In the U.S., one in four homeowners are unprepared for the costs of extreme weather events, and nearly half (47%) of Americans view homeownership as riskier with the increase in severe weather events. 69% of consumers are concerned about property damage in high-risk areas.

However, insurance carriers are also finding themselves overwhelmed with mounting catastrophe-related losses, which topped $15 billion in 2023.

Carriers are turning to emerging technology to help model and mitigate risks, and while these tools are crucial for shortening claims cycles, they don't solve the underlying problem facing the industry: a lack of property resiliency.

Property resilience refers to the ability for a physical property to withstand, adapt to or recover from outside disruptions, like extreme weather events. To keep policyholders insured in high-risk areas, carriers must bake resilience incentives into their policies to address policy gaps and high premiums, as well as work with restoration professionals to ensure the right materials are used to rebuild and future-proof homes against extreme weather.

See also: Climate and Catastrophe Risk Strategies

Current Limitations of Property Insurance

Today's property insurance model is reactive, focused on reimbursing losses and repairing damages over providing incentives to policyholders and contractors to focus on proactive measures. Such measures include strategies such as carriers subsidizing annual property assessments and contractors using climate-resistant materials, such as using flood damage-resistant materials in areas prone to hurricanes to decrease frequent restoration.

Shifting toward a more resilient model requires insurers to rethink how they are protecting homeowners in high-risk areas, specifically regions where they may be underestimating certain risk. Current risk models used by carriers are based on historical data, and extreme weather events are only increasing, with the U.S. experiencing 28 unique billion-dollar weather and climate disasters in 2023 alone and surpassing 2020's record number of disasters. Due to these significant increases, outdated models no longer work, leaving insurers unprepared for the sudden increase in frequency and intensity of extreme weather events.

Additionally, current pricing models may not factor in risk adjustment to react to climate change, creating a gap in insurance that many homeowners cannot afford. Policyholders in high-risk areas also face a lack of options when it comes to insurance, as the rise of extreme weather events has resulted in reduced or even denial of coverage in certain regions. Limited coverage options leave homeowners vulnerable to climate-related risks, and with the majority of homeowners unable to relocate to more insurable regions, high-risk areas face a new insurance crisis with each event.

Introducing Resiliency into Insurance Policies

The concept of resilience in the insurance industry has become a focal point as policyholders are facing increased external disturbances, such as flooding and hurricanes. Resilient properties are designed to keep policyholders safe, minimize damages and rebound quickly. The first step toward resiliency often includes a property resilience assessment (PRA) that allows carriers to work with inspectors to identify hazards and address them.

Addressing the lack of resilience in current policies requires carriers to focus on a three-fold approach:

1. Expanding coverage and including PRAs in high-risk areas: By expanding coverage to include different risk areas without raising premiums, carriers can ensure homeowners are offered all protections. For example, in Florida, where the majority of homeowners have hurricane policies, individuals may not have flood insurance despite flooding becoming a rising risk. Providing flood coverage ensures protection and opportunities to future-proof properties.
2. Encouraging resiliency throughout the entire insurance lifecycle: Carriers must promote resilience and offer homeowners incentives to take preventive measures. Consider including PRAs in policies, allowing homeowners to review vulnerabilities on the property and address them ahead of weather events.
3. Adopting new technologies to accelerate the claims cycle: Carriers can make a shift toward resilience and invest in predictive analytics and update their risk models to better predict weather events. Leveraging emerging technologies, like artificial intelligence, can help predict risk and forecast costs ahead of storms, heat waves, etc.

See also: What Trump 2.0 Means for Climate Initiatives

Technology-Enabled Strategies to Drive Resilience

Leveraging technology enables carriers to work more effectively with inspectors, adjusters and contractors at each step of the way. Trust between restoration companies and insurance carriers is often fragile, and this can only be solved by a trusted third-party solution that provides accurate measurements and handles claims processes automatically.

Carriers can partner with PRA inspectors to leverage immersive imagery technology that documents a property assessment, showing what areas of the property may need climate-proofing and allowing carriers to make the distinction on how to reimburse policyholders for updates. Contractors can also work with carriers to explain why these renovations should be made before potential catastrophes hit, saving all parties money.

Carriers can also use immersive imagery when working with contractors to identify a single source of visual truth for the adjustment process, helping to build trust. When both stakeholders reference the same trusted technology, they can reach the same decision faster and shorten claim cycles. By treating a claim like a forensic investigation and technology like the tools to examine the property, insurers can work with contractors to identify areas where they can build back with stronger materials to avoid future damage.

The claims adjustment process is a large part of property insurance and is often where bottlenecks occur, slowing restoration timelines. For insurers, the largest cost savings come from improving the claims management cycle. By speeding up claim times, technology can provide enormous return on investment.

A More Resilient Industry

Catastrophe-related costs continue to plague carriers, contractors and homeowners alike, and as extreme weather events increase, the insurance industry has the opportunity to move forward with resilience. By expanding coverage, adopting new technologies and baking resilience into every step of the homeowner's journey, carriers can collaborate with all parties involved to make homes resistant to damages.

Underwriting is one of the most complex business aspects for any insurance firm, and robust insurance underwriting software can facilitate it. By implementing such a system, a company can optimize and automate numerous underwriting processes, from application review and risk assessment to coverage determination and premium calculation, fostering its overall operational efficiency.

By adopting an insurance underwriting system complemented by artificial intelligence (AI), a company can further improve underwriting performance and embrace tangible business benefits. Sixty-two percent of insurance firm executives who participated in the 2024 World Property and Casualty Insurance survey from Capgemini say AI technology helps them enhance underwriting efficiency and quality.

This article highlights three business advantages firms can gain by implementing AI-powered insurance underwriting software.

Improved Underwriting Accuracy

An insurance underwriting system that AI complements can help a firm improve underwriting risk assessment and avoid many potential risks.

