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.

Water perils not related to weather are a part of all insurance books of business. But our environment is experiencing what seems to be increasingly unpredictable and extreme weather events that can create havoc on those same books of business. From strong ice storms and blizzards to prolonged arctic blasts, these conditions disrupt daily life, damage infrastructure, and challenge business continuity. In this evolving landscape, organizations must consider adopting monitoring solutions to anticipate and mitigate weather-related risks.

Recent events have demonstrated that severe cold now affects regions across the entire country, from the Northeast to the Gulf Coast. Real-time monitoring is essential for managing these emerging risks effectively.

A consensus has emerged among insurance carriers leading the way in IoT adoption: Connected insurance represents a societal good. This shift is driven by several key benefits:

• A reduction in expected losses
• Improved alignment of rates with risks
• Increased efficiency in claims processing

These advantages may allow a significant number of policyholders to benefit from lower premiums while maintaining the technical balance of insurance portfolios. As a result, carriers can achieve sustainable profitability while expanding the availability and affordability of coverage.

HSB (part of Munich Re) and the IoT Insurance Observatory previously explored the industry's progress in integrating IoT-driven protection in their article, "Creating the Tipping Point for Insurance IoT: A Playbook for the Future." This article outlined the advantages of a connected insurance portfolio for carriers, policyholders, and society at large.

HSB has deployed hundreds of thousands of sensors across U.S. properties. This connected portfolio enables the identification of key trends affecting insured properties while demonstrating the effectiveness of proactive protection strategies.

The Role of Networked Sensor Technologies (IoT)

Most would agree that IoT sensor technologies are critical in mitigating the impact of extreme winter conditions. Their effectiveness is built on a sophisticated ecosystem of capabilities that work together to detect adverse conditions, alert relevant parties, and enable swift action to prevent potentially costly damages.

Key components of an effective IoT approach include:

• High-Quality Sensors: These frontline devices detect water leaks, temperature fluctuations, and flow deviations. To function reliably, sensors must be highly accurate, durable, and resistant to extreme conditions.
• Real-Time Monitoring and Alerts: Continuous data transmission enables instant notifications to relevant stakeholders via SMS, email, and mobile apps.
• Responsible personnel: Alerts are only half of the equation—there must be personnel who will act in response.
• Robust Connectivity: Reliable communication through Wi-Fi, cellular, or LoRaWAN networks ensures redundancy—even during power outages.
• Advanced Analytics: Predictive algorithms identify risk trends and potential failures well in advance. Over time, these insights inform maintenance and risk management strategies.
• User-Friendly Interfaces: Intuitive dashboards focus attention on the most critical, actionable information, complemented by training and support for end users.

Notable Weather Events: A Sensor-Driven Perspective

Several extreme weather events have underscored the value of IoT-based monitoring, with Winter Storm Elliott (December 2022) and Winter Storm Enzo (January 2025) serving as key case studies.

Winter Storm Elliott (December 2022)

Winter Storm Elliott was a historic extratropical cyclone that disrupted nearly every U.S. state, including typically warm regions such as Texas and Florida. In the Midwest and Northeast, wind chills dropped as low as –30°F.

HSB's sensor network detected freezing conditions in Western states first, then tracked the storm's eastward progression. In areas unaccustomed to extreme cold (e.g., Texas), sensor alerts surged by 500% above normal levels. These timely alerts helped prevent damages worth millions of dollars by enabling preventive actions.

Winter Storm Enzo (January 2025)

From Jan. 18–25, 2025, a Siberian Express polar vortex, followed by Winter Storm Enzo, brought record-breaking cold across the U.S., affecting over 70 million people and leaving more than 75% of the country in freezing conditions.

In southern Texas and Louisiana, temperatures plummeted into single digits, with Baton Rouge's airport recording 7°F—the lowest temperature in 95 years. New Orleans experienced up to 10 inches of snow, matching a record set in 1895. For the first time, blizzard warnings were issued for coastal Louisiana and Texas.

During this period, HSB's sensor network issued approximately 9,000 alerts, with 90% related to freeze conditions. The peak of the storm (Jan. 21–22) saw 3,760 alerts in just 48 hours. Jan. 21 alone recorded 2,000 alerts—10 times the normal daily average.

