pages of medical documentation per claim. Complex cases can often exceed 1,000 pages. This creates a large opportunity to leverage AI to help with the understanding and processing of data. However, mistakes can come at a significant cost, potentially resulting in rejected MSA submissions, delayed settlements, additional reserve requirements, and even long-term Medicare recovery actions against insurers or claimants who failed to properly protect Medicare's interests.

These potentially costly consequences make a thoughtful AI implementation essential for MSA processing. Success with AI for document processing occurs when it is used as a tool that enhances workflows. This is where intelligent document processing (IDP) systems demonstrate their potential, as they can combine AI with document management technologies to transform how complex, unstructured documents are handled.

By presenting AI as an enhancement to the claims professional's workflow rather than a replacement, a company is able to address both workforce adoption concerns and error risks simultaneously. The key is creating a system where claims professionals maintain decision-making authority while the AI handles the time-consuming organizational tasks. This integration is what makes document processing improvements possible.

Breaking down a typical MSA review process, roughly 30% to 40% of that time is spent on manual document organization and navigation. This includes sorting pages, identifying document types, and locating relevant information across hundreds of pages. The IDP system tackles these challenges by handling the initial heavy lifting. It digitizes and organizes documents, identifying important details automatically. Claims professionals can then work with this pre-organized data, significantly reducing the time spent on manual document sorting and navigation. The result is a structured foundation that allows claims professionals to navigate efficiently through what was once an overwhelming volume of information.

The most effective implementations of these systems incorporate human verification. Claims professionals begin with the AI-organized information, make refinements and corrections where needed, and then use this enhanced foundation to perform their specialized analysis. This verification step ensures accuracy while still capturing significant time savings. Once the claims professional confirms or corrects the AI's initial processing, the system can then perform more sophisticated tasks with the validated information.

For example, the AI system can identify and extract date references across hundreds of pages of documents, creating an initial chronological sequence. Rather than manually finding each date throughout hundreds of pages, claims professionals review the pre-assembled timeline to verify its accuracy and completeness. They can spot missing events, incorrect dates, or sequence errors by reviewing the overall pattern of care rather than hunting for individual date references page by page. Once the claims professional validates this timeline, correcting any errors they find, the system uses this confirmed data to generate a comprehensive chronological view of medical events.

This could also work with keyword flagging. The AI system can be programmed to identify critical terms such as "surgery" throughout the documentation, whether this is in images or PDFs. This is especially valuable because surgical procedures often represent significant costs that must be accounted for in MSA calculations. When the AI highlights these terms, claims professionals can navigate to relevant sections instead of manually sifting through them with the risk of overlooking something. When poor document quality causes the system to inadvertently miss important keywords, claims professionals can flag them, helping the system learn and improve.

This brings us to the challenge of managing operational costs. Sophisticated IDP systems address this by intelligently determining the appropriate level of AI processing needed for each document. Rather than routing everything through the most expensive large language models, these systems analyze document complexity, classification certainty, and business value. This analysis allows them to allocate computational resources optimally. Routine documents can be processed using lightweight models, while only complex or high-value documents require advanced generative AI capabilities.

This intelligent resource allocation creates significant cost savings without sacrificing performance. As claims professionals verify results and provide corrections for document misclassifications, missed medical events, procedure code errors, and ambiguous treatment dates, the system gradually improves its ability to assess document complexity and determine appropriate processing levels. Rather than creating additional verification work, the system focuses human attention only on elements with low confidence scores or high business impact.

By using this feedback to come up with better instructions, the system is able to learn from claims professional corrections to recognize similar documents in the future, becoming more efficient with each processed claim. This creates a positive cycle where accuracy increases while resource requirements decrease over time, addressing the operational cost challenge head-on.

This approach to implementing IDP systems provides solutions to the challenges related to workforce adoption issues, compliance risks, and cost concerns. It prevents employee frustration by positioning the claims professional as the decision-maker while the AI serves as a sophisticated but at times imperfect assistant. It maintains crucial quality controls to reduce legal and financial risks by keeping the responsibility directly with the claims professional. Through learning the type of intelligence that is needed, it also manages operational costs effectively over time.

This MSA case demonstrates how AI can enhance human judgment in document-intensive processes. Even when claims professionals must still review key documents, the value comes from making that review more structured and focused. By creating a feedback loop that continuously improves performance and managing computational resources intelligently, organizations can transform initial AI disappointment into sustainable success. This balanced approach delivers better outcomes for all stakeholders while avoiding the pitfalls that derail many AI initiatives.

"Best practices" are on the docket at every leadership offsite, conference panel and board-level meeting. But as currently acted on, best practices don't amount to much more than "doing a bit better."

A vague and watered-down expression can only result in equally vague and watered-down improvement measures: maybe a new dashboard, a revised call script, new key performance indicators (KPIs), a new customer relationship management (CRM) system. Sure, a dollar might be saved here or there. But what's needed is bold, lasting, transformative change.

To truly achieve "best practices," meaning evidence-based, scalable, and continuously refined over time, insurers would need to undergo a complete operational overhaul. The problem is, that kind of overhaul is an unappealing prospect—ripping out one system and trying to replace it with another can mean a slowdown in productivity and a focus on change management, rather than the work of actually doing insurance. Moreover, it can be controversial, disruptive, and highly visible—three things insurers tend to avoid, especially when margins are thin across many lines of business. Major change risks rattling investor confidence, unsettling personnel, and triggering concerns from customers and board members alike.

