Car insurance rates have soared in post-COVID years, and despite many insurance industry experts predicting slower rate increases in 2024, data from the first half of the year shows a 15% increase in full-coverage premiums. Industry analysts project a total 22% increase in 2024.

Rate increases in 2024 are largely a continuation from 2023, a year that saw full-coverage premiums rise by 24% in response to insurers' record underwriting losses ($33.1 billion) in 2022. Underwriting losses decreased in 2023 but were still $17 billion.

Additionally, legislative changes in states such as South Carolina and Maryland have increased insurers' financial responsibility, leading them to charge higher premiums. In California, which froze insurance rates during the COVID-19 pandemic, some insurers are requesting double-digit increases as they struggle to return to profitability, while others are exiting the state entirely.

Other Key Findings:

• The average annual full-coverage premium now costs $2,329.
• California, Missouri and Minnesota could see car insurance costs increase by more than 50% in 2024. 
• Maryland has the highest car insurance costs in the U.S., with an average full-coverage rate of $3,400 annually. New Hampshire drivers pay the least, at an average of $1,000 annually.
• Vehicle maintenance and repair costs have increased by nearly 38% over the past five years, according to the Bureau of Labor Statistics Consumer Price Index. 
• Increasingly severe and frequent weather events are driving up auto insurance premiums. Hail-related auto claims represented 12% of all comprehensive claims in 2023, up from 9% in 2020, according to CCC Intelligent Solutions.

See also: It's Time to Revitalize Auto Insurance

The 10 most expensive states for car insurance

Three of the 10 most expensive states for car insurance — Florida, Michigan and New York — have no-fault systems. In these states, drivers file claims with their own insurance companies to receive compensation for their injuries, no matter which party caused the accident. No-fault systems are supposed to speed claims but have also provided opportunities for insurance fraud.

Other states with sky-high insurance rates, like Florida, Louisiana and Nevada, face weather-related damages from hurricanes and wildfires. While climate risk has historically affected homeowners insurance more than auto insurance, insurers factor in the risk of car damage from hail, wind and falling objects.

Vehicle theft rates, traffic congestion from high population density and an increase in car accidents contribute to higher rates, too. Recent analysis of the 10 most expensive states for car insurance identified the hidden factors affecting policyholders.

Maryland

• Average annual cost of full coverage: $3,400
• Percentage higher than the U.S. average: 46%
• Projected average rate increase in 2024: 41%

Maryland saw an 8.2% rise in motor vehicle crash deaths in 2023, while on average the U.S. saw a 3.6% decline, according to the National Highway Traffic Safety Administration.

New Maryland legislation, effective July 1, requires auto insurers to provide enhanced uninsured motorist coverage. 

South Carolina 

• Average annual cost of full coverage: $3,336
• Percent higher than U.S. average: 43% 
• Projected average rate increase in 2024: 38%

South Carolina ranks 13th in the U.S. for questionable vehicle-related insurance claims, according to the National Insurance Crime Bureau (NICB).

A 2023 South Carolina Supreme Court decision increased financial responsibility for insurance companies, ruling that auto insurers cannot limit property damage claims to just covered vehicles under UM/UIM coverage. Under the new law, insurers must effectively cover all properties registered to the policyholder and their family.

New York 

• Average annual cost of full coverage: $3,325
• Percent higher than U.S. average: 43% 
• Projected average rate increase in 2024: 4%

New York drivers had the most expensive full coverage in the country at the end of 2023 but saw a 1% decrease in the first half of 2024 while rates in other states increased significantly. New York is the ninth most densely populated state in the U.S., which increases the risk of claims. New York also has the highest number of stolen cars, with 32,715 thefts in 2023, NICB data shows. 

Nevada

• Average annual cost of full coverage: $3,271
• Percent higher than U.S. average: 40%
• Projected average rate increase in 2024: 20%

The state had the third-highest vehicle theft rate in 2023, with 572.7 car thefts per 100,000 residents, according to the NICB.

Nevada's rising climate risk could play a large role in future rate setting. Wildfires burn an average of 450,000 acres in Nevada annually, and the state also sees some damage from major storms. .

Florida

• Average annual cost of full coverage: $3,201
• Percentage higher than U.S. average: 37%
• Projected average rate increase in 2024: 18%  

The state's continuing insurance crisis, influenced by severe weather events, has pushed some insurers out of the state entirely, while others have declared insolvency. In 2023, Farmers Insurance stopped offering coverage in Florida, and AAA didn't renew certain home and auto insurance policies. 

Over the past two years, Florida has seen a flurry of legislative activity aimed at reducing frivolous lawsuits against insurers, lowering consumers' insurance rates and mitigating auto insurance fraud. But the no-fault state accounts for 74% of questionable auto glass claims in the U.S., according to the National Insurance Crime Bureau. 

Louisiana

• Average annual cost of full coverage: $3,182
• Percentage higher than U.S. average: 37%
• Projected average rate increase in 2024: 23%  

The state's growing insurance crisis, tied to Louisiana's high hurricane and tornado risks, has mostly affected home insurance. However, the state's climate risk is also beginning to affect car insurance rates, as comprehensive coverage — one part of a full-coverage policy — protects against damages sustained in a weather event. 

Louisiana also saw a 10% surge in vehicle thefts in 2023, according to NICB data.  

Louisiana lawmakers passed a series of auto insurance reforms in 2024. The laws target excessive medical billing in personal injury lawsuits and limit policyholders' time to file an immovable property claim to two years after the policyholder knows (or should know) about the damage. These reforms, aimed at lowering rates, reduce some insurer responsibility. 

Delaware

• Average annual cost of full coverage: $2,982
• Percentage higher than U.S. average: 28%
• Projected average rate increase in 2024: 13% 

The state has the seventh-highest population density in the country, according to U.S. Census Bureau data. 

In one bright spot for policyholders, the Delaware Department of Insurance adopted a regulation in January 2024 requiring insurers to promptly refund any unearned auto insurance premiums (meaning payment for the unused days of coverage after a driver cancels their policy). 

Washington, D.C.

• Average annual cost of full coverage: $2,977
• Percentage higher than U.S. average: 28%
• Projected average rate increase in 2024: 17% 

Washington, D.C., has the highest population density in the nation. The district also saw traffic fatalities increase by more than 40% between 2022 and 2023, according to National Highway Traffic Safety Administration data. 

Premium increases may soon slow in D.C. The D.C. legislature passed a law requiring home and auto insurers to file for prior approval to raise rates. Excessive increases require notice and an opportunity for a hearing. Previously, D.C. had a file-and-use system, meaning insurers could raise rates immediately after filing with the Department of Insurance.

