KEY TAKEAWAY:

--Recent research estimates that one-third of the U.S. population now lives in the wildland urban interface, and construction in these areas is now the fastest-growing land use type in the coterminous U.S. This problem is no longer unique to traditionally wildfire-prone areas like California and the Pacific Northwest.

--Businesses can meaningfully reduce their risk with simple improvements like cleaning gutters and removing debris, protecting vents and openings from embers and eliminating all combustible materials within five feet of the structure. 

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The recent West Maui fires are a stark reminder of the growing impact of wildfires on both people and property across the globe. These wildfires were the deadliest in the U.S. in over a hundred years, as close to 100 people lost their lives in a matter of hours and thousands of homes and businesses were destroyed. A preliminary estimate by the University of Hawaii Pacific Disaster Center estimates the cost of rebuilding West Maui from the recent wildfires could exceed $5.5 billion. The ferocity of the fire devastated the town of Lahaina and surrounding areas in a way not previously seen on the islands, fueled by high winds from an offshore hurricane as well as local drought conditions.  

Wildland fires have become more frequent, complex and severe over the last two decades. Between 2005 and 2022, nearly 100,000 structures were destroyed by wildfire in the U.S. alone. In addition to the significant human toll, the financial costs are staggering; according to the American Property Casualty Insurance Association (APCIA), private insurers paid over $50 billion in claims from 2017 to 2022, and the federal government alone spends over $2.5 billion on wildland fire suppression annually.

While contributing factors include invasive species, land management practices and the impacts of anthropomorphic climate change, the proliferation of people and property in the wildland urban interface (WUI), or settled areas adjacent to wildland vegetation, increases the risk of catastrophes. 

Recent research estimates that one-third of the U.S. population now lives in the WUI, and construction in these areas is now the fastest-growing land use type in the coterminous U.S. The regrowth of vegetation and forests in previously cleared or developed lands like Lahaina represents only a small portion of the growth of property within the WUI. Most of this growth can be attributed to new housing developments and population migration. When these new housing developments plant roots, businesses of all types that serve residents are not far behind. This is especially a concern because of the number of wildfires attributed to human sources; 89% nationwide and 94% within California.

This problem is no longer unique to traditionally wildfire-prone areas like California and the Pacific Northwest. The population growth in the southeastern U.S. has outpaced the overall growth rate of the country by 40% over the past 50 years; the growth rate of homes and businesses in the WUI has been in the double digits, as well, particularly since 1990. 

See also: What to Do About Climate Risks?

Economic Headwinds 

Greater wildfire resilience is clearly needed for communities across the globe, but the way forward is complicated by many factors. These include current economic conditions, such as:

• Rising interest rates, which have made home ownership significantly more expensive than it was just five years ago. 
• A surge over the past three years in migration to rural and suburban areas, where there are more likely to be developments in the WUI.

Constructing homes and businesses in the WUI can also lead to higher wildland firefighting costs, which are often not considered by developers, homeowners and local governments. In turn, strains increase on wildland firefighters, who are already struggling to manage the increasing size and complexity of wildfires. The problem is real and worsening for a workforce that faces dangerous conditions on an almost daily basis. 

See also: Our New Era of 'Global Boiling'

The Road to Greater Resilience 

The U.S. Bipartisan Infrastructure Law of 2021 included funding for the creation of the Wildland Fire Mitigation and Management Commission, chaired by the departments of Interior and Agriculture, as well as FEMA. The commission, a broad range of experts from both the public and private sector, recently released "ON FIRE: The Report of the Wildland Fire Mitigation and Management Commission." This comprehensive report on the U.S. wildfire problem includes over 100 recommendations across eight key themes, including mitigation, management and post fire rehabilitation and recovery. While the report is broad and designed for policymakers, it makes clear that risk reduction for structures is a key component when it comes to minimizing risk. 

Even though the costs to retrofit homes and businesses to make them more wildfire-resistant can range widely, businesses can meaningfully reduce their risk with simple improvements like cleaning gutters and removing debris, protecting vents and openings from embers and eliminating all combustible materials within five feet of the structure. 

Additionally, it is important to consider the risk to employees from wildfire smoke. Gases and fine particulates emitted by wildfire can irritate the eyes and respiratory system; they can also worsen chronic heart and lung diseases. Rescheduling work, reducing the level or duration of physical exertion and reducing contact time are just some of the ways to protect outdoor workers when air quality is poor.

In recent weeks, a 150-year-old banyan tree that was damaged in the Lahaina fire, a well-known and beloved landmark, began to sprout new green leaves. In the face of devastation, nature is remarkably resilient and adaptive. Greater wildfire resilience will require a mix of prevention, technology, education and investment. Many of the steps needed will be difficult, requiring public/private partnership and community-wide support.