But how much of this resistance to fundamentally reexamine insurance operations is truly about protecting against disruption—and how much is a reluctance to think in new ways? The industry has long been defined by its conservatism, and that mindset continues to shape its decision-making. When younger professionals think of insurance, they picture fluorescent lights, thin cubicles, outdated software—nothing that relates, say, to an intuitive digital app. There's safety in legacy processes. "That's how we've always done it," the thinking goes. "It's worked so far. Our people are used to it. Why do things differently?"

But this needs to change. Introducing AI to areas of insurance that aren't yet using it can provide the necessary overhaul, one that fundamentally reimagines how agents do their work and how insurers collect, analyze, and apply data that will help them. 

Fortunately, this transformation no longer needs to be abrupt or alienating. Today's technology allows insurers to roll out change in a piecemeal, custom-tailored way—minimizing disruption while maximizing long-term impact.

The result won't just be checking a "best practices" box on paper. It will mean real operational improvements: higher margins, greater employee satisfaction, an easier time attracting younger talent amid a talent crisis, and higher customer satisfaction—all of which can help reframe the reputation of an industry long seen as inhuman and overly bureaucratic, especially in light of recent events.

Forget the Firm Handshake—Focus on the Data

First, data collection. Over the past decade, insurers have leaned on broad metrics like total premiums written, retention rates, and revenue per agent. But these numbers are too general to offer meaningful insight. They tell us what happened—but not why. Sure, we know this agent wrote this many policies in the past year. But are we any closer to understanding what actually drove that performance?

AI is often praised for its ability to zoom out—processing and connecting thousands of data points far beyond what the human mind can track. But what's underrated is its ability to zoom in. With the right inputs, AI can deconstruct the behavior of top producers, revealing the subtle habits that set them apart from their peers. It's not about collecting the most data—that just leads to a glut. It's about collecting the right data.

Traditional thinking still dominates how many executives explain producer success. They'll chalk it up to someone's alma mater, a trusty handshake, a family legacy in insurance—or fall back on vague clichés like "work ethic" or "wanting it more." The problem is, these explanations frame success as innate and unteachable. If top producers are simply born with it, then there's no hope—or strategy—for helping average producers improve.

Top producers' inner workings can be uncovered with the help of AI. It could be the speed and timing of their follow-ups. Or the exact phrasing they use to tailor pitch strategies to different clients. Or even their ability to strike the perfect balance between persistence and discretion. How a producer structures their day for maximum efficiency can also create ripple effects that lead to higher conversion rates.

Insurers need to strip away the mystique of high performance—not just to help average producers improve, but to show that success isn't luck or legacy; it's a learnable system. When producers can see the path, they're more likely to walk it—and more likely to stay.

Define "Best Practices," Not "Somewhat Better Practices"

Yet even if AI can generate accurate observations and build a data-driven template for the ideal agent, those insights won't translate into better performance if producers are still asked to use CRM systems they're reluctant to go into. This is the biggest bottleneck to achieving best practices: The systems meant to support agents are often the very ones holding them back.

The select few from the younger generation who are genuinely excited about working in insurance often become jaded quickly—usually thanks to the daily frustrations of using clunky CRM systems. All it takes is one lunch, one venting session with a friend in finance or tech, to realize how far behind their tools really are—and to start thinking about jumping ship.

These systems often demand additional work: manual data entry using clumsy interfaces with little to no integration with calendars or phones. Worse, the systems lack AI-driven insights—so agents are forced to treat every lead the same, regardless of how cold or warm it is. It's no wonder turnover rates among agents remain so high.

Incorporating AI into these systems isn't just a promising retention strategy for policyholders—it's a powerful one for agents, too. Success breeds success, so when they can instantly see what practices work, what paths to take to close a sale, they'll want to do more. It's human nature. In this case, advanced technology doesn't strip the job of its humanity—it restores it. It gives agents space to focus on what drew them to the field in the first place: building lasting, mutually beneficial relationships with clients.

But ease and humanity aren't the only reasons agents stay motivated and loyal to their agency. There's also a financial incentive. Modern AI-powered systems identify cross-selling and upselling opportunities that might otherwise go unnoticed, letting agents maximize their commissions.

Provide More Data Points to Underwriting

Just as performance analysis has historically relied on too few data points, underwriting has long been constrained by limited inputs—typically just credit scores and claim histories. But consumer behavior is evolving too quickly, and often unpredictably, for insurers to keep relying on such narrow datasets.

AI allows a far more diverse range of data points to be taken into account. It can factor in social media activity, purchasing behavior, and real-time insights from Internet of Things (IoT) devices. For example, telematics in vehicles enables insurers to monitor driving habits continuously, allowing for dynamic premium adjustments based on real-world behavior rather than outdated, static models.

For too long, insurers have been playing catch-up. Some lag times have shortened, but we should be aiming to eliminate the lag entirely. Any delay just measures how much margin is leaking. For the first time in history, we're within reach of truly real-time risk pricing.

Underwriters shouldn't fear an AI-driven overhaul of their sector. Just like producers, they didn't enter this industry to be buried in repetitive administrative work—only to be blamed for every oversight. With AI handling what it's best at, like fact-checking and surface-level analysis, underwriters can return to what they're best at: making high-level statistical judgments and strategic decisions.