Michigan

• Average annual cost of full coverage: $2,719
• Percent higher than U.S. average: 17%
• Projected average rate increase in 2024: 8%

Michigan adopted a no-fault insurance system in 2019 in an attempt to lessen rate increases. The state saw a 4% increase in full-coverage premiums between June 2023 and June 2024, compared with a 28% rise nationwide, but Michigan still has some of the highest rates in the country.

The Michigan Department of Insurance and Financial Services Fraud Investigation Unit received 3,789 fraud reports between July 1, 2023, and June 30, 2024. 99% were insurance-related, and 50% involved auto and no-fault claims.

Georgia

• Average annual cost of full coverage: $2,688
• Percent higher than U.S. average: 15%
• Projected average rate increase in 2024: 24%

On July 1, 2023, House Bill 221 went into effect, ending the state's file-and-use provision and giving Georgia's insurance commissioner 60 days to review rate filings before insurers can implement increases. Despite the new legislation, Georgia drivers saw a 21% increase in full-coverage costs between June 2023 and June 2024.

See also: Unprofitable Insurance: Tail Effect Hits Auto Lines

States where car insurance is rising fastest

The cost of full coverage across the U.S. increased by 28% between June 2023 and June 2024 — but drivers in some states are seeing year-over-year rate increases of more than 50%.

Minnesota, which saw a 55% increase in rates, experienced $1.8 billion in damage following a series of storms that dropped hail up to baseball size across the Twin Cities in August 2023.

Severe storms also hit Missouri and northwest Illinois in 2023. A supercell produced large hail and heavy rains, forming a tornado as it moved across the states.

North Carolina faces a different weather risk — hurricanes. In 2023, Hurricane Idalia hit the state. While the hurricane weakened to a tropical storm, it still brought damaging high winds, heavy rainfall and local flash flooding. Storms like this cause water damage to cars.

Missouri and California are among the 10 states with the highest auto theft rates per capita. 

California is playing catch-up

The state froze rate increases during COVID, but drivers saw a 45% full-coverage rate surge in the last year.

California is increasing its minimum car insurance requirements. Gov. Gavin Newsom signed Senate Bill 1107 into law in late 2022, doubling and, in some cases, tripling the liability limits for auto insurance policies. The change takes effect Jan. 1, 2025. This means California residents will see higher premiums next year, albeit with higher protection limits. 

California's consumer protection laws keep insurance costs down for policyholders, but it's difficult for insurers to operate profitably. The state's Department of Insurance is slow to approve rate boosts, and insurers have pulled back on writing policies. GEICO has closed all its California offices, State Farm has stopped quoting via phone, and Progressive has halted advertising in the state.

As more insurers leave the state, the department may approve additional rate increases to keep companies in the market.

See also: Modernizing Commercial Auto Insurance

What consumers can do

Consumers can take several steps to manage insurance costs, including comparing rates among multiple insurance companies and asking about available discounts for safe driving, military service, vehicle safety features and multi-vehicle policies. Adjusting deductibles and participating in usage-based insurance programs may also help reduce costs.

Artificial intelligence can benefit the insurance industry by accelerating work processes, improving customer service and enhancing efficiency. However, many insurance companies struggle with implementation. This guide offers a step-by-step approach to identify and implement AI opportunities effectively.

Understand Company Operations

The first step is understanding organizational workflows. Start by consulting with enterprise architects to review process maps or blueprints of core operations across departments like underwriting, claims and customer service. These visual diagrams show how different units interact and where their workflows connect.

After gaining this overview, examine processes more closely. Look for pain points, especially in areas with repetitive, time-consuming or error-prone tasks. Focus on operations that, if improved, could save time or enhance customer satisfaction.

Select one value chain, department or team as a starting point. Choose a unit with clear challenges or one receptive to innovation. Working with supportive teams often yields better initial results than targeting resistant groups with larger issues.

Engage Front-Line Staff

Consult employees directly involved in identified processes. Hold workshops to review workflows, discuss challenges and identify bottlenecks. This collaboration validates initial assessments and may reveal overlooked issues.

These discussions can highlight quick wins where simple AI tools could help with minimal process changes. Focus first on high-impact pain points that are relatively easy to address.

Start Simple

When evaluating AI solutions, break down each challenge into components. Look for tools that integrate with existing systems. Prioritize solutions that don't require significant IT changes or cross-departmental coordination.

Begin with a pilot project targeting one specific issue. Test the solution with a small group before wider deployment. This approach allows for feedback and adjustments while minimizing risk.

Measure Results

Track performance metrics before and after AI implementation. Monitor processing speed, case handling volume and error rates. Gather user feedback about workflow improvements and efficiency gains.

See also: How AI is Redefining Insurance Pricing Strategies

Scale Successful Projects

For effective pilots, consider broader implementation. Present results to IT, finance and leadership teams to build support for expansion. Maintain the principles that drove initial success: keep projects focused, set clear goals and ensure user engagement.

Avoid major legacy system modifications when possible. Use APIs to connect new AI solutions with existing infrastructure. This flexibility enables rapid adjustments during implementation.

Document outcomes to guide future decisions and demonstrate value to stakeholders. Consistent measurement helps justify continued AI investment.

Implementation Guidelines

Success factors include starting small, delivering measurable benefits and involving end users. Flexible integration methods allow for easier adjustments. Performance tracking helps validate investments and build momentum.

See also: Cautionary Tales on AI

Moving Forward

AI implementation in insurance need not be overwhelming. A methodical approach focusing on process understanding, stakeholder engagement and controlled testing can yield meaningful improvements. This practical strategy helps organizations build capability and prepare for more advanced AI applications while delivering immediate operational benefits.

Artificial intelligence is often talked about as the future of the insurance industry. It's described as a game-changer that could transform how things are done. With all this excitement, insurers might feel like they need to jump into big AI projects, expecting fast results. But it's important to see AI not as a magic solution, but as a useful tool. If used strategically, AI can solve specific problems and improve current processes without depleting resources or causing major disruptions.

AI as a Tool, Not a Revolution

Many people think AI will change insurance overnight. This kind of thinking can lead to unrealistic expectations and poor strategies. Instead, AI should be seen as a powerful tool that can enhance different parts of the insurance business. By thinking of AI this way, insurers can focus on using it in specific areas instead of pursuing sweeping changes that might not meet their real needs.