For example, AI algorithms embedded into insurance underwriting systems can collect both structured and unstructured data from documents submitted by applicants (birth certificates, medical reports, electricity bills, etc.) in different formats and process this data to bring it into a unified view.

Thereby, the software can help underwriters run more comprehensive and accurate risk assessments.

AI-enabled software systems can also facilitate the analysis of applicant data. AI algorithms can analyze the entire array of collected data to assess an applicant's risk profile, identify potential risks and provide recommendations to underwriters on whether the application should be accepted, according to the defined criteria. Given that AI can detect patterns in data and identify risks that might otherwise go unnoticed by humans, the technology can significantly contribute to overall risk assessment accuracy.

Fraud detection and prevention is another AI-enabled underwriting software capability that can be useful for firms. For instance, AI algorithms can identify fake information, misrepresentation or non-disclosure in potential insureds' applications much faster and more accurately than humans. An AI algorithm can flag a suspicious application, suggesting human operators examine it more thoroughly and potentially deny the application. In this context, it is no surprise that 59% of insurers that participated in the 2024 survey by Infosys are planning to increase their budgets for AI-powered fraud detection.

Enhanced Productivity of Underwriters

Underwriting software enhanced with AI capabilities enables insurance firms to improve underwriters' performance significantly. AI can automate both clerical and high-value underwriting tasks, including risk assessment and fraud detection. Compliance checks, premium calculation and record-keeping of insurance policies are other activities that can be automated with the help of AI.

Due to the automation of these and other activities, underwriters can operate much faster and accomplish more tasks – 90% of insurance specialists interviewed by KPMG in 2024 agreed that AI saves time. By reducing the load on human resources, insurance firms can achieve greater cost-efficiency and profitability. Moreover, AI-powered software enables underwriters to focus on complex customer issues and devote more time to insurance product personalization, enhancing customer satisfaction and loyalty.

Improved Business Competitiveness

Establishing accurate and competitive policy pricing is essential for any insurance firm, as price is one of the primary factors for customers to consider when choosing an insurance provider. However, setting competitive pricing for insurance premiums is a highly challenging task, especially in commercial lines. Nearly a third (32%) of all commercial lines underwriters surveyed by Capgemini in 2024 say building competitively priced policies is their major industry challenge.

An insurance underwriting system complemented with AI can help professionals streamline premium pricing activities. Specifically, after an underwriter has determined coverage for a customer, AI algorithms can calculate policy premiums based on personal information, including age, lifestyle, driving records, credit history and other data gathered from an insured and up-to-date market data related to competitors' prices (if the software is connected to third-party data sources via application programming interace, or API). Such a personalized approach to pricing can help an insurance firm foster its competitiveness in the insurance market and increase customer satisfaction.

Loop, a U.S.-based car insurance and insurtech startup, has implemented a suite of technologies, which includes telematics and artificial intelligence, to enable dynamic insurance price calculation for drivers. Instead of relying on credit scores to estimate the cost of auto insurance, AI algorithms calculate pricing based on three key factors - a customer's vehicle type, driving record and driving behavior. Loop claims its dynamic pricing approach has already helped customers save $6.3 million on insurance premiums and continues to save $1,360 per year for each customer.

Final Thoughts

Insurance underwriting software, helping businesses centralize and automate key underwriting processes, is essential for insurance firms. By implementing a system complemented with AI, a firm can enhance its underwriting workflows and leverage additional business benefits. Reduced underwriting risk, enhanced underwriters' productivity and improved business competitiveness are some notable ones.

Nonetheless, a firm can gain all these advantages only if it implements an AI-powered system successfully. Careful planning and execution are crucial for realizing the full potential of AI in insurance underwriting.

2024 was a volatile year for the U.S. insurance sector. Rising costs, wild storms, a stubbornly sluggish economy, a new chief occupant at 1600 Pennsylvania Ave., and big technological changes, such as artificial intelligence and machine learning, have all changed the game for insurance companies.

What are the major issues affecting the big consumer insurance channels in 2025? Here's what industry experts have to say.

See also: 8 Trends Shaping the Future of Insurance in 2025

How the President-Elect's Planned Policies May Affect Insurance

Unsurprisingly, the incoming administration's policies are highly likely to shake up the insurance landscape.

"In particular, there could be a heightened focus on new health and regulatory compliance," says Danny Ray, a licensed insurance agent in Jacksonville, Fla.

The new administration should also have a policy impact on the auto sector.

"For starters, the push toward electric vehicles (EVs) might lead to updated coverage frameworks as insurers navigate EV-specific risks and incentives," Ray says. "This regulatory environment will require insurers to be more agile than ever."

Home Insurance Dealing With Mother Nature

The home insurance channel has several burners going, although weather-related events might be the hottest.

"I'm concerned about the home insurance market especially when it comes to costly events impacted by climate change like flooding and wildfires," says Max Dugan-Knight, a climate scientist at Deep Sky, a carbon removal project developer. "Climate change has been accelerating faster than most climate scientists expected, and this has already had destabilizing impacts on the home insurance and reinsurance markets in climate change-vulnerable states like Florida."

See also: 10 Tech Breakthroughs Likely in 2025

Technological Disruption and Insurance

Digital disruptions – and opportunities – are also casting a big shadow over the consumer insurance sector these days. Artificial intelligence is at the front of that line.

"There's a lot going on," says Joel Pepera, director of product development at Arity, a data science technology firm in Washington, D.C. "For instance, AI-driven telematics reduces bias in auto insurance pricing for underserved or high-risk areas. AI-powered telematics reduces bias in how insurers evaluate driving risk by focusing on what truly matters—behavior behind the wheel."

Travel Insurance in the Public Policy Eye

2025 could look more like 2016 for the U.S. travel insurance sector.

"During President Trump's first term, his travel ban had lasting effects as it barred travelers from certain regions and raised concerns about shifting entry policies," says Joe Cronin, president of International Citizens Insurance in Boston, Mass.