Expect Regulatory Pressure From the States, Not the Federal Government

Even with the prospect of federal deregulation under the current administration, insurers shouldn't assume a more relaxed compliance environment. Several states, especially California, are already enacting stricter environmental regulations in response to escalating wildfire risk—putting pressure on insurers to offer broader coverage in high-risk zones. At the same time, backlash over prior authorization delays in health insurance is gaining political traction, with new legislation on the horizon. Insurers that move too slowly could face not just financial penalties but long-term reputational fallout.

AI can help insurers stay ahead of the U.S. regulatory maze by monitoring policy changes in real time, flagging discrepancies across states, and identifying inconsistencies in claims, contracts, and internal processes. In a market where compliance expectations differ across all 50 states, these capabilities are becoming indispensable—especially for regional carriers aiming to scale nationally without stumbling into regulatory blind spots.

Complying with anti-discrimination laws is another area where AI can make a real impact. But its value goes beyond just staying compliant—it creates fairer, more consistent decision-making that can help shift public perception. The insurance industry has faced long-standing scrutiny for biased practices, and AI—if used responsibly—can be a tool to change that narrative.

Know the Risks—But Don't Overstate Them

While AI holds real promise for improving sales, underwriting, and compliance, insurers shouldn't jump in without a clear plan. Regulators are becoming more cautious—and in some cases, more aggressive—about how AI is used. Without thoughtful implementation, AI may see the expected efficiencies undone by compliance issues.

Insurance is, by nature, a risk-averse industry—understandably so. The job, after all, is to anticipate consequences before they happen. But when it comes to AI, many insurers are overstating the risks in ways that aren't rational. The greater danger isn't adopting AI too soon—it's falling behind as AI becomes the standard across every other industry.

The start of 2025 brought two devastating wildfires to Southern California: the Palisades fire and the Eaton fire. These events, fueled by severe Santa Ana winds and abundant post–atmospheric river vegetation, left behind widespread destruction, including thousands of damaged and destroyed structures. They also reinforced a larger trend of increasingly volatile wildfire behavior in the region—an outcome of shifting climatic conditions, altered precipitation patterns, and extended fire seasons.

The lessons from these latest fires underscore the evolving nature of wildfire and the need for it to be treated as a specialist peril rather than a generalist one. Most people get their wildfire coverage through their homeowners insurance, and most of the perils that are covered under a homeowners policy are seen as generalist, so you can use fairly traditional actuarial methodologies to figure prices. Wildfire used to fit that description. There was not much change. You may have had some bad years from time to time, but it wasn't bad enough to merit a specialist kind of approach by the entire industry, like we see for cyber risks.

This changed in 2017 when California's wine regions had surprising, devastating wildfires and then the Camp fire and Carr fire happened a year later. It became clear that the traditional approach of the industry was no longer very effective. Wildfire needs to be treated as a specialty kind of peril that requires much more targeted resources to underwrite and mitigate properly.

Why is the wildfire risk evolving in California?

Wildfires have long been a natural part of Southern California's landscape. However, their frequency, severity, and behavior have shifted dramatically in recent years due to human activity and climate change, necessitating a reassessment of risk and mitigation strategies.

The El Niño-Southern Oscillation (ENSO), a key climate driver, has become increasingly frequent and severe due to climate change. This has amplified atmospheric river events like the Pineapple Express, which bring heavy rainfall but exacerbate wildfire risk by fostering rapid vegetation growth followed by prolonged dry periods. For example, the Palisades and Eaton fires followed a strong El Niño event in late 2024 that shifted abruptly into a La Niña phase, creating abundant vegetation during the rainy period and extreme dryness in the months leading up to the fires.

Historically, Santa Ana winds were more likely to occur after the precipitation season had begun, mitigating their fire-spreading potential. However, as climate change has pushed the beginning of the precipitation season later in the year, these winds increasingly are occurring during drought conditions, and the resulting risk of large, destructive wildfires has grown significantly. Though wildfires have long been part of the region's ecological cycle, factors such as the ENSO, lengthening drought conditions, and extreme wind events have significantly altered fire behavior in recent years. As these elements converge, traditional models built on historical fire patterns are increasingly challenged, leaving both communities and insurers grappling with unpredictable risks.

Adding to this challenge is the expansion of the fire season seen across decades. Data on maximum fire sizes by month reveals a troubling trend. From 1985 to 1999, fires peaked in July and diminished after August. Between 2000 and 2009, fire sizes began to show secondary peaks later in the year. Most recently, from 2010 onward, a pronounced secondary peak has emerged in October and December, signaling an extended fire season. This shift, combined with the proliferation of invasive plant species, declining forest health, and worsening climate conditions, has exposed previously low-risk areas to significant wildfire hazards. These evolving dynamics present challenges for models relying solely on historical fire patterns, further highlighting the need for advanced predictive approaches.

Underwriting models need to keep up

The speed with which wildfire has evolved is making it even harder for traditional models to adapt as close to real time as possible. Despite the complex and evolving nature of the wildfire risk, it is possible to develop effective wildfire risk assessment models. Naturally, models must be more sophisticated and rely on advanced technology to make sense of the myriad of data needed to create the assessment.

As an example, the Delos model first integrates high-resolution data on fuel, wind, climate, and fire behavior alongside hundreds of additional layers of supporting data, providing comprehensive insight into wildfire risks. Second, it employs advanced machine learning methodologies looking at wildfire behavior independent from historical events to ensure that there are no surprises from tail-end risk events like the Palisades and Eaton fires. Finally, the model undergoes rigorous back-testing against historical fires and is reviewed by wildfire experts to ensure both accuracy and reliability. This approach has successfully predicted the full extent of all the major fires in the past five years, including the recent LA fires.

