Casey Beckman is Vice President, Global Compliance and Fraud, for RGA. She is a highly skilled financial services compliance professional with over 20 years of experience in building and transforming compliance and risk programs for global companies. Casey joined RGA in 2016 and leads the vision, strategy, and execution of the compliance operating model for RGA’s global operations. Before joining RGA, Casey held various compliance roles for Aegon/Transamerica, a leading provider of life insurance, retirement, and investment solutions. Casey earned a bachelor’s degree in business administration and management from Mount Mercy University, and an MBA from the University of Phoenix.

Paul Carroll

Casey, thank you for joining me today. Would you share a bit about your role at RGA and your overall industry experience?

Casey Beckman

I’ve been with RGA for eight years, and I’m currently RGA’s global chief compliance officer. I have been in the compliance field within the insurance industry for over 20 years. Prior to RGA, I worked at Aegon/Transamerica for 17 years and held various compliance roles both on the insurance side as well as the asset management side. I have experience in leading all aspects of a global compliance program, including artificial intelligence governance, financial crimes monitoring, economic sanctions compliance, code of conduct policy compliance, regulatory change management, compliance assurance testing, and directing investigations.

Paul Carroll

Based on your experience, what do you see as a major risk trend for the insurance industry?

Casey Beckman

The use of artificial intelligence comes to mind. This emerging risk has been impossible to ignore as we continue to use AI in both our personal and work lives. Along with this use comes an increase in compliance and ethical concerns such as data protection, AI transparency, and explainability, and mitigating biases that AI could create. It's important to remember that AI was built by humans and is susceptible to flaws and errors. 

RGA currently has an AI governance strategy project underway to ensure we have appropriate controls and governance over the use of AI to mitigate risks posed by AI. The project team includes various business leaders, data scientists, experts in AI technology, risk management, compliance, and legal. I can’t stress enough how important it is that organizations acknowledge that AI risk is owned by everyone, and everyone has an obligation to use AI responsibly. 

Paul Carroll 

What role can AI, data analytics, and emerging technologies play in mitigating compliance risks related to fraud, cybersecurity, and other areas?

Casey Beckman

I think AI will significantly impact how compliance teams monitor risks. 

AI has the ability to analyze a large amount of data and can quickly identify patterns and anomalies that can help with suspicious activity detection, whether it be financial transaction patterns or monitoring for cyber threats.

AI can also help with:

• Sanctions screening that could help reduce and clear false positives.
• Knowing your customer, to help determine if there are high-risk clients that would require additional due diligence or monitoring. 
• Overall compliance program health, such as metrics and reporting on key performance indicators (KPIs).
• Streamlining compliance processes, such as policy creation and maintenance, policy monitoring and adherence, and control development and assurance.
• Assisting with investigations, including performing data analytics.
• Gathering information, including from unstructured sources such as notes on paper or in PDFs and from audio and video, whose review has historically been extremely time-consuming. 
• Crafting reports, at least by providing a rough draft that provides a substantial starting point for the compliance officer.

Paul Carroll

How is the evolving AI regulatory landscape across global markets affecting risk management practices and compliance requirements?

Casey Beckman

We are beginning to see more momentum from regulators in this space, however, there are still many regulators that have yet to propose or enact regulations. Risk and compliance professionals need to ensure that any AI governance framework being developed is agile enough that as new regulations come out, the governance framework can adapt quickly to new requirements. 

Paul Carroll

What skills and mindsets will risk and compliance professionals need to develop to stay ahead of rapidly evolving AI risk facing the insurance industry?

Casey Beckman

Knowledge is power! There are obvious things like attending conferences, obtaining AI governance certifications, and building your network, which can help you stay ahead, but it’s also important to not lose sight of the fact AI governance is a team effort. It’s important to stay connected to your business leaders, research and development teams, data scientists, IT teams, etc. to align with your organization’s AI strategy to ensure the governance continues to mitigate the risks as AI evolves. Just like being agile with the regulations, the governance also must be agile and align with your organization’s AI strategy goals.