If these or similar policies were to be reintroduced in 2025, the travel insurance industry might experience increased demand for "cancel for any reason" policies, catering to travelers wary of booking trips amid policy volatility, Cronin says. "There would be a higher demand for flexible coverage options."

Healthcare Insurance Getting More Expensive

Healthcare cost increases will be common in 2025.

"As a result of the high demand for health services, expenses related to healthcare are surging, and there is a high likelihood for insurance premiums to be raised," says David Milo, insurance strategy consultant and founder of Independent Lending in Orange County, Calif. "Pricing models will also be affected by government shifts in the healthcare system or reforms in government healthcare practice."

Life Insurance Moves

Life insurance premiums should be headed upward in 2025, albeit at a moderate pace.

According to Swiss Re Institute, life insurance premium forecasts should see 2.7% annual premium growth in 2025 and 2026. That is below the long-term trend of 3.7% as measured annually by Swiss Re from 2014 to 2023. The company reports that U.S. insurance consumers should expect smaller rate increases this year.

Auto Rate Increases on the Doorstep

Consumers can also expect auto insurance rates to rise, and by more than other consumer insurance channels.

"As an industry, insurers increased auto rates to return to profitability but as a result, policyholders took to shopping their policies to find rate relief," says Henry Kowall, director of product insurance at Arity. "Having to contend with lower customer retention, some carriers are spending again on advertising to try to win over these new shoppers and grow sales."

What Insurance Consumers Need to Do in 2025

There is no shortage of challenges facing U.S. insurance consumers heading into the new year. To stay ahead of the pack, insurance consumers should take the following actions:

Bundle coverage: Combine home, automobile and any other policy with one insurer to access discounted rates. "That should be the norm for 2025," Milo says.

Increase deductibles: An expanding selection of higher deductibles on home and automobile policies should reduce premium charges.

Monitor health insurance plans: Consumers should monitor new trends and developments within the medical space, which may require alternative options. "Consider healthcare plans that are dynamic and might be cost-effective," Milo adds.

Embrace technology: Take full advantage of telematics or AI-based apps that monitor driving lifestyle and health practices. Consumers can also receive personalized policy suggestions via AI.

Review coverage regularly: Regular review of coverage can cut unnecessary costs and ensure adequate insurance.

The insurance industry stands on the brink of a major technological evolution. Economic uncertainty and shifting risk profiles due to climate change will drive insurers to embrace more advanced technology to stay competitive. Insurers also have to deal with increasing complexity in product offerings and new regulations.

Technology such as artificial intelligence will shape the future of insurance, offering both opportunities and challenges for businesses. Companies that harness these tools effectively will be poised to outpace competitors. They'll be able to make more informed decisions and streamline operations while ensuring compliance.

Insurers will reap the most value from improving these three areas in 2025:

1. Underwriting will become more data-driven as insurers pursue faster, more accurate risk assessments and coverage profiles. The underwriting market is highly competitive for both property and casualty (P&C) and life insurers, with adverse selection applicant blind spots that might conceal risk factors on the rise. Data modernization is necessary to get a comprehensive view of all relevant data from disparate sources to better access and analyze information in real time during the underwriting process.
2. Reinsurance modernization will enhance risk management to manage increased volatility due to macroeconomic pressures like inflation, geopolitical tensions and climate-related catastrophes. The rise in these risks is pushing the insurance industry to rethink how it manages risk. Reinsurance is becoming increasingly crucial as insurers seek to mitigate the impact of large, unpredictable payouts. To manage this heightened volatility, AI and predictive modeling will be essential. In 2025, insurers will rely on AI to forecast future risks more accurately and develop dynamic reinsurance strategies. These tools will enable insurers to assess the potential for large-scale claims, optimize reinsurance purchasing decisions, and establish more robust pricing models based on real-time data. By integrating AI into reinsurance processes, insurers can automate much of the data collection and risk assessment, improving speed and accuracy. This innovation will allow insurers to build stronger, more agile risk management frameworks that can respond to shifts in the global risk landscape.
3. Technology will transform insurance compliance to be more agile amid shifting regulatory cross-currents. This is especially important with coming regulatory shifts, including the Jan. 17 enforcement deadline for the Digital Operational Resilience Act (DORA) and other potential changes with the new U.S. presidential administration. To remain compliant, insurers must change or update their systems, and implement more automation to ensure the latest regulatory changes apply to their operations and processes. With AI, insurers can also predict regulatory trends and prepare for future compliance demands, mitigating risks of non-compliance before they arise. This will save time and resources. It can also minimize penalties and enhance the firm's reputation with regulators and policyholders.

See also: 10 Tech Breakthroughs Likely in 2025

Powering Transformation with AI, Data Fabric, and Process Mining

AI is central to the transformation efforts across underwriting and reinsurance while ensuring compliance. AI enables insurers to analyze vast amounts of data to make more accurate risk assessments and streamline operations by automating tasks. It can also personalize customer experiences through AI-powered chatbots and detect fraudulent claims. This will ultimately provide better customer service and more efficient product development.

AI is particularly useful for insurers when it's used to power advanced process mining. Optimization of the underlying processes is the backbone of the modernization effort. Especially when operating within a data fabric architecture, AI is the workhorse for process mining – helping analyze event logs to automatically identify patterns and remove bottlenecks, while boosting process efficiencies.

These capabilities help insurers tackle increasingly complex issues. Consider embedded insurance, such as adding travel insurance to a plane ticket, getting concert ticket insurance, or buying e-bike insurance along with the rental. Data fabric and AI help insurers seamlessly integrate their products at the point of sale. This enables real-time risk assessment and personalized policy creation based on comprehensive customer information, all while maintaining data quality and governance across the entire process.

See also: Insurance Industry Faces Major Changes in 2025

Embracing Transformation for a Stronger Insurance Value Chain

2025 will be filled with challenges and opportunities for companies to reap more value and insight from the "insurance value chain." Insurers that embrace AI and stronger data architectures can improve everything from product development, marketing and underwriting, to policy administration, claims processing, and customer service. These gains will help insurers create and sustain more value for their shareholders and customers in the year to come.