Conclusion

The Palisades and Eaton Fires serve as a stark reminder of the evolving wildfire risks in Southern California and the need for innovative solutions in wildfire risk mitigation. As climate change and environmental shifts continue to affect fire behavior, traditional models struggle to keep pace with emerging risks.

I have high hopes for progress in better analytical understanding of how to harden homes and broader communities. This should mean some areas that are considered unaffordable to insure now will, in future years, where homes have performed enough hardening against wildfire, be able to obtain affordable coverage. There are a lot of efforts taking place in the aftermath of the Los Angeles fires to figure out how to make these communities safer. Additionally, the California Department of Insurance has put a lot of effort into having insurers respond to these kinds of things.

Together, we can build a more resilient future in the face of evolving wildfire threats.

Delos has published a whitepaper providing more detail on the LA fires, which can be viewed here. 

Volatility can be problematic for insurers for two reasons. First, investment income makes up a very large proportion - typically at least two-thirds - of an insurer's profitability. Market volatility such as we are seeing in the first half of 2025 makes it harder to assess optimal investment strategies to pursue that income; will interest rates continue their recent downward trend or will they reverse, given sticky inflation?

Second, the investment decisions insurers make today can affect results for years to come due to the nature of their products and accounting rules. For example, under U.S. statutory accounting, most life insurers' portfolios are still earning income based on yields from bonds issued prior to 2022, i.e., before interest rates rose from a more than decade-long period of historic lows.

From trade to taxation to the role of government, insurers are not immune from dramatic policy swings. Given that, it's no surprise that insurers rated "Domestic Political Environment" as their top risk in Conning's latest investment risk survey. But it is not correct to assume that all the changes the industry faces are due to a change in presidential administrations. In fact, many of the uncertainties (e.g., the pending changes from the NAIC's Generator of Economic Scenarios) have been in the works for years.

Political and market volatility are not the only major uncertainties for insurers: there's also a large amount of regulatory change in the offing. For example, the NAIC is looking to adjust capital charges for a wide range of assets to ensure that assets with comparable risk have comparable charges. While we await the final adjustments, we know from the NAIC's recent increase in charges for securitization residual tranches - to 45% from 30% - that the impact may be quite large. If that isn't enough, life insurers are also preparing for the pending change in reserve and capital calculations for many of their products, a result of the transition from the Academy Interest Rate Generator (AIRG) to the new NAIC GOES scenarios.

So, what can we make of all this? Clearly, it's important to recognize the potential risks that insurers face in today's environment. During the 2008 financial crisis, we saw how uncertainty can lead to a rapid derisking of insurers' portfolios, a process that can have a long-lasting impact on everything from product design to profitability.

But we also need to remember that insurers are in the risk business. Whether it's asset risk or catastrophe risk, the successful insurers are the ones that find the right balance between seeking profitability and taking on variability. More importantly, many of those companies have been maintaining this balance for decades during all types of market storms: the 2008 Financial Crisis, 1970s Stagflation, world wars, the Great Depression, and more.

Given all that, you might expect insurers were planning to dramatically scale back portfolio risk. Yet the Conning survey showed the exact opposite: Most insurers were expecting to continue increasing their portfolio risk. For example, more than 40% of respondents expected to increase their allocations to both public and private equity. While those values are down from the 2024 survey, they are still well above the portion of respondents expecting to reduce their allocations to those assets. In fact, the overwhelming majority of respondents - nearly 80% - actually had an optimistic view of 2025.

One aid to that resiliency is a set of customized tools allowing insurers to analyze a wide range of potential futures. With a properly calibrated model, insurers can better understand the potential upside and risk associated with an asset allocation strategy. They can also use these tools to help fine-tune their expected risk/reward balance across a range of strategic questions, such as whether to seek reinsurance to offload risk or how to refine product design to help limit risk exposure. These tools may also give them a leg up in developing concrete action plans for handling the next major unexpected event.

There is no question that today's risks may appear new and daunting. And we know that past performance does not guarantee future results. However, we can take comfort in the knowledge that the insurance industry has handled many significant and unprecedented challenges over the years and has survived and thrived. We are confident the industry can and will handle whatever comes next.

References

National Association of Insurance Commissioners, Capital Adequacy (E) Task Force RBC Proposal Form, April 20, 2023. 

Conning, Inc., "Investment Risk Survey: Insurer Optimism Cools on Markets, Adding Risk; Private Assets Still an Interest but Inflation No Longer a Leading Concern," Matt Reilly, Feb. 11, 2025.

Organizations require a robust risk management framework for sustained growth. That means developing a structure that considers all risk aspects, from macroeconomic factors to credit and operational issues. Today's risk management framework also leverages the latest technological advances to create procedures and guidelines for managing those risks. Strategies to achieve the goal of balanced risk management include investing in technologies that can identify and assess both immediate and future risk factors, establishing thresholds based on the organization's appetite for risk, identifying and monitoring risks that could breach this framework, incorporating those risks into strategy development, and executing those strategies effectively.