To use AI effectively, organizations need to understand the whole business—from product development and underwriting to claims, customer service, and IT. This means identifying inefficiencies or repetitive tasks that AI can help solve. For example, customer service agents often struggle to provide quick and accurate information because they must search through numerous documents. Also, manual work in claims can slow processes and lead to errors. By identifying these specific issues, insurers can evaluate how AI tools might improve operations.

How to Decide on AI Solutions

When evaluating AI solutions, it's important to be practical. Cost is an obvious factor—tools should be affordable and scalable, whether that means handling more users or more data or adding features over time. Integration is also important—the less an AI tool needs to connect with existing systems, the better. Solutions that require minimal changes to current workflows and are easy to use are ideal because they cause less disruption and are more likely to be accepted by staff.

Two examples are chatbots for customer service agents and AI tools for handling documents in claims. These are effective first steps for AI because they address common problems, are relatively easy to implement, and provide quick, visible benefits without major disruptions. A chatbot can help agents find information faster, leading to better customer service. AI document tools can sort and extract information, reducing manual work and accelerating claims processing.

A practical approach to AI means starting with small projects. Choose simple initiatives that can show quick wins, like fixing a specific customer service issue or automating a repetitive task in claims. Small projects can be completed quickly, often in just a few weeks, which helps build momentum. These quick wins are important for gaining organizational support for AI and encouraging wider acceptance.

Initially, it's best to involve only the people directly affected by the issue. This keeps projects simple and avoids unnecessary complications. For early AI projects, integration with existing IT systems may not even be necessary. Once a small project succeeds, it can serve as a model for other projects and help build support across the company.

Building Toward a Larger AI Plan

After success with small projects, insurers can work on a broader AI strategy. This means scaling AI tools across the company and involving key departments like IT, finance, and leadership. Having a clear plan helps ensure AI is used in a way that meets business goals. Growing AI gradually also helps the company develop the internal skills needed for more advanced projects.

The biggest mistake is starting with projects that are too large and complicated. Signs of this include unclear goals, needing to connect with too many systems, or involving too many departments immediately, which increases complexity. Big, ambitious projects are tempting, but without experience, they can consume too many resources and disrupt workflows. A better approach is to start small, with simple, easy-to-use tools. This helps insurers get real benefits without losing control over adoption pace and scope.

Setting Up for Future Growth

By taking a practical approach, insurers can use AI to work more efficiently, improve customer service and stay competitive. The key is to avoid trying to do too much at once and instead focus on practical uses that fit into current operations. Starting with small projects not only brings quick results but also prepares the company for bigger AI opportunities in the future. A practical approach helps insurers succeed now and set the stage for long-term growth as AI technologies continue to improve.

AI has significant potential for the insurance industry, but using it successfully requires a careful, step-by-step approach. By seeing AI as a set of tools for solving specific problems, insurers can implement solutions that are cost-effective, easy to integrate and compatible with current processes. This approach allows companies to build skills over time and create a strong foundation for more advanced AI use in the future.

Artificial intelligence promises to revolutionize the insurance sector with streamlined operations, enhanced customer experiences and improved risk predictions. However, this cutting-edge technology often clashes with the industry's risk-averse nature. Insurers' cautious approach, essential for managing risk, can hinder the agility and innovation required to adopt AI. This tension has led many insurers to take a measured, "wait-and-see" approach, delaying the integration of transformative technologies.

The insurance industry, historically, has been relatively resistant to disruptions. Infrequent customer interactions and complex, long-term policies create customer "stickiness" that reduces pressure for technological improvements. Stringent regulations, designed to protect consumers and ensure financial stability, pose significant barriers to disruptors.

Natural barriers to entry into the insurance industry are expected to persist short-term, but the rapid pace of AI development presents a challenge. Insurers that don't adapt will miss opportunities to enhance efficiency, lose their edge in risk prediction and fail to meet evolving customer expectations.

See also: Cautionary Tales on AI

AI in the Insurance Industry

Imagine a world where policy administration and claims processing – tasks that once consumed countless work hours – are autonomously coordinated by AI. These intelligent systems can route tasks, automate routine decisions and escalate complex cases to human experts. This improvement will significantly cut down on the maintenance costs of large insurance companies.

These benefits extend to customer experience, as well. AI-powered chatbots and virtual assistants are already transforming this part of the value chain, offering instant, 24/7 support. Consider a scenario where an employee has just experienced a work injury. An AI-powered claims assistant guides them through the process, analyzing photos of the injury, cross-referencing policy details and even scheduling a medical appointment – all within minutes. By handling these routine tasks, AI frees human agents to build meaningful relationships.

Risk assessment, the cornerstone of the industry, also stands to be transformed. Machine learning algorithms can analyze vast datasets, uncovering patterns and insights that might elude the most experienced actuaries. Generative AI will further harness unstructured data, including as much as 80% of all available information in insurance companies. By leveraging these underused insights, insurers can create comprehensive, 360-degree views of the insured. This deeper understanding leads to more accurate and personalized risk profiles, significantly enhancing the quality of underwriting decisions and claims assessment.

'It all sounds promising, but ...'

That's the common refrain from insurance colleagues. While AI holds immense promise, there's a notable gap between its potential and the reality of implementation.

One hurdle that affects all industries is the disconnect between AI expectations and current real-world applications. This gap frequently leads to overly ambitious implementation plans and subsequent disappointment when results do not materialize as quickly as anticipated.

There are also unique hurdles that the insurance industry faces. One significant obstacle is the sector's reliance on legacy systems. These systems, developed over decades to handle complex insurance processes, aren't easily compatible with modern AI tools. Integrating AI often requires a substantial overhaul, including redesigning data pipelines, creating new interfaces and establishing processes to effectively leverage AI outputs. These overhauls are costly and time-consuming, especially to an industry with limited IT resources.

Moreover, the data within these legacy systems, historically treated as a byproduct of the process, often lacks the quality, organization and accessibility required for effective AI applications. In the age of AI, high-quality data that accurately represents the insured risks and customer behaviors is a critical asset, as it significantly affects the ability and accuracy of AI solutions.

Given the critical role of AI in insurance operations, it's essential to implement robust safeguards to ensure reliability, accuracy and security, including measures like output verification, anomaly detection and human oversight. Additionally, AI introduces security vulnerabilities, requiring expertise to defend against risks such as adversarial attacks on underwriting models and privacy breaches in customer service chatbots.

Strategic Considerations

An important step for insurers is investing in the quality and availability of current and future data. This paves the way for future AI success and is challenging to rectify retroactively, making it a high priority for those seeking to leverage AI to its fullest. Without high-quality, accessible data, even the most sophisticated AI models will fall short of their potential.