Now that we've come out the other side of the holidays, 'tis the season for predictions about what awaits us in the coming year, and the MIT Technology Review just served up its list of the 10 biggest technology breakthroughs that it expects to occur in 2025. 

One of the AI applications cited could accelerate the efficiencies insurance companies are finding with generative AI, and another could have a profound effect on how insurance companies market themselves and generate leads. Still others could make 2025 the year of the robotaxi and possibly get you closer to having that robot valet you've always wanted. Other technologies on the list would have far smaller impacts for the insurance industry but would create challenges and opportunities by changing the risks in major industries, such as steel and ranching, while slowing the rate at which the world is heating up. 

The Review isn't always right -- and I have reservations about a couple of their projections -- but the publication is always thought-provoking. 

Let's have a look at what they expect for this year.

At the top of the list is an observatory that will come online in Chile this year and help scientists unravel mysteries such as dark matter, but, as interested as I'll be in what's learned, the observatory won't affect insurance, so let's move on to No. 2.

No. 2 is generative AI, in particular its ability to synthesize information for users of search engines, rather than take the traditional approach of serving up links. The article says: "Google’s introduction of AI Overviews, powered by its Gemini language model, will alter how billions of people search the internet. And generative search may be the first step toward an AI agent that handles any question you have or task you need done." 

The shift, which I agree will start happening in a big way this year, will disrupt marketing and sales-generation efforts that are designed to spot people asking certain questions and steer them to a corporate website for answers and, eventually, a purchase. The shift will also cause great strains for media companies, already under siege, because the search engines will now simply answer queries rather than steer users to the media outlets to read articles with the underlying information. What the change does for advertising, in particular, and journalism, in general, is hard to project, but the changes will be earth-shaking -- and, I expect, not good for the craft I've practiced for decades now.

I'm less sanguine about the prospects for so-called agentic AI in the short term. While insurance companies will get much better this year at using generative AI to more efficiently gather, process and generate documents for claims agents, underwriters and agents and brokers this year, I just don't think the technology is mature enough yet to actually let an AI act independently on behalf of a human worker. Even if the technology is ready, the trust isn't there. We all know the horror stories about AI hallucinations, and the consequences are far greater if my AI can act as me rather than just presenting information for me to review. Agentic AI will happen, but I'd put it on my calendar for 2026 or 2027. 

No. 3 is small language models. That term is designed to contrast with the large language models (LLMs) that ChatGPT, Gemini and others have developed over the past two years by consuming every bit of information they can find anywhere about anything. I've been waiting for these small models almost since I first heard about LLMs, and I think the Review is right that they should blossom this year.

Small language models benefit from what the developers of LLMs have learned but can be focused just on the insurance industry, or a segment, even on a particular company's client and knowledge base. As a result, the small models can be more precise and accurate as they serve up automated responses to clients or as they address questions on data or policy by claims agents, underwriters and others. 

Because the models are small, they require much less computation, so they're faster and use far less electricity. I expect a major impact on insurers, starting this year.

No. 4 is "cattle-burping remedies." You read that right. Cows burp methane, a far more pernicious greenhouse gas than carbon dioxide. Cows are the biggest single source of livestock emissions, which together account for as much as 20% of the world's total climate pollution. An old friend of mine is a co-founder of a company, Blue Ocean Barns, that I believe is the largest producer in the U.S. of the food additive that can reduce methane emissions 30% in dairy cattle and much more in beef cattle, so I've been reading about her work for some time and agree that the additives are both proven and ready to scale. I hope so. 

These remedies will have little effect on the insurance industry but will change some of the risks in the massive dairy farming and ranching industries.   

No. 5 gets us back to AI, in this case in driverless cars. The Review says 2025 will be a breakout year for robotaxis, and I agree. While the rollout has taken longer than many of us expected a few years ago, Waymo is now operating a driverless-taxi service in San Francisco, Los Angeles and Phoenix and plans to expand into Austin and Atlanta later this year. Others, including Amazon, are beginning to operate robotaxis in test markets. Yes, General Motors stopped funding the robotaxi efforts of its Cruise subsidiary last year, and I still see no reason to take Tesla seriously despite all the robotaxi hype built into the stock price, but I think we've passed a tipping point. 

The services will still be so limited in 2025 that they shouldn't have any noticeable effect on the auto insurance market, but the change will only pick up speed from here.

No. 6 is cleaner jet fuels. Like the food additives for cows, making jet fuel from industrial waste or carbon dioxide pulled from the air involves some really cool stuff and could be good for the environment but will have little effect on insurance other than to change some considerations about airlines and those who make their fuel.

No. 7? AI again, this time in the form of fast-learning robots, using many of the same techniques that LLMs do for generative AI. The Review says the acceleration of learning already means robots are washing dishes in industrial settings and says they soon could help out at home, too. 

I'm skeptical. I've been waiting for my robot valet ever since watching "The Jetsons" as a kid, and I've read far too many predictions like this one to think we've finally arrived. I have no doubt that robots are making stunning progress in settings such as Amazon warehouses, but you'd have to wash an awful lot of dishes at home to justify spending tens or hundreds of thousands of dollars on a robot. And what would the robot do when it isn't washing dishes? Can it clear the table and put the dishes away after washing them, while staying out of my way? Can it also vacuum, clean up after the kids, walk the dog...? 

We'll get there, but maybe in 2035, not 2025. In any case, I don't see major implications for insurers any time soon.

No. 8, long-acting HIV prevention meds, and No. 10, stem-cell therapies that can treat epilepsy, type 1 diabetes and more, would be wonderful, but even the Review doesn't expect major results for years. No. 9, "green" steel, fits in with cattle-burping remedies and cleaner jet fuels in my book. The steel industry generates about 8% of the world's greenhouse gases, so cutting way back on its production of carbon dioxide would be a huge step in the right direction, but the effect on insurers would be minimal. 