Use of artificial intelligence in risk management

Artificial intelligence (AI) and complex machine learning models allow for a more dynamic financial prediction framework, integrating real-time and cross-domain data. AI may not have been created with risk management in mind, but the technology is tailor-made for predicting and mitigating financial threats, aiding in improved decision-making, and providing protection and safeguards for various asset classes. Over a 10-year period ranging from 2022 to 2032, the size of the global AI risk management market is predicted to more than quadruple from $1.7 billion (2022) to $7.4 billion by 2032—a compound annual growth rate (CAGR) of more than 16%. Increased trust in technology is the most significant driving force, with previous ethical misgivings easing and a general improvement in quality and trustworthiness in emerging models.

Predictive analytics enable companies to foresee, prepare, and ultimately lessen the potential impact of previously unexpected scenarios, with the financial crisis of 2007 and 2008 as the most recent and relevant example. During the crisis, a clear correlation emerged between a skyrocketing unemployment rate and an increase in defaulted mortgage payments. Now organizations can build and update models that analyze key metrics during economic unrest or uncertainty, letting those businesses implement mitigative or protective measures. More routine, everyday examples include strategic decision-making in offering loan terms, as underwriters can use the model to better understand the loan's expected performance, net present value (NPV), probability of default, and other critical measures. Traditional models continue as the industry's standard, but increasingly dynamic modeling facilitates real-time updating.

Meanwhile, an industry as data-driven as insurance quickly adapted to the AI era, with the new technology contributing to everything from crafting individualized policies to automating underwriting procedures. Even claim processing, traditionally identified as the top source of customer frustration with the industry, has enjoyed advancements in the forms of:

• Claim prioritization. Programs can search for key terms to help adjusters deal with claims in order of their urgency.
• Addressing incomplete or disorganized claims. AI can identify missing information, documentation, or identification from claims and request necessary details from clients via automated emails or chatbots.
• Fraud detection. By identifying patterns of behavior and scanning enormous volumes of data in real time, AI detects and uncovers trends that can indicate an increased possibility of fraudulent activity.

Real-world examples of dynamic risk management

The term "risk management" is typically assumed to pertain to the financial realm, and with good reason. There are predictable and unforeseen risks in every industry, and an AI-related application exists to address just about all of them. For example, a traditional risk management strategy in the industrial world involves prioritizing extensive preventive safety measures to minimize accidents and liabilities. But managers with access to a more dynamic approach can blend preventive and reactive strategies, allocating resources based on actual risk exposure rather than worst-case scenarios. These companies rely on AI-driven predictive maintenance rather than overinvesting in preventive measures. By using sensors to detect wear and tear in machinery, they can intervene only when necessary, reducing unnecessary spending while still managing safety risks effectively.

In the traditional risk management-related financial field, consumer lending organizations are historically hesitant to offer favorable terms or even eligibility to customers with no or limited credit history. These customers ultimately struggle to secure loans in a risk-averse market. Organizations can use a combination of inter-domain data and predictive modeling to analyze the true risk presented by offering loans to these customers. Examples include:

• Bill payments. A history of on-time payments for utilities, rent, and other monthly expenses indicates an individual who is likely to be a good credit risk for the organization.
• Secondary loans. While they aren't included in traditional FICO scores, any record of repaying an advance loan on a paycheck can also reflect positively on an individual's credit.
• Income/spending habits. With access to a person's bank account data, machine learning can quickly identify income patterns and compare them with outgoing expenditures, determining account balances and other relevant information. Pay stubs and W2s can also be immediately scanned and evaluated.
• Social media. Behavioral patterns or online browsing history can be useful in gaining an overall sense of customer behavior patterns and doubles as a useful tool in predicting a customer's creditworthiness.

Lastly, while a traditional risk manager in the insurance industry might purchase comprehensive coverage to protect against potential physical or employee-related risks, those who take a more agile risk approach use AI and real-time data to continuously assess risks and adjust coverage accordingly. In the commercial transportation sector, some companies are leveraging telematics and driver behavior analytics to customize insurance coverage. Instead of a fixed insurance policy, safer drivers receive lower premiums, and riskier ones face dynamic adjustments, optimizing costs while managing exposure effectively.

Of course, the greater role of the insurance industry is the implementation of risk management for other industries–some of which are increasingly related to AI-specific uncertainties. These include handling data, formulas, algorithms, and other machine learning features that, if improperly managed, can result in financial and reputational harm. Through smartphone apps, wearable devices, and GPS monitoring, insurance companies can base premiums on real-time customer behavior rather than a preconceived idea of how much risk that customer's demographic profile presents.

The establishment of a proper risk management framework includes considering business income, credit, operational risk, and uncontrollable factors related to the greater global economic scene, world events, and industry-specific details. By identifying the contributing factors and investing in the latest technological advancements, today's risk managers can position themselves ideally in an increasingly uncertain marketplace.

It's frustrating when a transformative new technology is held back because the infrastructure can't yet support it.

Think of electric vehicles (EVs), which are caught in a Catch-22 of sorts. Many people are reluctant to buy them until there are more public chargers available, and charging networks are not being built until there are more EVs to use them.

Genetic testing faces a similar problem. It has the potential to transform healthcare through precision diagnostics and therapies, but it's being held back by health plans' insufficient programs for managing it. Caught by surprise by the rapid growth in the number and applicability of tests, plans have been struggling to handle them through their routine testing programs.

It's not working. More than 180,000 genetic tests are on the market, with an average of 10 added daily. CPT coding has not kept up. There are about 500 CPT codes for roughly 360 times as many tests. This results in a system that is slow, inefficient, expensive, and susceptible to waste, fraud, and abuse. Health plans need management programs built specifically for genetic testing, which will only grow in volume and complexity.