Another key factor is governance. Establishing effective AI governance requires insurers to develop frameworks that address the unique challenges posed by this technology. This entails creating clear policies and guidelines for AI use, incorporating ethical considerations and defining transparent decision-making processes.

There is also a need to invest in human capital. Insurance companies require specialists with in-depth understanding of AI applications, including data scientists, AI engineers and AI security experts. However, these skills are in high demand and short supply, with big tech companies fiercely competing for this talent. To address this challenge, insurers should pursue a dual strategy: actively recruiting specialized talent while simultaneously developing these capabilities in-house by training existing staff.

Equally important is bringing the rest of the organization on board with AI. This includes employees across operations, risk and HR departments that will interact with AI tools in their daily work. One strategy is to identify a core group of enthusiastic users. By providing additional training to these key users, companies can create internal champions for AI adoption. This investment in skills development not only empowers employees to collaborate effectively with AI but also lays the groundwork for broader understanding and acceptance throughout the organization.

Transparent communication about the company's AI strategy and its potential benefits is essential. Company-wide discussion should highlight opportunities AI creates for employees, such as the ability to focus on higher-value tasks. As AI advances, many employees will grow apprehensive about their future roles. By building trust and clearly articulating AI's goals and future, companies can address these concerns effectively. This proactive approach to communication will pay dividends, fostering a workforce that is both prepared for and enthusiastic about AI integration.

See also: Insurance: An Industry Embracing AI

Start Small, Think Big

How can insurers move forward without getting bogged down in complex implementations with limited IT resources?

A practical approach is to begin with pilot projects in low-risk, high-impact areas where AI can deliver tangible value. Employee-targeted service chatbots or document verification are ideal starting points.

Chatbots can quickly retrieve policy coverage details or explain common claim procedures. AI-powered document verification could streamline claim payouts by automatically extracting and validating information from submitted receipts or medical reports. These projects allow insurers to gain practical experience with AI implementations while building confidence in its capabilities. Such initial projects serve as stepping stones, facilitating broader AI implementation while minimizing risks.

Collaborating with insurtech firms helps insurers explore AI's potential. These partners, unburdened by legacy systems, can speed up AI integration. This collaboration strengthens AI foundations while maintaining governance and workforce awareness.

Insurers should also modernize core systems alongside AI implementation, ensuring long-term data-driven solutions. Key steps include improving data quality, updating infrastructure and ensuring cross-platform compatibility. A solid data foundation is crucial before advancing AI integrations as technology evolves.

Conclusion

AI's potential in insurance lies more in its future possibilities than in its present reality. The industry's inherent conservatism, while essential for managing risk, creates a unique tension with the rapid pace of AI development. This presents both a challenge and an opportunity for insurers willing to navigate this complex landscape.

A strategic approach that balances the industry's need for caution with the growing necessity to innovate is ideal. By embracing this approach, insurers can bridge the gap between AI's promise and the pragmatic realities of the insurance business. This strategy enables them to navigate the hype, address the real challenges of legacy systems and data quality, and pave the way for meaningful AI integration.

While the insurance industry has, to its great credit, been leaning into initiatives to make people and their property more resilient in the face of climate change, last week's U.S. presidential election means the regulatory environment will be significantly different, come Jan. 20. 

Donald Trump has long referred to the "climate hoax" and has called President Biden's big climate-related law the "Green New Scam." Trump will surely withdraw the U.S. from the Paris climate accord, as he did during his first term, and may even do so through an executive order on his first day in office. Backed by considerable oil and gas money, the president-elect has promised to "drill, baby, drill." So any regulatory support for reducing emissions and slowing climate change will be out the window. 

But all is not lost for those of us who see climate change as one of the great challenges of our time. Trump won't necessarily block efforts to strengthen buildings and infrastructure in the face of intensifying storms — as long as the justification never mentions the word "climate." States and local governments will soldier on, in any case. Market forces will also keep driving progress, especially for electric vehicles, albeit more slowly, and for property resilience. And institutional inertia and legal challenges likely mean Trump won't be able to accomplish some of the more extreme measures being urged on him by conservative groups, even though Trump 2.0 is heading into office with much more experience and a more detailed plan than Trump 1.0 had eight years ago. 

I can't claim a crystal ball, but I'll lay out a few thoughts concerning how the shift in the federal approach to climate will affect the insurance industry and how we can play a key role over the next four years. 

Perhaps the biggest shift will affect electric vehicles, whose share of the new car market in the U.S. will now likely be just 28% in 2030, below the previous forecast of 33%, according to automotive forecaster GlobalData. 

There is, to be sure, a lot of complexity in that new forecast. Trump has long scoffed at the need to shift away from cars that burn gasoline. He promised to rescind, on the first day of a second term, the substantial incentives that the federal government has provided for purchases of electric vehicles. A Trump 2.0 Environmental Protection Agency has also been expected to withdraw mileage standards that encourage a transition to EVs. But the situation is cloudier now because of Elon Musk, the CEO and biggest shareholder of Tesla, the largest U.S. EV company. Musk publicly endorsed Trump, pumped some $200 million into the Trump campaign, and used his X social media platform (previously known as Twitter) to promote Trump. Musk clearly expects a return on his investment, and I assume he'll get one. 

Perhaps Trump's plans for massive tariffs on exports from China, which makes the world's most popular electric vehicles, will protect the U.S. market enough for Musk to be satisfied, but he may also win other concessions that would minimize the slowing of the momentum that EVs have built in recent years. 

There are lots of other forces at play, too — Wired does a thorough job of exploring them here — but the trends seem pretty clear. The increase in sales of EVs will slow, at least for a few years, even though the benefits are such that they will continue to gain share in the long term.

Slowing the transition isn't necessarily a bad thing, strictly from the standpoint of insurers. Any major transition causes problems, and EVs have proved tough to underwrite — the battery is such a big part of the value that EVs depreciate differently than do those with internal combustion engines (ICEs); collision damage that could be repaired on an ICE vehicle might total an EV because batteries really can't be repaired; etc. Insurers will need to be agile and continually adjust. 

Beyond EVs, Trump's general emphasis on letting businesses do whatever they want will make it hard for groups like the Insurance Institute for Building and Home Safety, which is trying to get regulators, insurers, and builders to adopt standards that will make structures more resilient. Fortunately, not only does insurance regulation happen at the state level, but building codes are adopted at the state or even local levels. A lot of resilience requires community effort — if your home is vulnerable to wildfire, you increase the risk to mine, and vice versa — so plenty of progress can still be made without federal involvement. 