Here's hoping the insurance industry will, as usual, do its part in quantifying, mitigating and transferring risk so these 10 breakthroughs, and many more, can flourish this year.

Happy New Year.

Paul

P.S. In case you're curious, here is the list that the MIT Technology Review produced at the beginning of 2024 on expected breakthroughs last year.  I'd say they nailed it on "AI for everything," super-efficient solar cells, weight loss drugs, Twitter killers, and the first gene-editing treatment. They were mostly right on enhanced geothermal systems, heat pumps, chiplets and exascale computers. They only missed on the Apple Vision Pro (which I told you back in February would be a flop, based on the same sort of experience and analysis that convinces me that the Tesla robotaxi and robot valets are nowhere near ready). 

P.P.S. Here are the two smartest pieces I've read thus far about the innovations being unveiled at CES: The 10 Coolest Things We’ve Seen So Far at CES 2025 and AI takes over CES 2025 — one smart gadget at a time. Nothing in either article does much for me, but, then, I'm not a gadget guy. I was given a page turner for my Kindle for Christmas and was genuinely puzzled at the concept. My finger doesn't work any more? But maybe you're into gadgets more than I am. 

The end of the year is celebrated with our most cherished holidays, special food, gift giving and some downtime to reflect and recharge for whatever lies ahead in the New Year. It’s somewhat cathartic to think about what’s likely and possible but even more productive to apply some research and rigor to support predictions, regardless of their eventual accuracy. 

In the business world, it’s the time of year for pundits to look ahead and around corners to make their predictions while forecasters highlight financial models and articulate what to expect, whether at micro or macro levels. 

The P&C industry, by design, looks back to look ahead when it comes to rate making and risk modeling. Much of the 2024 story will be told throughout the first half of 2025 as earned premiums and other lagging financial results filter in and are officially reported. Considering the last three years of insurance-in-crisis, with new signs of underwriting profits returning, it’s refreshing to look forward to 2025. 

It’s also a good time to mix in a sprinkle of wishful thinking and optimism. So we will call this our 2025 P&C “Wish List,” knowing full well that moving the needle in insurance is like turning a cargo ship.

1. Rates stabilize, and competition begins to warm up, adding market capacity. As personal lines auto is forecast to be around 98.4%, per S&P, there is renewed optimism about profitability. Reinsurance rates have stabilized, and confidence in some of the most challenged markets in Florida and California is pointing in a positive direction, with insurers returning to write business. While E&S lines have absorbed displaced capacity, these encouraging signs of stability are welcome news, despite consumer and small business premium pain and protection gaps that will extend in the new year and beyond. 
2. Building trust takes center stage, and insurers act. After the fallout and still unfolding story from the murder of United Health CEO Brian Thompson, the broader insurance industry faces cynicism and distrust. High premiums, constrained access, widening protection gaps and obscured corporate identities are contributing to toxic sentiment. As lawmakers take aim at premiums and shrinking capacity, calls for regulatory changes are gaining momentum. Insurers must look for ways to upgrade their image, ensure fairness and find balance to risk transfer economics. Predict & Prevent support, discounts for loss-avoidance measures, such as smart home water leak detection and telematics for driver safety, are just two examples that need a boost.

See also: AI in Insurance: 2025 Predictions

As AI is expected to dominate over the next decade, current usage to validate claims is proving to be highly controversial and viewed as unfairly denying claims. What if AI in claims was designed to actually “look for coverage”? In other words, the art of claims adjusting has always been founded on the principle of matching the loss circumstances to the insurance (policy) contract to compare coverage and the extent to which it is applied. Although the “coverage investigation” process may not be a consumer friendly phrase, savvy adjusters turn it around and begin policyholder conversations by assuring them the process is to look and find available coverage through all the legalese. AI positioned as a fair and neutral  source, scanning for protection and certified by a professional, might be a concept worth developing. Taken a step further, AI could also optimize a claim payout by applying the age-old standard of, pay exactly what is owed – not a dollar more or dollar less. Either way, it is much about the positioning of AI and how company standards and best practices can be a strength rather than a liability for insurers.

3. New products and solutions launch to fill the protection gap. One way to fill the protection gap is with new products like parametric insurance to help soothe the sting from out-of-pocket expenses. Embedded insurance models are a perfect fit for add-on and upselling to fill the gaps borne by premium unaffordability, market restriction or more limiting policies. 
4. ClimateTech takes off. Technology has always been the answer when it comes to solving daunting problems. Better risk modeling, improved predictions and ways to determine how best to apply resources before and after large events while influencing building and infrastructure resiliency are all top of mind.
5. Market reforms pay off and spread. In some of the most challenged markets, especially California and Florida, new risk models and litigation reforms are beginning to show promise. In California, several carriers have announced plans to re-enter. In Florida, the state-run plan, Citizens, is already depopulating risk, a healthy signal, as commercial carriers take out and write risk. There are many lessons learned and a long way to go.
6. Legal abuse/social inflation comes under fire. The degree of legal abuse and growing awareness of litigation financing, nuclear verdicts and the overall impact to premiums gains higher recognition. Efforts to pump the brakes and level the playing field are sorely needed. It will not be easy, and the insurance industry needs to do more to avoid attorney representation and litigation while pushing harder for legal reforms.
7. Predict & Prevent takes another step forward. Numerous pilots and programs have been launched, on workplace safety, loss detection in homes and early warnings at construction sites, to name a few. Pilots convert to in-market reality and with more emphasis, enticing discounts or trade-off from sole risk transfer insurance.  
8. Telematics doubles down on driver safety, not just premium accuracy. The main thrust of auto telematics in personal lines has been a lot about switch and save, with untapped potential to guide and promote driver safety. Distracted driving is getting attention, and wider-adoption of crash detection is on the horizon. The compelling potential for application to incident management and emergency response will make broader adoption inevitable.   
9. AI moves from concept to reality, beyond testing and basic uses cases. AI has probably garnered more attention than all other technology combined. Insurers are walling off and limiting access while concentrating on straightforward usage, e.g., document summarization. Conversational AI is exciting yet concerning to most as even a moderate replacement of human activity. As testing proves or deprioritizes, insurers are poised to release and build toward an AI future. The training wheels will come off inevitably. 
10. M&A increases among the top 50 P&C insurers. Saved for our final prediction: At least one announced deal is foreseeable as some carriers seek to gain scale and market share while others may wish to exit, whether in commercial auto, home lines or elsewhere. Looking at the top 10 U.S. carriers, the race is most heated among four: State Farm, Progressive, GEICO and Allstate. That is most likely to continue. Look for others to strive to keep pace through acquisitions,  e.g., Aviva’s purchase of Direct Line in the U.K.