As health plans work to improve their handling of genetic testing, whether internally or with a lab benefits management firm, they should ensure that the following nine elements are incorporated into their genetic testing benefits framework.

1. Accreditation and regulatory compliance: Utilization management is necessary to ensure patients receive the proper care and required services without overusing resources. Accreditation by respected agencies like the National Committee for Quality Assurance and URAC and good standing with state regulatory agencies help ensure that organizations making these decisions follow evidence-based best practices.

2. Coverage criteria based on science: The volume of genetic tests is exploding, and maintaining a current understanding of the clinical science and the appropriate coverage criteria documented in clinical policies requires frequent review. To ensure the latest science and clinical medicine are codified in medical policies, experienced working laboratorians, pathologists, and geneticists should perform a comprehensive scientific and clinical review of the newest literature annually or as the science warrants. If health plans lack the internal resources to do so, they should partner with a business that specializes in it.

3. Optimized laboratory network and quality testing: Not all labs are created equal. Some perform better than others. Quality evaluations, results, and audits can identify these high-performing labs. Once the trusted labs have been designated, plans can promote these labs to patients, providers, and even tiered networks with increased benefits to those who use higher-tier labs. In return, the labs that benefit from promotion can offer unit price reductions.

Genetic testing must meet appropriate scientific and clinical standards beyond just coverage criteria. Guaranteeing that labs have completed sufficient scientific, technical, and clinical validations is essential to ensure the information provided to the clinician informs patients' healthcare needs. Plans should have systems to evaluate labs beyond Clinical Laboratory Improvement Amendments requirements and the quality of specific mutation analysis tests.

4. Prevention of fraud, waste, and abuse: The looser the operating framework for testing, the more likely fraud, waste, and abuse will occur. Integrating test specificity and enhanced claim-to-authorization matching processes will reduce that and save plans money.

5. Claim-to-authorization match during adjudication: In many cases, the criterion for matching allows broad, non-specific matches, which contributes to inappropriate payments, stopped claims for manual review, delays in claims payment, and the potential for fraud. Increasing the flexibility and specificity of matching criteria alleviates those challenges.

6. Continuing utilization management vs. claims adjudication: Plans should continually evaluate laboratory tests, required coverage criteria, and historical laboratory performance to determine when a specific laboratory or a collection of tests should be adjudicated during the claims process without prior authorization (PA) or continue utilization reviews in a PA process. Additional controls, such as regular auditing of laboratories to ensure compliance, are recommended.

7. Enhanced provider education and experience: In many cases, laboratories perform the same or similar genetic tests while billing with different combinations of CPT codes. While coding tools like MolDX and Concert Genetics help, they must be embedded in comprehensive programs to be effective. Establishing coding requirements for each test at each laboratory allows streamlined operations and more comparative analytics within the plan. The test specificity concepts discussed provide a clean, robust, and efficient means to overcome potential code challenges and clarify provider billing requirements. Health plans adopting a specificity method for test identification will see increased efficiency, improved laboratory and physician satisfaction, and reduced potential fraudulent billing.

8. Expedited review of prior authorizations: PA can be frustrating and time-consuming for all parties. It's why the federal government and states are creating requirements limiting PA requirements. A "gold card" program that eliminates PA for top-performing labs can simplify administration, improve patient outcomes, and increase savings for health plans, labs, and patients. A lab benefits manager identifies and supervises the network of top labs, reducing the burden on payers.

9. Managing demand from biomarker legislation: As more states pass biomarker legislation, plans need a lab benefits management program to ensure patients receive the right tests. Alignment with nationally recognized guidelines and evidence-backed clinical utility is necessary to ensure that these mandates don't inadvertently hinder innovation or inflate healthcare costs.

Genetic testing will become an even more critical—and beneficial—part of healthcare. Plans that establish separate, science-based policies for managing it will realize the maximum benefits for patients, providers, and themselves.

Banks have an inherent advantage over insurers when it comes to learning how to use AI most effectively, Aditi Subbarao of Instabase says in this interview with Ron Rock of JobsOhio. Banks simply have more data because their interactions with customers are deeper and more frequent, and banks’ data is organized more accessibly. 

So what should insurers learn from banks?

Subbarao says insurers should learn to be braver, especially in using AI for risk management, fraud detection and improving the customer experience. Saying you need to clean your data first is an excuse, she says.

She also says insurers should think about setting up a federation of AI agents, which can pull data from the many silos where insurers store it and provide a clear understanding of a client’s situation. 

That would go a long way to eliminating the data advantage that banks now enjoy.

Ron Rock 

What inspired you to get into AI and be in the financial services industry with AI.

Aditi Subbarao 

I must say, I'm really, really fortunate, because the way I see it, AI is very much kind of the center of activity and innovation in the industry at the moment. Having spent more than 12 years in financial services myself, Ron, I have first-hand experienced the problems and the difficulties that most people working there face in trying to find the information that you need to have to better serve your clients and to do your job better. If there is one thing that any banker or even insurance professional would think of it is, "I wish I knew how to do something. I wish I knew what would happen. I wish I knew." 

And I think AI is the one thing that has the potential to change so many things in the financial services and insurance space, just given the amount of data that they need to deal with. And that was very much the driver behind getting into the field of AI and then helping apply that into a space which I know and love.