The most significant impacts were observed in Mississippi, Alabama, and South Carolina, where alert volumes soared up to 40 times their normal daily levels. Thanks to these real-time alerts, policyholders were able to take preventive action, helping to avert potentially millions of dollars in water damage losses from frozen pipes. This event demonstrated a strong return on investment, with many programs exceeding a 200% ROI. This ROI is calculated by comparing the savings from this specific event to the total annual investment of IoT.

The Insurance Angle

The insurance industry must continue scaling these measures to maximize the prevention benefits demonstrated by HSB's experience. As IoT-driven solutions mature, they provide primary carriers and policyholders with critical tools to strengthen safety programs across commercial and residential property portfolios.

According to research by the IoT Insurance Observatory, carriers are pursuing several go-to-market strategies to integrate connected protection solutions:

• Complimentary Protection Solutions: Some carriers offer IoT protection services at no cost to existing policyholders. While the insurance contract remains unchanged, the insurer absorbs the service cost, expecting the reduction in claims to outweigh the investment.
• Certified Protection Solutions: Policyholders purchase and install certified protection systems, and in return, carriers provide annual premium credits. Over multiple coverage periods, the cost savings generate a positive return on investment for policyholders.
• Mandated Protection Solutions: Certain insurance products now require specific IoT technologies to qualify for coverage or receive favorable terms. This approach integrates technology directly into the insurance offering, fundamentally reshaping the industry's value proposition.

Even brokers and agents are recognizing the advantages of connected insurance. In the commercial property sector, some intermediaries now advise clients to:

• Invest in Protection Solutions: Businesses are encouraged to install mitigation technologies tailored to their specific risks.
• Adjust Deductibles for Mitigated Risks: Policyholders can opt for higher deductibles on covered perils, offsetting the cost of IoT protection with reduced annual premiums.

This strategy ensures that the investment in prevention technology is recouped through premium reductions while minimizing claims—creating a win-win scenario for insurers and policyholders alike.

Conclusion

As climate patterns continue to shift unpredictably, the insurance industry must embrace solutions to safeguard policyholders and mitigate losses. The increasing severity of winter storms, as evidenced by Winter Storm Elliott and Winter Storm Enzo, underscores the critical role of IoT sensor technology in protecting properties, reducing financial impact, and enhancing resilience.

HSB's deployment of sensor networks has demonstrated the tangible benefits of real-time monitoring, allowing carriers to see success in both weather- and non-weather-related damages. The average return for $1 invested in an IoT program is $8. These successes highlight the need for broader industry adoption, where insurers, brokers, and policyholders can collaboratively scale preventive measures to create a more resilient insurance ecosystem.

The future of property insurance lies in connected protection. By leveraging IoT-driven insights, insurers can enhance underwriting precision, reduce claim frequency, and offer more sustainable coverage options. Whether through complimentary protection, certified solutions, or mandated requirements, the integration of IoT technology is reshaping the industry's approach to risk management.

To fully realize these benefits, carriers must accelerate adoption, refine implementation strategies, and drive market education. With sensor-based monitoring, the industry can shift from a reactive claims model to a preventive protection paradigm—one that benefits insurers, businesses, and society as a whole.

This article was originally published at Carrier Management.

Political violence coverage (strikes, riots and civil commotion, or SRCC, plus terrorism and war) is currently sitting in a shifting market. Losses due to SRCC have increased by 3,000% (!) as per Swiss Re, largely due to increasing geopolitical instability and the arrival of social media. Riots in France in June 2023 are estimated to have cost the insurance market close to $780 million, and the 2024 July/August riots in the U.K. (which started and quickly spread due to misinformation and disinformation on social media) have estimated insurance losses of $320 million (which are assumed to have been limited due to the 2016 U.K. Riot Compensation Act). Terrorism coverage follows the same path, as recent losses have led to hardening markets, resulting in challenged capacity and increased premiums.

War coverage is somewhat different, with policies often containing flexibility for coverage to be withdrawn within a specific time if a threat escalates in certain industries. Even so, the 2022 Russia-Ukraine war was preceded by warnings of an imminent invasion from the Americans, while some European intelligence agencies failed to predict the onset of the war (which could have affected how many policies remained in place or were altered in time in favor of the insurer). The Ukraine war will affect insurance policies and premiums, and in October 2022 the Organization for Economic Co-operation and Development (OECD) estimated overall insurance industry losses would reach $20.6 billion.