See also: Customer Engagement Trends for 2025

Aside from predictions, it is certain that the P&C industry faces headwinds: unprofitable commercial auto, a battered homeowner line, the mounting “brain drain” talent loss, lingering inflationary pressure, growing social inflation, a climate exposure wild card and more. Higher premiums are masking expense ratios at present, and stability will once again reveal demand for efficiency gain through automation and insurtech innovation, stymied over the last 18 months. 

In any case, 2025 will begin on stronger footing and is poised to be more profitable and competitive: characteristics that are foundational to our P&C Wish List.

Artificial intelligence (AI) has become a prominent topic in recent years, with its applications in the insurance industry leading to significant advancements. As insurers deal with the complexities of risk assessment and claims processing, AI emerges as a technology that enhances efficiency, accuracy and customer experience. Through machine learning, natural language processing and predictive analytics, AI streamlines underwriting, personalizes policies, detects fraud and optimizes pricing models.

AI-powered tools and the advent of generative AI are revolutionizing work methods and business operations. Despite its potential, implementing AI and generative AI requires thorough consideration of various challenges. Experts play a crucial role in ensuring a successful transition, and while generative AI won't solve all industry problems, it can significantly aid in moving the industry forward.

The History of AI

The history of AI dates to the 1950s, with Alan Turing's proposal of the Turing test and the coining of "artificial intelligence" at the Dartmouth Conference in 1956. Early programs like the Logic Theorist and General Problem Solver marked AI's birth. The 1960s and 1970s, known as the Golden Years, saw optimism and developments in natural language processing and computer vision. However, the 1970s to 1980s experienced the First AI Winter due to unmet expectations, though expert systems emerged during this time.

The 1980s to 1990s witnessed an AI boom with neural networks and machine learning expansion, followed by the Second AI Winter in the 1990s to 2000s, characterized by reduced funding and a focus on specific problems. The AI renaissance from the 2000s to the present has seen breakthroughs due to advances in computing power and big data, with deep learning and neural networks driving major achievements in speech recognition, computer vision and autonomous vehicles.

Segmentation Within AI

AI's diverse and specialized branches address distinct challenges and applications. Analytical AI excels at processing and interpreting data, while generative AI creates content. It's crucial to distinguish AI from Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and Large Language Models (LLMs), recognizing their connectedness within the broader AI landscape.

ML involves software learning and improving through adjustments based on feedback, paving the way for advanced technologies like Generative AI. DL, a subset of ML, enables machines to operate with greater complexity, using neural networks to quantify relationships between inputs. NLP finds applications in optimizing search engines and analyzing social media content. Generative AI uses deep learning techniques to generate data based on patterns from vast existing datasets.

OpenAI's Generative Pre-trained Transformer (GPT) is a type of ML specializing in pattern recognition and prediction, trained using Reinforcement Learning from Human Feedback (RLHF) to make responses indistinguishable from human responses. While AI-driven innovations like Tesla's Autopilot showcase advancements, caution is warranted when relying solely on LLMs for critical decision-making due to potential hallucinations — instances where LLMs generate incorrect or irrelevant information.

Foundation Models vs. LLMs

Foundation models and LLMs are significant AI advancements, each with unique purposes. Foundation models are versatile, adaptable for tasks like image recognition and language translation, trained on diverse datasets including text, images and audio. Examples include BERT, GPT-3/4 and PaLM. These models are continuously evolving, aiming to enhance accuracy and capabilities.

Foundation models, large-scale neural networks trained on vast data, serve as bases for various applications. They encapsulate extensive knowledge across domains, enabling adaptation to a wide range of tasks. In contrast, LLMs are specialized for processing and generating human language, excelling in text generation, translation and summarization due to training on large text data. Examples include OpenAI's GPT-3 and Google's BERT.

The key differences lie in the scope of application, training focus and development stages. Foundation models are versatile, while LLMs are specialized for language tasks. Foundation models are under active development, while LLMs are more established and widely implemented.

The Current State of AI

AI-enhanced technologies have become increasingly accessible across industries, despite their hefty price tags. Voice-based assistants drive AI adoption in sectors like IT, automotive and retail. Smaller-scale AI solutions like chatbots enable smaller brands to enhance customer satisfaction while saving resources. Software-as-a-service models democratize access to AI tools, broadening their reach.

Deep learning models excel in complex tasks like virtual assistants or fraud detection by discerning intricate data patterns. Mobile devices facilitate AI technologies, enabling voice assistants, smart monitoring, personalized shopping and warehouse management. Generative AI tools produce high-quality generative video models for major studios.

Most AI today is machine learning, finding patterns in data and making predictions. AI's influence spans intelligent applications, neural networks, AI platforms and cloud services. Emerging technologies like augmented intelligence and edge AI amplify human intelligence and enable local algorithm processing without internet connectivity. AI's influence extends to robotics, where multimodal models enable robots to perform a broader range of tasks.

The Artificial Intelligence Index Report reveals AI computing power doubles approximately every 3.4 months, highlighting AI's dynamic nature and potential to reshape industries rapidly. Generative AI (GenAI) adoption has surged, with industries integrating these technologies into operations, leading to cost reductions and revenue increases.