As social inflation continues to exert pressure on the insurance industry, this is not only a financial issue, but one with far-reaching impacts on insurers, beneficiaries, and society at large. 

Rising litigation costs, evolving societal expectations, and increasingly complex claims are not isolated challenges — they are part of a larger wave of change. To meet these challenges, insurers must explore changes across the board, from modernization efforts  to reforms within the legal system itself, to cultural shifts that prioritize efficiency and empathy. 

Social inflation refers to the upward trend in insurance claim costs driven by a combination of legal and societal factors, distinct from traditional economic inflation. Unlike economic inflation, which reflects changes in the cost of goods and services, social inflation stems from a broader set of influences, including increased litigation frequency, escalating jury awards, evolving societal expectations of corporate responsibility, and changes in legal practices. 

This phenomenon has become particularly pronounced in sectors such as life and liability insurance, where claim valuations have seen a significant rise. Legal frameworks that permit extended litigation and large punitive damage awards are key reasons. 

Additionally, the growing influence of litigation funding and the public’s increased scrutiny of corporate practices have intensified the financial pressures faced by insurers. 

The rise in costs associated with social inflation is not merely a result of higher claim values but reflects a fundamental shift in how claims are processed and contested. 

See also: Social Inflation and Reserve Development

According to a recent report from the Swiss Re Institute, the surge in large court verdicts has driven a 57% increase in liability claims in the U.S. over the past decade, with social inflation peaking at 7% annually in 2023. Jurors and courts are increasingly awarding higher compensation as a reflection of societal demands for fairness, justice, and accountability in corporate behavior. This shift has led to more frequent and higher cost claims, particularly in cases where punitive damages are involved. According to Swiss Re, legal expenses in the U.S. are rising at a rate that surpasses economic inflation, signaling an urgent need for the insurance industry to adapt. 

Addressing social inflation requires a multidimensional strategy that goes beyond cost control and delves into structural reform. This includes revisiting legal frameworks that contribute to protracted litigation and higher settlement costs. The existing system often delays claim resolutions, leading to elevated legal expenses that ultimately increase premiums for policyholders.

Implementing legal reforms, such as caps on damages or streamlined litigation processes, could help mitigate the financial pressures on insurers and improve the overall efficiency and fairness of the claims process. 

In addition to necessary legal reforms, a fundamental shift in corporate culture is imperative. Insurers must adopt a framework of enhanced transparency and prioritize the customer. This entails not only optimizing the claims process but also improving communication channels and transparency with policyholders and beneficiaries. 

Gianfranco Lot, Swiss Re’s chief underwriting officer, P&C Re, highlighted the severity of the issue, noting that U.S. liability lines exposed to bodily injury claims have incurred cumulative underwriting losses of $43 billion over the past five years. This has led to a significant decline in available capacity for global businesses, with rate increases failing to keep pace with escalating loss trends. 

See also: Social Inflation: Decades of Insurance Litigation Abuse

The integration of automation and data analytics can significantly enhance both efficiency and transparency. These technologies allow carriers to better leverage data, improving the prediction of claim trends, risk assessments, and process optimizations. As a result, claims can be handled more quickly, with fewer errors, and beneficiaries can receive real-time updates. 

Given the escalating underwriting losses and increasing litigation costs faced by insurers, technology must be leveraged as a crucial component of a broader strategy to address these challenges. While technology alone cannot resolve systemic issues, its role in reducing operational costs and enhancing data accuracy is vital. 

As social inflation intensifies, the industry is faced with implementing a multifaceted strategy encompassing both internal and systemic reforms. Legal changes, corporate transparency, and technological advancements must collectively contribute to a more  equitable and efficient claims process. Insurers adept at integrating these elements will be better positioned to manage the complexities of rising claim costs while upholding their obligations to beneficiaries. 

To learn more, you can download this white paper from Benekiva.