A smart friend at a major insurance company told me this week that "we can take some lessons from how mayors are already working together, going back to the local nature of this. Mayors share information and support one another, and that’s on a bipartisan basis."

Market discipline should help with property resilience, too, as with EVs. The Washington Post reported recently: 

"In the past decade, hundreds of thousands of people have moved to places threatened by climate change, bidding up real estate from flood-prone coastlines to the fire-scarred Southwest. But [some investors] are pushing in the opposite direction. Their argument: As Americans wake up to the threat of climate change, the value of homes in risky markets will begin to slide. That’s created opportunities to profit by betting against housing markets exposed to weather catastrophes or investing in places that will attract people who want to avoid the worst."

Trump and supporters have floated some ideas for fundamental changes related to climate policy that could make life much more complicated for insurers, but it's not clear yet which of those ideas are serious and which were off-the-cuff remarks by Trump or wishful thinking by supporters. In any case, I'm not sure the ideas are high enough on his list of priorities that he'll even make a serious attempt in time. 

I say "in time" because, even if Republicans win control of the House — which seems highly likely as of this writing but isn't guaranteed — his margin will be so narrow that Democrats will be favored at this point to retake control in the 2026 elections, given how rough mid-term elections tend to be for the party of the sitting president. There's even a possibility that Republicans might lose control before the mid-terms, given that at least two members of the House seem to have already been tapped for positions in the administration. Their seats will remain vacant until special elections are held, and Democrats have done well in special elections in recent years. 

In terms of radical ideas, I'm thinking, in particular, of the proposal to abolish the National Oceanic and Atmospheric Administration, which would cause chaos for weather forecasting that insurers rely on. The proposal is part of Project 2025, a document from the Heritage Foundation that may or may not be a major planning document for the new Trump administration, depending on who was speaking and when they were talking. While Trump supporters may well be serious about slashing government agencies and departments, I suspect the real animus toward NOAA is about federal work on climate science. If the new administration can get the word "climate" out of every federal document and can slash or eliminate work on climate science, I imagine that will be enough, and I don't think privatizing weather forecasting is plausible within two years, especially with so many other big goals out there. 

Along those lines, I'm also skeptical about the talk of killing all the environmental projects in the Biden administration's big infrastructure and climate laws. Trump may well be serious, but he'll need congressional approval, and I just don't think he'll have enough of a margin in the House. While House Republicans voted overwhelmingly against both bills, now that real money is being disbursed, many have been issuing press releases claiming credit for the funds being spent in their districts. Bloomberg reports that 80% of the cleantech funds, or more than $160 billion, has been allocated to districts represented by Republicans, and I can't imagine that two or three of them won't balk at having to tell their constituents, "Oops, never mind about all that money and all those jobs."

I feel the same way about the talk of abolishing the Affordable Care Act. Trump has been promising a better plan for nine years now. How much longer am I supposed to believe he has one? Besides, Obamacare has steadily become more popular, and Trump seems to have higher priorities.

Whatever the new administration's exact priorities and policies turn out to be, the insurance industry can play an important role that no other can fill as we try to protect people and property in the face of climate change. 

We're the folks with the data about how severe the damages are from natural catastrophes and with the knowledge about how those risks could have been mitigated. We're the ones with the trend lines showing how much the damage is increasing, 

Others can argue, if they really want, about why storms are intensifying, why severe convective storms are becoming more common, why droughts and wildfires are becoming so much worse, and so on. But it's hard to dispute the dollars-and-cents arguments insurers can make.

Those arguments should be enough to let us continue a lot of the good work we've been doing to make the world a safer, more resilient place, for the next four years and beyond. 

Cheers,

Paul 

Organizational silos hinder effective risk and safety management by preventing a unified view of risks and limiting insights that inform key decisions. Integrated Risk Management (IRM) breaks down these barriers, offering a comprehensive view of an organization's risk landscape and enhancing outcomes. 

A successful IRM program has multiple benefits: 

• Expanded visibility into safety programs and operational activities can help reduce Total Cost of Risk (TCOR) by minimizing claims costs, lowering premiums, and optimizing risk management expenditures 
• Greater visibility into insurable risk and safety programs leads to stronger enterprise risk assessment, mitigation, and management, as well as improved compliance 
• Tying safety programs to financial and enterprise risk can lead to buy-in for EHS initiatives, ultimately reducing incidents and injuries 

This eBook examines: 

• How RMIS, GRC, and EHS systems contribute to identifying, assessing, and managing organizational risk 
• Why true IRM requires a unified platform for sharing all safety and risk data across the enterprise 
• Practical challenges and best practices for implementing an effective IRM strategy 
• How new technologies are going to shape the future of risk and safety management 

Download the eBook to start your journey towards improved outcomes and operational resilience! 

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Sponsored by: Origami Risk

Artificial Intelligence is one of the key topics in every insurer’s discussions. However, what technologies are considered part of Artificial Intelligence and what are the factors insurers need to take into consideration when investing in AI? It is important for insurance professionals to understand the impact of the technology. 

What is AI to insurers 

Insurance companies are already doing interesting things in leveraging different AI technologies. Some of the technologies that are considered part of AI when asking insurers include: Machine learning, Deep learning, Neural networks, Natural Language Processing (NLP), image recognition, Speech/Voice Recognition, Data science, Robotic Process Automation (RPA), DevOps / DataOps. 

It is important to understand that where insurance companies see AI can have a big impact on their business. Celent research shows that AI-related technologies can have the biggest impact when incorporated in the claims processes. Predictive fraud analytics is one of the areas that is expected to see broader adoption in the near future. The next area is utilizing AI for customer service improvements. Natural language processing has been around for a while now to improve customer interactions, trying to optimize and automate customer service. 

If AI can be used to optimize claims processes and identify and mitigate fraud, there are more resources and time available to improve customer service. Technology has a direct impact on how you can free up resources to improve processes. 

The product development side, including underwriting, sees insurers using AI to optimize pricing. New types of data and models are used in order to price risks. It now goes beyond cost parameters, as predictive analytics is used in the context of pricing optimization to determine customer behavior and price elasticity. 

Moving to AI fraud prevention 

In fraud detection, a simple solution used by numerous insurers already is to deploy automated rules at a micro level. These can be deployed quickly and only require retrospective analysis. Defining fraud patterns would be a step up the ladder, where networks and organized fraud can be identified. This needs deeper knowledge and techniques. Implementing algorithms and predictive models further allow fraud to be reliably predicted in real-time. To do so, you need to have processing power, access to various data sources (both internal and external data) and be able to shape the data models. Providing feedback constantly improves the models while they are running. 