What coverage is actually needed?

Looking specifically at the terrorism threat, do you need to buy coverage for the full value of your asset, including business interruption? This obviously depends on what kind of asset you are considering, its location, its defense lines and your risk appetite (which might include lenders' requirements). As an example, would a large-scale wind farm with a total insured value of $1 billion need to buy full value terrorism coverage? The analysis starts by quantifying the risk. There are plenty of ways to determine the risk of a specific scenario occurring, and a couple of commercially available solutions for the insurance industry exist, although they currently come with geographical limitations to their capabilities. You could argue that your estimated maximum loss (EML) for a terrorism scenario is a total loss of the asset, in a scenario that might not be fully realistic or with an extremely low probability (e.g., an aircraft impact, a 20,000-pound bomb or something similar).

The rationale for instead using a probable maximum loss (PML) approach as the trigger is that rather than purchasing terrorism coverage for the full value of assets and associated business interruption costs, alternative worst-case scenarios that do not result in the total loss of the asset are considered. This can potentially enable coverage to be tailored to the more probable events. Before that can be done, a detailed threat assessment should be completed to determine which specific scenarios should be evaluated in the PML study.

Threat Assessments

The threat assessment needs to include a full analysis of the current political situation, including historical data, which can be found from both open source and credible commercial providers. For a terrorism event, hostile actors and potential resources can be identified and used as input to the PML study for the asset. The assets actual defense lines will also help to identify the most probable scenario and how that scenario would play out in the unlikely event of an attack. There are plenty of both national and international standards in place that provide guidelines for a choice of scenario (e.g., FEMA 452), as well as theoretical bomb size. This is typically expressed in TNT equivalent, i.e., the amount of energy released in an explosion.

Blast Damage Modeling

Once the scenario has been chosen, a blast damage model can be used to determine the effects of a specific event, depending on the location of the bomb. This is a key aspect often overlooked and related to the assets defense lines. By analyzing the resulting incident overpressure, one can determine the structural damage to property. An example of an analysis done on an energy facility using the tool ALERT can be seen below.

As another example, a hotel with bollards in place outside the hotel entrance might reduce the effects of a terrorist attack significantly, as the attacker will be hindered from detonating the bomb inside the hotel. That could not just save lives but also prevent a full collapse of the building. Bollards have the benefit of always being functional, which can't be said about other protective systems, like drone defense systems, which have shown weaknesses in recent attacks on energy facilities in the Middle East. These are just two examples of security details that will affect potential scenarios and subsequent choice of insurance coverage.

To summarize:

1. Risk environments are highly volatile and territorial. The insured should look at each political violence peril in detail to determine how much coverage is required to balance capital versus risk appetite.
2. Political violence coverage can be advisable based on a targeted scenario approach.
3. A tailored PML approach offers the possibility to analyze how various safety and security measures could reduce the exposure.
4. Detailed blast damage is a critical part of the assessment process.

References:

S. Collins, "Swiss Re warns of rising riot losses," Commercial Risk, 16 October 2024.

L. S. Howard, "Insured Losses From UK Riots Will Be Manageable, With Claims Below £250M: Report," Insurance Journal, 13 August 2024.

F. Churchill, "Political violence market hardening amid rising losses: AGCS," Insurance Day, 23 February 2023.

F. Churchill, "Defining 'unprecedented' in the world of political violence," Insurance Day, 19 August 2024.

OECD, "Impact of the Russian invasion of Ukraine on insurance markets," Business and Finance Policy Papers, Paris, 2022.

S. Kalin et S. Westall, "Costly Saudi defences prove no match for drones, cruise missiles," Reuters, 2019.

As 2025 unfolds, one persistent challenge remains at the forefront: the rising cost of employee healthcare. Traditional health insurance plans continue to grow more expensive, pushing employers to seek innovative solutions to maintain both affordability and quality. Captive insurance companies are emerging as a compelling option, offering businesses a way to take control of healthcare expenses while addressing the evolving needs of their workforce. This article explores the trends shaping employee healthcare in 2025 and why businesses are finding that captive insurance can play a pivotal role.