Managing loss control has never been more critical for maintaining property risk. Insurers face rising claim frequency, escalating weather events, and the need for more effective loss control measures. Traditional methods, like in-person inspections, often fall short, leaving insurers vulnerable to unnecessary losses and inflated payouts.

The good news? More insurers are adopting technology to change these outcomes. Enter location intelligence. 

By combining historical aerial imagery, property condition data, and AI, insurers gain the insights they need to optimize underwriting, inspections, and fraud reduction—and ultimately improve loss ratios.

A Common Challenge: Roof Condition Visibility

Inaccurate or outdated property condition data can cripple insurers' ability to accurately assess and price risk and manage claims. Consider Frederick Mutual Insurance Co. (FMIC). Initially, FMIC struggled with roof claims due to a lack of clear visibility into property conditions across its portfolio. Without property intelligence based on current imagery or reliable property data, the company relied heavily on inspection teams to verify roof conditions—an approach that was time-consuming, costly, and resource-intensive while not providing visibility on roof condition.

This challenge isn’t unique to FMIC; roof claims continue to be a pain point for the industry. Without a source of truth to determine if a roof was previously damaged—especially after severe weather events—insurers can’t effectively identify fraudulent claims, leaving them exposed. Location intelligence directly tackles this by delivering clearer, data-backed insights into risk.

Seeing Risk Clearly

The combination of new and historical aerial imagery, AI, and property condition data offers a comprehensive understanding of a given property—long before a physical inspection. With these tools, insurers can detect aging roofs, faulty designs, and materials exposed to weather risks, all before these conditions lead to expensive losses and claims. AI-derived detections from imagery and risk scores allow for more detailed damage assessments, enabling insurers to pinpoint hazardous or high-priority areas. This targeted approach not only enhances underwriting accuracy but also strengthens loss control efforts.

Location intelligence also sharpens claims accuracy. By comparing claimed damage against historical imagery and property data, insurers can swiftly identify discrepancies and potential fraud. Inspection processes are streamlined, helping insurers allocate resources to properties that need immediate attention.

FMIC, a Nearmap customer, found success leveraging this approach. The company improved its direct loss ratio by 57% and maintained a 36% average loss and loss adjustment expense ratio over five years. The insights not only helped FMIC reduce losses but also boosted operational efficiency, allowing for straight-through processing of low-risk properties and reallocating resources to complex cases. In essence, FMIC was empowered to write business with greater confidence. 

See also: Crucial Role of Geocoding in Insurance

The Future of Location Intelligence in Insurance

Location intelligence, powered by AI and advanced analytics, will become even more essential for insurers aiming to manage losses and verify claims. It’s also paving the way for improved risk management and better relationships between insurers and insureds. Here’s why:

1. Risk Mitigation
   Traditional risk assessment methods fall short, especially amid rising construction costs and increasing weather severity. Location intelligence allows insurers to overcome these limitations by identifying properties at higher risk for damage from hailstorms, hurricanes, tornadoes, and floods. Insurers can:
   • Offer more competitive coverage options in high-risk areas by accurately assessing and pricing risks.
   • Implement risk mitigation strategies, such as encouraging property improvements that increase resilience, protect assets, and reduce potential losses, all while making insureds partners in risk management.
2. Building Trust and Resilience With Policyholders
   No one wants to deal with a major property loss, so when a crisis hits, a fast claims response can strengthen trust between insurers and policyholders. Location intelligence enables this by:
   • Capturing imagery 24-48 hours after a severe weather event and leveraging AI to classify the damage, which accelerates claims verification and can reduce the need for physical inspections.
   • Allowing for quick communication about property damage and streamlined claims payout processes. These are practical, vital ways to help people who may be displaced and need rapid access to resources. 

See also: 'Data as a Product' Strategy

Adopting location intelligence can help insurers make more informed decisions, better manage risks, and deliver a more responsive, customer-focused experience. As the industry grapples with evolving risks, accurate AI, aerial imagery, and property condition data will remain central to building a more resilient future for insurers and their customers.

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! 

Download Now

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