When it comes to better understanding fraud, it has become imperative to combine prospective and predictive analytics with real-time data. While basic rules may be defined in a core system, AI goes further using data to refine models and stay relevant over time. 

Models can also be used to detect fraud at underwriting to provide an alert when a suspicious person or company applies for a policy. This is especially important from a compliance perspective. Machine learning can be very powerful here, enriching data sources with all available information rather than only looking at the one application. 

Implementation speed depends on the complexity of the problem to be solved. Simple problems would require some anticipation based on lessons learned from the past. More complex problems would require not only anticipation but also prospective and predictive analysis. 

Insurance companies value AI in all areas as a key enabler of innovation. We are going to see more investment in these technologies because insurers understand there is a clear ROI and a quick payback period. 

Human intelligence vs. AI in fraud detection 

Machines should be optimized in the identification of patterns and scoring of potential fraud. Yet, human intelligence will still play an important part in special investigations. Claims adjusters and special investigators need to understand the field and leverage business expertise to derive the full potential of the tools that are available. Technology then provides the experts with insights of the bigger picture and standardized processes. Analyzing all available data can be cumbersome, if not impossible, for an investigator. Information from other experts and historical claims can be helpful, but only if it’s available in a useful format. The technology is ready for the scoring and identification of potential fraud in such a way that it can be presented in an understandable manner and really support the investigators or adjusters. The key is explainability, or “Actionable Insights”, which are of the utmost importance. 

There are 5 elements that should be taken into account when working with AI: 

1. Soundness: AI applications should be reliable and accurate, behave predictably, and operate within in the boundaries of applicable rules and regulations 

2. Accountability: Model complexity or third-party reliance should never be used as arguments for limiting the organization’s accountability. 

3. Fairness: It is vital for society’s trust in insurance that AI applications do not inadvertently disadvantage certain groups of customers. 

4. Ethics: This moral obligation goes beyond compliance with applicable legal requirements. Insurers should ensure that their customers, as well as other stakeholders, can trust that they are not mistreated or harmed, directly or indirectly, because of the firm’s deployment of AI. 

5. Skills: From the work floor to the board room, a sufficient understanding of the strengths and limitations of the organization’s AI-enabled systems is vital. 

It is important for an insurance company to review the key aspects of their business and where they see bottlenecks. Many times, simple process optimization is a good start, freeing up time for employees to focus on the areas where manual work brings the best return.Improving accuracy however is a function of consistent testing and learning. 

Recommendations 

Without having a clear view of the business case, AI is not always a definite solution. You have to think about what challenges you’re trying to solve and what people, tools, experience and technologies are needed to solve them. 

The technology itself is not always the only consideration. When looking for a new solution provider, culture is an important success factor. Implementing AI needs a well thought through change management program, because culture is the biggest building block for success. Applying AI can easily change many of the organization’s processes, systems and people. Therefore, the people need to be motivated and committed. If you want to launch an initiative around AI, change management is key. 

Keep in mind that although there is a lot of buzz surrounding the use of AI, it should be carefully considered to ensure you realize the best value from it. The fight against fraud is a good starting point when introducing AI in the organization.

Sponsored by ITL Partner: FRISS

Accenture defines embedded insurance as any insurance that can be purchased within the commercial transaction of another product or service. That covers an enormous playing field.

Embedded insurance isn’t new. Purchasing life insurance at the airport before flight departure was “version 1.0” of embedded—a model that turned into an incredibly profitable business. Another evolving embedded model is auto insurance added at point-of-sale together with a car purchase or lease. Again, not a new concept but a continuous area of interest with more recent insurer/car brand alliances. Such household purchases are major life change moments, with an opportunity to switch insurers, hence the constant attention. 

Embedded Insurance Market: Big and Getting Bigger 

The embedded insurance market is expected to grow from $156.06 billion of gross written premiums in 2024 to more than $700 billion by 2029, a CAGR of 35%.

According to Forrester’s recently released research report "Predictions 2025: Insurance," next year insurers will continue to pass on higher costs of claims expenses to customers. Continuing demand for tech and product innovation won’t bear much fruit, despite higher budgets. AI adoption will play a subordinate role to other business priorities. Insurers will increasingly rely on embedded and usage-based products to drive top-line growth and improve customer experience. 

The embedded insurance market is gaining traction due to several factors. First, it has offered a way to reach new customer segments and expand insurance coverage by embedding insurance products into popular platforms or products with large user bases. This approach has allowed insurers to tap into existing customer relationships and offer insurance solutions at the point of need or interest. Warrant protection for electronics, portable devices, and appliances is among the most popular; Allstate, in particular, is dominating the retail space with over 140 million customers since its acquisition of SquareTrade in 2016 for $1.4 billion. Last month Allstate Protection Plans acquired Kingfisher, which repairs, trades in, and upgrades mobile devices.

Embedded insurance addresses the issue of underinsurance or lack of awareness by providing coverage that is relevant and easily accessible to customers. Embedded insurance has even more potential to enhance customer engagement and loyalty. Insurers could create personalized and contextually relevant offerings by integrating insurance seamlessly into everyday products or services. Both traditional insurance companies and insurtech startups have been exploring embedded insurance opportunities, according to Modor Intelligence research. 

See also: Beyond the Hype on Embedded Insurance

Embedded Auto Insurance

Embedded auto insurance integrates offerings into the vehicle purchase journey, expanding the traditional F&I process. It not only makes the buying process easier for customers but provides advantages for dealerships, from boosting customer retention to additional revenue opportunities. While such offerings are not new, greater digitization, real-time quoting, and ease of billing/payment are newer advancements and are making embedded models more effective. 

Embedded Insurance Industry Overview 

The embedded insurance market is lightly consolidated, with few players. Some major global players include Lemonade/Metromile, Slice, Hippo, and Root Insurance. In the study period, market players were also involved in mergers and acquisitions, as well as partnerships focused on expanding their presence. The prospects for growth will likely ratchet up competition, but mid-size to smaller businesses are landing new contracts and breaking into untapped sectors thanks to product innovation and technology improvement.

Embedded Insurance for the Mobile Connected Revolution

Rapidly evolving mobility trends, advancements in connected technology, and rising customer expectations are contributing to increased volatility in the still young embedded insurance ecosystem. According to Capgemini, the rise of autonomous, connected, electric, and shared (ACES) mobility options are projected to reach 40% of the automotive market by 2030. And 42% of policyholders expect a single policy that covers them regardless of transportation mode.