Ron Rock 

Where in the financial services space do you see the biggest potential for AI?

Aditi Subbarao 

I actually think the biggest potential exists across the financial services space. AI can genuinely completely transform and revolutionize how practically every single function, every single role, is done in that space. 

However, if I had to pick a few areas, I would say this typically lies in risk management, fraud detection and customer experience. 

So being able to access, process, analyze and then act upon the huge realms of data from across the market, across trades, across customers or across like so many different sources, and then using that to understand your risk better; and then take proactive actions to manage that risk is going to completely change how risk management updates. 

On the fraud side, AI can now analyze and even predict a lot of different occurrences that human beings would have found impossible to, especially at the speed at which they need to be done; like how we now have instant payments. How can you keep monitoring the regulations that you need to on that? 

And the last piece is customer experience. We already see banks and organizations that are genuinely customizing their products and services, their overall experience that the customers are getting. 

I think these are the three areas that would be top of the line for me in terms of impact in F.S. [financial services].

Ron Rock 

So obviously banking is usually at the forefront. When you compare financial services, banking is at the forefront, insurance a little bit lacking. So why do you feel that banking is taking hold of AI quicker? 

Aditi Subbarao 

That's a very interesting question, you know, and something I've thought about for a long time. And now that I have the opportunity of working across both industries, I would again say there would be sort of three reasons. 

The first one is the kind of data that banking has. It's just so much more and so much more frequent. For example, one billion payments are made by the banking industry every single day, and we're not even talking about the deposits or the loans or the investments or trades. It's just payments. On the flip side, claims, which is the most common transaction in the insurance industry, it's orders of magnitude lower. So there's just more data.

A lot of this data tends to be structured, especially because a lot of financial services transactions are either exchange traded or cleared by clearing houses and so on. So banking has had the advantage of much more data, much more frequently, in a more manageable format. 

You can use this data to train AI models and then also apply AI on top of it, so that's one major advantage. 

The other thing, which is slightly nonobvious, is almost the business model or the organizational structure. And what I mean by that is, in banking, the asset and liability sides of the business are very closely linked together. Whether you're taking deposits and then making loans, or whether you're making investments and then managing the risk, it's all together. It's very closely coupled. Whereas with insurance, somebody who's working on underwriting risk or processing claims is so far removed from the investment side of the business that it doesn't really work very seamlessly, and therefore finding the right applications and using AI so that it can create impact also gets very siloed.

And the last piece, which is in fact something that I find most fascinating, is just the variety of functions that a bank provides and how closely they are embedded in their customers’ lives. I'll ask you a question: On your phone, how often do you open your banking app? 

Ron Rock 

Daily. Probably twice, three times a day. 

Aditi Subbarao 

Do you have an app from your insurance company on your phone? 

Ron Rock 

I do.

Aditi Subbarao 

How often do you open it? 

Ron Rock 

Once every couple of months.

Aditi Subbarao

I think banks are just so much more closely embedded in the day-to-day as compared to insurance companies, so naturally you have far more opportunities and far more applications for AI. I think that's where the advantage has been, but it has been so encouraging to see, especially at ITC, that I think that gap is going to start closing very quickly.

Ron Rock 

What can insurance companies learn from the banking industry and how they've adopted AI?

Aditi Subbarao 

I'd say this falls into two pieces. The first is slightly the more cultural aspect of it. I would almost say insurance needs to be a bit braver, take a bit more risk. And this is so counterintuitive, because the very DNA of insurance is risk aversive, avoiding risk, protecting from risk. But especially with AI and generative AI and the new advances that are happening there, you need to be brave. You need to go and do stuff which hasn't been done before. You need to experiment. You need to try things out. So what if they fail? Try it and move on. And I think insurance needs to kind of make that mindset shift a little bit, like banking has done and started to do. That would be my first piece of advice, just go and try it and experiment more. 

The second piece of advice is kind of what we referred to before, which is, I think insurance has always been more about "here is the protection we can give you; take it on our terms," whereas banking has now oriented a lot more to "what does my customer need, at what point, and how do I structure it that way?" I think the more that insurance starts becoming customer-centric, the more that it starts creating new products and services based on what protection people actually need, the more they will find the natural drive to start adopting AI, because you cannot do it without AI. So those would be my two pieces of recommendation, or like areas where insurance can learn from banking. 

Ron Rock 

For emerging technologies in this space, there's a lot going on. I mean, you see it across the expo. What are some of the emerging technologies that you think are going to impact the industry?

Aditi Subbarao

I will narrow down your question, if you don't mind, and again, focus on Gen AI, because that's what I do. Even within that space, I'd say, searching across all the data, irrespective of where it lives: Being able to now do that is something we're already seeing live in action. It doesn't matter whether you have really complex variable pieces of data, like a PDF sitting in somebody's shared drive who left two years ago, or like an Excel sheet sitting on some cloud database, you can now literally ask a natural language question to query across all of those sources of data. I think enterprise search is a very powerful technology, which is now already starting to be used.

The second piece that I am really excited about is agentic AI. There you have autonomous AI agents who can now take decisions, perform their actions, and even figure out what the next best action is all by themselves. I think that has the power to change the game completely, to use a cliche. But the best part of it is we are now at a stage where those AI agents will go to the data rather than the data coming to them. So we can operate AI in a federated manner. Especially for industries like insurance, where data is sensitive, it lives in multiple different places. You can't move it across jurisdictions. All of that will be tackled because we now have the ability to do federated AI agents.