AI Within the Insurance Value Chain

Machines excel at analyzing data, uncovering patterns for applications like fraud detection and claims assessment. Traditional AI categorizes this capability, and machines continue to improve. Human creativity extends beyond analysis, developing innovative insurance products and marketing campaigns.

Generative AI is beginning to produce original content and ideas, such as personalized policy offerings and predictive risk models. However, transparency and confidence in predictive models' decision-making are crucial in insurance. Explainability is important as consumers and regulators need to understand pricing.

Traditional models like linear regression and decision trees have worked for decades in insurance, offering mathematical familiarity and ease of deployment. Newer AI technologies are more complex, requiring more time and understanding.

Product Development

Generative AI offers novel approaches to product development, such as using synthetic data to test safety features in auto insurance and autonomous vehicles. However, regulators must adapt to AI-based products to facilitate integration without slowing the approval process.

Sales & Distribution

A predictive scoring model in the distribution channel forecasts the likelihood of a lead purchasing a policy. AI initiatives increase conversion rates by enhancing predictability and efficiency for agents during quoting.

Underwriting & Risk

Advanced AI technologies improve risk scoring, process unstructured data and generate risk assessment scenarios. AI platforms enhance underwriting by automating risk evaluations and converting documents into decision-ready risks.

Claims & Fraud

AI reduces workload in claims processing by automating data extraction and leveraging connected car data. Synthetic data enhances machine learning models' ability to detect fraud. Data privacy, bias and ethical considerations are crucial in AI implementation.

As AI continues to evolve, its impact on the insurance industry is likely to be profound. However, successful implementation will require careful navigation of technical, ethical and regulatory challenges. The insurance sector stands at a crossroads, with AI offering transformative potential but also presenting significant hurdles. As the technology matures, insurers must balance innovation with responsibility, ensuring that AI enhances rather than compromises the industry's fundamental principles of trust and security.

The Race Between Incumbents and Insurtechs in AI

Innovation in AI drives meaningful change through collaboration between incumbents and emerging players. Initially hesitant, traditional insurers have become active in deploying AI technologies. From a venture perspective, AI is viewed as a transformational technology, but the influx of startups and venture funding creates challenges.

Startups targeting niche issues may face growth limitations, while incumbents implementing in-house solutions or adopting others pose competition. The key question is whether incumbents will engage with multiple startups or if the market will favor a "winner takes most" scenario.

The insurance industry, particularly in workers' compensation, has long prioritized stability and careful adherence to regulations. While this focus has fostered trust and consistency, legacy systems and complex state-specific regulations have added layers of complexity. The industry must evolve to meet the demands of a rapidly advancing technological landscape.

Fortunately, the necessary tools are already here. AI, automation, and application programming interfaces (APIs) are introducing unprecedented levels of speed and accuracy to workers' compensation claims. For organizations ready to explore these innovations, the potential to streamline operations and deliver better outcomes has never been greater.

Challenges Rooted in Tradition

Workers' compensation processes can be slow, shaped by strict regulatory environments and a strong emphasis on risk management. While valuable for ensuring stability, this cautious culture can sometimes make change daunting. Outdated systems often contribute to inefficiencies, complicating training and decision-making. Understandably, some skepticism toward new technologies remains, as change often comes with a learning curve.

See also: Applications of AI in Workers' Comp

Technology's Growing Impact

Despite these challenges, technology's positive impact is becoming increasingly clear. AI, for instance, is revolutionizing claims processing by providing advanced data analysis capabilities that enhance decision-making accuracy. While concerns around AI replacing human roles are common, the reality is more collaborative — AI is a tool to augment human expertise, not replace it.

Automation has become essential for streamlining workflows. Tasks that once required time-consuming manual work and were prone to errors can now be handled quickly and accurately through automated systems. Features like straight-through processing and improved data reporting make claims processing faster and more efficient. APIs ensure data moves smoothly between platforms, reducing delays and providing real-time insights. Advanced dashboards, powered by tools like Power BI, give claims managers a clear picture of problem areas, helping them make smarter decisions.

A recent Deloitte survey found that 67% of organizations expect over 80% of claims to be automatically triaged and assigned in the future — without any manual intervention. This shift frees resources to focus on complex cases that require extra attention.

Overcoming Barriers to Adoption

Adopting new technology isn't without its challenges. One of the most significant hurdles is gaining buy-in from employees accustomed to traditional methods. Workers who have spent decades mastering their roles may need time to see how new technology complements their expertise. Building trust and demonstrating the value of these tools is essential to easing this transition.

Organizations can address this need by fostering a supportive culture. Clear communication, continuing training, and visible leadership commitment are key. Case manager training should integrate standardized templates and technology-driven tools while emphasizing engagement techniques, like motivational communication, to improve outcomes for injured workers. When employees see how technology supports their work and leadership champions the change, resistance often turns to enthusiasm.

Using AI for Fraud Detection

AI can potentially transform workers' compensation, making fraud detection, policy underwriting, and claims processing faster and more accurate. Advanced fraud detection tools are already helping insurers identify exaggerated claims and prevent costly errors. For example, a study by AXA Research Fund in Spain found that most fraudulent claims involve real incidents with inflated damages. These "opportunistic" frauds usually happen once and cost less than $635. In contrast, about 40% of fraud is premeditated, with costs exceeding $3,170 per case.

To tackle fraud, teams are using tools like Microsoft's Truepic and OpenOrigins' Secure Source, which verify the authenticity of images by analyzing camera data. While these technologies can't catch all opportunistic fraud, they are becoming valuable tools for modern investigators.

Insurance companies can improve case management by adopting standardized plans with pre-populated data and automated assignments, reducing manual work and improving efficiency. Supporting staff with tools to engage injured workers and track recovery progress against return-to-work (RTW) plans can also enhance outcomes. Additionally, improving digital document management systems makes it easier for staff, injured workers, employers, and third parties to access and share claims-related documents, streamlining the process.