The McKinsey Center for Future Mobility says connected cars are expected to account for 90% of all new U.S. vehicle sales by 2025. Advancements in connected car technology are not only reshaping insurance products and distribution but also redefining consumer relationships and expectations. Original equipment manufacturers (OEMs) like Tesla and Toyota now embed insurance directly into new car purchases. According to the 2024 Embedded Car Insurance Study by Polly, 81% of Millennials and Gen Z desire the option to purchase auto insurance as part of their car buying experience. In fact, 83% of these cohorts reported that they bought some type of embedded insurance with a recent purchase.

Adding or “embedding” insurance products directly into mobility services, including vehicle sales, ridesharing, car rentals, bike-sharing, and even public transportation systems, offers numerous benefits to consumers and service providers alike. Coverage is automatically included as part of the service, providing immediate and comprehensive protection. As demand for changing mobility grows, the potential for embedded insurance increases.

Some Noteworthy Embedded Models:

• Liberty Mutual partners with Jaguar Land Rover North America to provide tailored auto insurance solutions for Jaguar vehicle owners in the U.S. during the car buying process
• Tesla comes with built-in insurance features
• Toyota Auto Insurance is underwritten by Toggle, a digital and embedded insurance company that is part of Farmers Insurance
• Carvana entered into an exclusive partnership in 2021 with insurtech carrier Root to develop integrated auto insurance solutions for Carvana’s online car buying platform
• INSHUR formed a partnership with ride-sharing service Uber in 2018 to embed insurance directly into Uber’s platform, providing on-demand drivers with streamlined, personalized insurance coverage that adapts to driving schedules
• Turo, a peer-to-peer car-sharing platform, collaborates with Liberty Mutual to offer embedded insurance for its users

As technology continues to advance and the mobility sector evolves, the direct integration of insurance products into mobility services will become increasingly common, offering enhanced convenience, personalized coverage, and revenue opportunities for all stakeholders. Embedded insurance will drive mobility forward.

See also: Embedded Insurance: Challenges and Opportunities

Where Agents Fit In  

Through our research on the insurance consumer, we’ve learned that while customers are increasingly comfortable with learning about insurance and comparing options online, they are often not ready to make a purchase before consulting with a human agent. Most customers still pick up the phone to a call center. 

According to Accenture’s Insurance Consumer Study, 85% of consumers prefer to interact with a human when asking for advice on products or offerings. Only 15% conduct their purchase solely online.  

If consumers are looking for human touchpoints when purchasing just one insurance product, they increasingly need guidance when combining multiple, more complex products. As the risk of being wrong about the type of coverage they need multiplies, customers want to be able to rely on a single source of truth to help them sort out their exposure and figure out how to be adequately covered.  

We are sure that agents still have a significant role to play even as some products move toward embedded 3.0. Specifically, we believe that role includes helping customers understand their risk profile and how the coverages and products they buy explicitly or implicitly cover them—including where there might be overlaps in coverage. We feel insurers should pay attention to the relationship between agent and embedded and the implications for carriers, agents, and embedded distributors. 

Headwinds/Tailwinds and Challenges

It is reasonable to question embedded models’ potential distribution channel conflict, the fit for licensed agents, and the viability of the forecasts for enormous embedded premiums. Many wonder just how much of new embedded premiums are a shift from other channels, negating net gains. Will embedded serve as a catalyst for early adopter carriers to take market share from competitors? 

Meanwhile, there are threats of channel conflict, which insurance agencies have encountered since the advent of carrier direct phone sales, followed by the explosion of on-line options. Either way, agents are not only required to legally sell insurance, they are vital in navigating a myriad of insurance complexities and need to be included in embedded insurance model designs. This issue alone is a challenge for the industry, not to mention the commission compensation paradigms that have to be addressed.

The growing protection gap has never been more evident and is anticipated to accelerate. This has been widely demonstrated throughout the last several years with lack of flood insurance. Currently, insurers are re-tooling insurance policies to limit or exclude coverages in reaction to soaring loss costs and part of multi-pronged strategies to restore profits. 

Consumers and businesses alike are raising deductibles, dropping coverage, and “self-insuring” to blunt the impact of seemingly endless premium rate increases. These tectonic changes set the stage for new insurance products, including parametric and interval coverage, gap protection, and yet-to-be-developed solutions – all of which are likely to be added on and outside of existing policies. The protection gap alone creates significant tailwinds for forward-minded carriers, MGAs and insurtechs willing to enter the P&C space.

Looking Ahead

Whether you are an insurer, insurtech, agent, broker, MGA, retailer, wholesaler, or anywhere else in the insurance ecosystem and supply chain, you must invest now in learning how your business can participate in the embedded economy of the future.

Paul Carroll

I’m hoping to ask you questions about a host of issues, but let’s start with what artificial intelligence is doing in workers’ compensation. 

Bill Zachry

The amount of change that’s happening right now is awe-inspiring.

For instance, these large language models can take a lot of disparate records, put them in logical order, summate them, and give the information to the doctors in a way that they can turn into a medical-legal report. One of the problems the system has had is that there is a lack of consistent quality in the medical-legal process. AI can improve the consistency and quality of the reports. I'm very optimistic about that piece of the puzzle. 

I'm a former claims adjuster, and I think of the system in terms of a linear progression: Start with prevention, move on to benefit provision, and then close/settle the claim.

Starting with injury prevention, I’m an adviser to Voxel, a fascinating AI company that takes videos within store distribution centers and uses AI to determine whether employees are lifting safely, whether they're wearing PPE equipment, whether they're driving the forklift safely, and whether they're following other safety-related guidelnes. AI has so many applications. 

Paul Carroll 

At The Institutes, of which ITL is an affiliate, we've been talking a lot about the potential for the insurance industry to move toward a Predict & Prevent model, so I'm interested to hear more about the prevention piece of the puzzle. 

Bill Zachry

With Voxel, for instance, they provide a safety score and give the safety people specific examples of opportunities for improvement. They’ll show when people aren’t wearing their safety hats or gloves. Then the safety people go back and work with the front line. One of their big clients had a 64% drop in claims frequency, and that technology is only getting better. 

Some areas, like construction sites, are harder than distribution centers, warehouses, and retail facilities. The change in exposure is so rapid. But I think we’ll eventually get to those tougher areas, too.

Paul Carroll

I assume a focus for the future will be on getting the feedback to be in real time, or at least near real time.