Ron Rock 

So finally, a lot of leaders in the financial services space are looking for the next best thing, looking for how to transform their organizations. What advice could you give them?

Aditi Subbarao

I'll give you one sentence, which is the correct "textbook" answer, and then I'll give you the other sentence, which is the sort of "come on, guys” answer. 

The textbook answer here is, find your why, like the genuine reason why you want to do it, not just because your competitor is doing it, or not just because your marketing department expects some sound bites to provide to your customers, but what is driving you to do it. Then find the right use cases where you can actually see the value, to be able to demonstrate it and see it and feel it across the business, to energize the people. Then find the right partners and the right ecosystem to go do it with. The what, the where and the with whom are what you need to find out. 

But there's also the "come on" answer here, which is something I feel more strongly about. One of the biggest obstacles to organizations getting started with AI has been data, and a lot of leaders come to us and say, "Well, I can't get started on AI because my data is not in order. I first need to tidy up my data." In my opinion, that's a chicken-and-egg situation. It's an excuse, because you can actually use AI to clean up your data, to find you the data that will then go back into the AI to actually access all of these records that are sitting across your organization that you think you can't do anything about because they're not clean, but use AI to clean them up. It's almost like, use the AI to get your data shop in order, and then once it is in order, use the AI to give you the insights and make the actions that you need to take. 

It's almost like my advice would be, nothing is holding you back. If there's a will, there's a way, 

Ron Rock 

Thanks, Aditi. 

In today’s complex risk landscape, organizations need unified, efficient responses to interconnected threats. This Boosting Productivity eBook explores the rising demand for efficiency, the impact of silos and disconnected systems, and how a single-platform approach can drive engagement, streamline operations, and deliver actionable risk insights—without adding headcount.

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Sponsored by: Origami Risk

Businesses have ranked political risks and violence as a top 10 global risk for the third straight year, according to the Allianz Risk Barometer 2025, demonstrating that it has become a key concern for companies of all sizes. According to a new report from Allianz Commercial, civil unrest ranks as the biggest concern for more than 50% of company respondents globally, reflecting the fact that incidents are increasing and lasting longer.

Not including continuing social unrest in the Balkans and Türkiye, there have been over 800 significant anti-government protests since 2017 in more than 150 countries, with more than 160 events in 2024 alone – with 18% of protests lasting more than three months.

Following the "super election year" in 2024, policy changes by governments will continue to be trigger factors for protests and flashpoints in many countries, as could any economic hardships that result from tariff wars. In addition, an increase in terrorist attacks from religious and political extremists – motivated by both far-right and -left ideologies – is also a major concern for businesses. Companies need to adapt to volatile and uncertain geopolitical conditions to avoid negative surprises and mitigate risks.

Politics is increasingly perceived as being dominated by populism, blame and division, geopolitics by nationalism and a changing world order, and economics by mismanagement, corruption, and continually rising disparity between the rich and the rest. Political violence can affect businesses in many ways. In addition to endangering the safety of employees and customers, the violence can cause those in the immediate vicinity to suffer business interruption losses and material damage to property or assets.

Civil unrest now the major political violence concern

Businesses are more concerned about the disruptive impact of anti-social behavior on their operations than that of any other political violence and terrorism exposure. The impact of civil unrest or strikes, riots and civil commotion (SRCC) activity also ranks as the top concern in countries such as Colombia, France, South Africa, the U.K. and the U.S. Just in the top 20 countries for frequency of protest and riot activity around the world during 2024, there were more than 80,000 incidents, with India, the U.S., France, Germany, Türkiye and Spain among the hotspots, according to Allianz Research.

This view is shared by insurers, which have seen the SRCC peril increase in frequency and severity in recent years. Events, including riots in Chile and South Africa, have contributed to insured losses well in excess of $10 billion over the past decade, surpassing other levels of political violence and terrorism insurance claims. In certain hotspot territories, losses can rival or surpass those from natural catastrophes, while in others, although the direct impact may be minor, events can still trigger long-lasting changes in societies.

All kinds of civil unrest and protest activity remain a problem. Contributing factors such as high inflation, wealth inequality, food and fuel prices, climate anxieties and concerns about civil liberties or perceived assaults on democracy have not eased.

Religious and political terrorism on the rise

The increasing frequency of plots and attacks from Islamist groups and individuals, as well as supporters of far-right and far-left movements, are among the factors driving the complex global landscape. A growing concern is the Islamist terrorism threat in Europe, with an increasing number of attacks or plots happening over the last 12 months.

Terrorist attacks jumped by 63% in the West, with Europe most affected, as attacks doubled to 67. At the same time, analysis shows there were more than 100 reported terrorism and right-wing extremist incidents during 2024, driven primarily by events in the U.S., followed by Germany. Meanwhile, far-left extremists are targeting individuals or companies who they see as contributing to issues such as climate change and inequality.

Businesses need to be alive to the shapeshifting nature of political violence risk and protect their people and property by ensuring safe and robust business continuity planning is in place. Companies also need to review their insurance. Property policies may cover political violence claims in some cases, but specialist protection is also available. Businesses with multi-country exposures are showing a greater interest in political violence coverage, but there is also greater engagement from the small and medium-sized enterprises (SME) about these risks, a true reflection of increasing concern.

To read the Allianz Commercial Political Violence and Civil Unrest Trends report, click here.