Rising Healthcare Costs: A Pressing Challenge for 2025

Average health insurance premiums have risen by 7% in 2025, marking four consecutive years of increases, according to a report by Lending Tree. This trend is driven by increasing medical costs, expanded coverage requirements, and economic uncertainty. Employers, facing tighter budgets and a competitive labor market, are under pressure to provide attractive benefits without overburdening their finances.

The Aon 2024 Global Risk Management Survey underscores  this issue, reporting a marked increase in the use of captives for managing employee benefits risks, particularly healthcare. Employers are recognizing that the traditional approach to health insurance may no longer be sustainable in the face of mounting costs.

A Solution for Modern Employee Healthcare Challenges

Captive insurance companies offer an innovative approach to managing employee healthcare. These entities allow businesses to self-insure their workforce by creating a subsidiary that assumes responsibility for healthcare coverage. By doing so, employers gain greater control over plan design, cost management, and provider networks, leading to customized and often more affordable solutions.

Key benefits of captive insurance for employee healthcare include:

• Cost Savings: By cutting out traditional insurance carriers and negotiating directly with healthcare providers, businesses can reduce administrative costs and premiums.
• Enhanced Coverage: Captives enable employers to tailor plans to meet the unique needs of their workforce, often resulting in more comprehensive coverage options.
• Risk Mitigation: Through reinsurance, captives can protect against high-cost claims, ensuring financial stability even in the face of unforeseen medical expenses.
• Data-Driven Insights: Captives provide employers with detailed claims data, empowering them to implement wellness initiatives and identify cost-saving opportunities.

The Impact of the 2025 Presidency on Employer-Sponsored Healthcare

The recent change in presidency has introduced new dynamics to the healthcare landscape. Early signals suggest potential shifts in regulatory policies affecting employer-sponsored healthcare programs. These changes are likely to emphasize affordability, transparency, and employee access, with potential tax incentives for businesses that invest in innovative healthcare solutions like captives.

Employers must remain vigilant as new legislation could affect how healthcare benefits are structured and funded. Captive insurance companies offer the flexibility to adapt quickly to regulatory changes, allowing businesses to stay compliant while maintaining control over costs and plan design.

Trends to Watch in 2025

Several emerging trends are shaping the future of employee healthcare and are likely to drive increased adoption of captive insurance:

1. Increased Focus on Mental Health and Wellness: Employers are recognizing the importance of holistic health benefits, including mental health coverage and wellness programs. A survey by Wellable Labs found 80% of employers surveyed plan to increase their investments in employee mental health. Captives allow businesses to integrate these elements seamlessly into their healthcare plans.
2. Customization and Flexibility: As remote and hybrid workforces continue to become the norm, employees expect benefits that cater to diverse needs and geographic locations. Captives enable tailored solutions that address these complexities.
3. Technology-Driven Healthcare: Telemedicine, wearable health devices and data analytics are transforming how healthcare is delivered and managed. Captives have the flexibility to leverage these technologies to improve outcomes and control costs.
4. Regulatory Changes: With new regulations expected to affect employer-sponsored health plans in 2025, captives offer businesses a way to adapt quickly and maintain compliance.

Barriers to Adoption and How to Overcome Them

Despite their advantages, captives are not without challenges. Establishing a captive requires upfront investment, expertise and careful planning. Companies with fewer than 75 employees may find it difficult to achieve the scale needed for financial viability. However, as more businesses recognize the long-term benefits, these barriers are being addressed through:

• Partnerships with Experienced Brokers and Captive Managers: Brokers and captive insurance management companies specializing in captives can guide businesses through the setup process and connect them with reinsurance providers.
• Collaborative Models: Group captives, where multiple employers share the risk, are gaining popularity as a way to make this approach accessible to smaller organizations.
• Education and Awareness: As awareness of captives grows, businesses are better equipped to weigh the costs and benefits and make informed decisions.

Preparing for the Future

The year 2025 promises to be a turning point for employee healthcare. Businesses that explore innovative solutions, such as captive insurance, will be better positioned to attract and retain top talent while controlling costs. Captives offer not only financial advantages but also the flexibility to adapt to a rapidly changing healthcare landscape.

As the challenges of rising premiums, evolving employee expectations, and shifting regulations persist, captives provide a forward-thinking solution that aligns with the needs of modern businesses. By investing in this approach, companies can secure a healthier, more stable future for both their workforce and their bottom line.