Bill Zachry

It’s already there. The real challenge is making sure everyone is engaged so they will not only get the information but also be motivated to act on it. 

The next focus after prevention is claims administration.  As the U.S. has gone from a manufacturing to a service economy to a tech economy, claims frequency has been declining for decades. That trend is going to continue. But there’s been a slight uptick in severity. Severity is actually not what used to be considered severity. Severity used to be quadriplegics, burns, amputations, head injuries, hospitalizations, and things like that, which are decreasing. Now we’re experiencing claims that drag on for reasons beyond the physical injuries. 

At Safeway, we had managed care nurses call every injured worker and run them through a questionnaire to gauge the risk of delayed recovery. Identifying and intervening with the at-risk employees cut our claims costs by 40%. These are employees who had experienced what are called “adverse childhood experiences.”

My experience was that the bottom 50% of the claims only accounted for 10% of my loss dollars, while, the top 3% accounted for 60% of my dollars. I found that if I identified that 3% within the first couple of weeks and set a program to get the cases closed within the year, I could cut more than 40% of my claims costs.

Doing that is one of the big opportunities in this industry.

Paul Carroll

Is this mostly an issue of staying away from litigation?

Bill Zachry

We found that if we identified and intervened appropriately with the “at risk” injured worker, they would not litigate. These were employees who responded well if you told them that you were going to take great care of them; and if we did, it was amazing how they responded. We also found that if they had already gone to an attorney, they'd already jumped off the cliff, and there was nothing we could do to help them. They were not interested in recovering and returning to work. 

You have to be careful because every incentive has an unintended consequence. For instance, when you have safety programs, people don't want to report claims, because there are usually financial incentives for maintaining a low claims frequency. 

One of the biggest challenges in the workers’ compensation system is how injured workers’ representation is paid. That drives behaviors and drives results. An injury's optimum result is getting the right care at the right time and getting the person healthy and back to working with zero disability. Well, zero disability means the applicant's attorney gets no money. So, time and again, I see the applicant’s attorneys maximize the disability to maximize their revenue, even though that’s to the detriment of the injured worker, who can’t return to their normal job even though everything else means they could have. If you focus on returning to work instead of maximizing disability, you get a lot more people back to work.

At Safeway, we realized that there were these unintended consequences and that you have to adjust your incentives every year to account for the problems of misplaced incentives. For example, we charged the facility for every injury. Then we offered to reduce the “chargeback” if the facility provided light or modified duties. Early return to work maximizes recovery and reduces lost time.  We also did not charge the store for the hours that the employee was working while doing lighter duties. On the other hand, if the store did not report the injury within 24 hours, we added $5,000 to their charge. You have to be aware of each unintended consequence and adjust the incentives as you go along. 

Another impact of AI will be on promptly approving the right care for the injured workers. 

I believe many claims administrators will use AI to improve treatment based on the diagnosis. They will be approving the care automatically. However, there are potential unintended consequences. For instance, there is no study on the percentage of claims misdiagnosed in workers' compensation. For the 50% of the claims that account for only 10% of the loss dollars, it doesn't really matter. You could diagnose a stubbed toe, and the issue could be the right thumb, but if you just leave the worker alone, let them get treatment and go back to work, you'll be fine. It's that top 3%, 5%, 8%, or 10% of the claims where, if you have a misdiagnosis, then the treatment can really go in the wrong direction. We don’t have the guardrails to deal with a misdiagnosis, so how do you identify a misdiagnosis?

If the claims administrator is using AI to approve treatment based on the diagnosis, they should also use AI to determine if they are getting the results that they want to get in an appropriate time frame. And if we aren't, what's going on here that we should be looking at?

Paul Carroll

Where else do you see room for progress?

Bill Zachry

We have opportunities to reduce the time between requests and treatment approval; I think that can be automated.

We also need to use evidence-based treatment guidelines nationwide. For instance, according to the American Medical Association, most diagnosed carpal tunnel syndrome issues are not usually caused by work. They're caused by genetics and other problems, such as age and sex. But in California, where you need only one iota of exposure, it's 100% compensable, while in Colorado and Texas, it's very rarely considered compensable. I think getting a standard for compensability is one of the opportunities.

There is also the expansion of work presumption injuries for public safety officers. I truly appreciate what the public safety officers do and how they do it. They are the ones who are running toward the gunshots and toward the fire. But the comp system is being used inappropriately. It’s evaluating them as though they should be able to run as fast, jump as high, and lift as much when they’re 45 or 55 as when they were 25 or 35. That’s just not realistic, and generally, they already have medical care 24/7. 

I think there’s an opportunity with gig workers based on a black cab company program in New York City. Uber and Lyft drivers are considered independent contractors in California, but if we added a 3% surcharge to everything they do and used those funds to create a program for them, we could get them covered for workers’ comp through the State Fund, or whatever. 

Another impact of AI is that much of the compliance work currently done by examiners or assistants will, I think, disappear. The examiner's new focus will be on the relationship between the examiner and the injured worker, trying to find out what the injured worker needs and wants. I think that's something that can't be done by AI. I think it has to be done through creating a relationship.

In the Medicare Set Aside process, which is focused on making sure that the cost is not shifted from workers’ comp to the Social Security system or to disability, it costs several hundred dollars to have a nurse pull all the data together and generate a report. But I’ve seen prototypes where you press a button and get the report in two seconds. The cost is eight cents. The piece of the industry that generates those reports will go away. 

If you look out to the future, based on technologies like CRISPR, I'm very optimistic about what we can do for severely injured people. You can even see a day when we won’t have disability.

Paul Carroll

That's a fascinating idea that never occurred to me, but I love your optimism, especially in light of all the other promising ideas you’ve presented. Thanks for the time and the insights, Bill.

About Bill Zachry

William M. Zachry is a board member of the California State Compensation Insurance Fund, appointed by governors Arnold Schwarzenegger and Jerry Brown. He is currently program co-chair of National Comp.  He served three years as a senior fellow at the Sedgwick Institute. Zachry was awarded the Summa Comp Laude award in November 2020, the RIMS Risk Manager of the Year 2014, the CCWC Workers Compensation Professional of the Year 2016, Co-Chair AMICUS Committee California Chamber of Commerce. He is the former GVP risk management at Safeway /Albertson's, former board member California Self Insurers' Security Fund, former co-chair California Chamber of Commerce AMICUS committee, chair California Fraud Assessment Commission, Zenith Insurance VP claims, HIH (C.E.Heath) (Care America) S.V.P. claims. references.