The potential for further innovation in workers' compensation is immense. Enhanced fraud detection algorithms, more accurate policy underwriting, and minimized data entry all point to a future where claims processes are more efficient and reliable. Early identification of complex claims and better reporting capabilities are already improving client experiences, with quicker response times and fewer manual errors.

See also: Blending AI With Human Interaction

The Future Is Innovation

The future of workers' compensation is rich with opportunity. Technologies like large language models (LLMs) and advanced prompt engineering are poised to transform how data is analyzed and shared, improving efficiency and accuracy while reducing repetitive tasks.

To remain competitive, industry professionals must embrace these changes with a continuous improvement mindset. The challenge is not simply adopting new tools but balancing innovation with the stability that defines insurance, ensuring both progress and trust.

The future of workers' compensation belongs to those willing to lead this change. Digital transformation is no longer optional — it's essential. By embracing it, we can create a system that empowers claims handlers and improves client outcomes.

The insurance industry is reaching an inflection point in its technology transformation. According to a 2024 Deloitte survey, 76% of U.S. insurance firms have already implemented generative AI capabilities in at least one business function, with claims processing, customer service, and distribution leading adoption. 2025 will mark a more decisive shift as experiments turn into enterprise-wide implementations.

The past five years have seen traditional operating models struggle to maintain profitability amid rising costs and competitive pressures. While early AI experiments helped some carriers improve efficiency, the real reinvention is only now beginning as organizations move from isolated pilots to enterprise-wide implementation.

Yet AI implementation challenges persist. Data security, privacy, and integration remain top barriers to AI adoption at scale. The proof-of-concepts and pilot projects that dominated 2023-24 are no longer enough. As AI reshapes entire industries, insurers must confront an uncomfortable truth: transform fundamentally, or risk becoming obsolete.

See also: Who's Getting Results From AI, and Why?

Key Predictions for 2025:

1. Traditional Automation Becomes Obsolete 

The era of standalone robotic process automation (RPA) and disconnected point solutions is ending. Forward-thinking insurance carriers will embrace intelligent automation platforms, integrated systems that combine AI, orchestration, and deep insurance expertise. These platforms go beyond automating repetitive tasks; they enable dynamic decision-making, end-to-end process optimization, and real-time adaptability across the insurance value chain.

Early adopters are already achieving significantly higher ROI compared with traditional automation approaches by using intelligent platforms for critical functions like claims handling and underwriting. This shift isn’t just about technology; it demands a rethinking of core processes with an insurance-specific context, embedding intelligence and agility at every level.

2. The Great Insourcing Wave Begins 

Insurance carriers will begin to reshape their operating models, bringing key operations back in-house and reducing reliance on third-party administrators (TPAs) and business process outsourcers (BPOs) with AI-enabled automation. This goes beyond cost reductions; enabling carriers to standardize processes, gain transparency over complex workflows, and deliver modern experiences.

This trend, already gaining momentum in APAC markets, shows carriers achieving both significant cost reductions and improved control over customer experience. Over the next few years, we'll see this shift accelerate globally as margins tighten and carriers prioritize operational control.

See also: How AI Is Changing Insurance

3. Platform Specialization Separates Winners from Losers 

Generic AI and automation solutions are falling short in delivering real value for insurers. Successful carriers are shifting to specialized platforms designed with deep insurance expertise, enabling them to address industry-specific challenges like complex workflows, compliance mandates, and customer demands.

These platforms go beyond one-size-fits-all solutions, offering tailored capabilities such as automated claims adjudication, underwriting engines, and fraud detection built directly into insurance workflows. Early adopters of specialized platforms are already realizing faster ROI and operational improvements compared with those struggling with costly, ineffective generic tools.

Common Pitfalls to Avoid 

• Treating AI as Just a New Disruptive Technology Initiative: Successful implementation requires a cultural shift and cross-functional collaboration. AI is not an isolated innovation initiative but an integral part of accelerated automation construct toward all operational transformation journeys driving business outcomes.
• Underestimating Governance Needs: Establish clear oversight to ensure AI models remain fair, transparent, and compliant. Without robust governance frameworks, organizations risk reputational damage, regulatory issues, and erosion of customer trust.
• Pursuing Generic AI Solutions: Focus on platforms with deep insurance expertise rather than off-the-shelf tools. Industry-specific capabilities are crucial for addressing complex insurance workflows and compliance needs.
• Neglecting Data Foundations: Ensure high-quality data and robust integration processes before scaling AI initiatives. Poor data quality and fragmented systems can severely limit AI effectiveness and lead to flawed decision-making across the insurance value chain.

Looking Ahead

The insurance industry is poised to enter an era of unprecedented transformation, driven by the integration of advanced AI capabilities across the value chain. Targeted AI interventions in key processes (not-in-good-order, or NIGO, cases, pending requirements, reflexive questionnaires, product categorization, email classification) can dramatically boost productivity and efficiency while enhancing human decision-making capabilities.

Generative AI, predictive analytics, AI-driven rules, and decision management, intelligent workflows are no longer abstract concepts—they are composable tools like Lego blocks, reshaping how insurers approach decision-making, customer engagement, and operational efficiency.

But this transformation goes beyond technology. It requires a fundamental shift in mindset, one that embraces agility, collaboration, and continuous innovation. As organizations move from pilot projects to enterprise-wide implementation, the focus must expand from immediate efficiency gains to long-term resilience and adaptability.

Leaders in 2025 will be those who understand the value of pairing cutting-edge AI capabilities with deep industry expertise. They will see AI not as a replacement for human ingenuity but as a powerful tool to augment team capabilities, enabling underwriters, product designers, and service teams to focus on high-value, strategic tasks. These leaders will create organizations that can not only adapt to change but thrive in the face of it.

By harnessing AI thoughtfully and purposefully, insurers have the opportunity to build more resilient operations, deliver meaningful customer experiences, and redefine their role in an ever-evolving world. Insurers must act now by pairing cutting-edge AI technologies like Neutrinos with deep insurance industry expertise, establishing governance frameworks, and enabling business agility.