At the organizational level, businesses should seek to understand the potential risks to their operations today and in the future, as wildfire risk will continue to evolve in the decades ahead. This knowledge can help companies prioritize where investments in greater resilience are needed and support informed decisions when it comes to acquisitions and developments. 

While the work needed to address the wildfire problem is wide-ranging, the road to greater resilience can begin with simple steps -- one business, one location, at a time.

Generative AI (GAI) is taking the world by storm. What one year ago was a technology few had heard of is now splashed across the front pages of newspapers, the covers of magazines and the evening news. Its potential for transforming work seems so huge that it is a key source of contention behind writer and actor strikes in the entertainment industry. As GAI moves through almost every other industry, more battles between management and workers are coming.

In the insurance industry, progress is already happening. Generative AI has found its way into applications at the most innovative firms, especially companies that supply insurance carriers and self-insureds with software services. At my firm, our newest products incorporate elements of GAI to assist claim adjusters in organizing medical and legal documents and mining them for actionable insights. While we are an early adopter/deployer of GAI, our discussions with carriers of all sizes indicate a significant and serious interest in discovering how it can transform underwriting, claims, customer service and all other parts of the insurance value chain.

See also: 5 Ways Generative AI Will Transform Claims

What should you do if you are a carrier or self-insured who wants to get on the GAI train before it leaves the station? Following are nine steps to take that maximize your chances of success in developing and deploying generative AI applications:

1. Build a coalition of support -- "It takes a village" is an overused phrase, but in the case of building and deploying generative AI applications, it's very true. The time and resources required to succeed are significant, and much iterative experimentation is needed to create solutions that solve the problem at hand. GAI is certainly not "plug-and-play" technology right now. Leadership needs to support the effort with resources, time and patience.

2. Partner with established players -- You don't have to start from scratch. Accelerate your adoption of generative AI capabilities with SaaS solutions that do the heavy IT lifting for you. My firm has been safely and securely delivering AI-driven solutions for years and understands what it takes to drive value while being HIPAA- and SOC2-compliant.

3. Develop technical knowledge -- You probably already have data science and MLOps teams if you are seriously exploring generative AI, so set goals for them to become skilled in GAI's techniques. You may decide to outsource your development to external firms, but even if you do, your teams need to be conversant with them to absorb and learn more. And technical knowledge will be paramount if you decide to develop applications in-house. Give your teams incentives to learn, and give them the time and freedom to do it.

4. Create a cross-functional team -- When the time comes to get down to business, build project teams that comprise data scientists, data experts, business process and IT professionals and, most importantly, subject matter experts. Many failed AI initiatives can find the roots of their failure in the data scientists working behind closed doors to emerge with a solution that isn't practical, doesn't consider key constraints and isn't implementable in the company's infrastructure. Get everyone involved, and give everyone a voice from the get-go.

5. Select use cases -- Selecting use cases to solve with any AI is half the battle. Resist the temptation to start working on the first use case proposed. And resist the temptation to begin work on the CEO's chosen favorite. Include their ideas in the "hopper" and see if they make it through thorough feasibility analysis. This analysis should consider ROI and option value created if successful, odds of success, time-to-deployment and availability of cross-functional bandwidth to complete their part of the project. Don't be afraid to take big risks, but do so with eyes wide open.

6. Experiment quickly and broadly -- Strive to have at least three GIA use cases worked on simultaneously. I am a big believer in building as broad a portfolio as possible. Put your use cases on a grid of "odds of success" and "magnitude of enterprise benefit." Skew your selection process toward the "efficient frontier," concerning these two tradeoffs. Encourage your teams to use an agile approach to experimentation and development. Set realistic two-week goals and focus on achieving each.

7. Celebrate success and learn from failure -- Failures will happen, and they have lessons to teach. Do the forensics on what happened before things failed. What decisions would be made differently in the future? Codify this knowledge in the project materials and records. Celebrate successes, too, even small ones. Milestones in development, deployment and use are obvious choices. Team morale and recognition from sponsors will be yours once milestones are passed.

8. Monitor, measure, maintain -- Your initiative doesn't end when deployed! Monitor the performance of the AI and its delivery to end users from the start. Evaluate your progress against predetermined operational goals. Measure your success. After enough time has passed, you should start to see differences in the metrics used to calculate ROI, and these differences and improvements should continue to grow. Certainly, maintain your AI and the supporting processes that consume it. Things break, and models get old as data changes. Ensure that resources for monitoring, measuring and maintaining are built into your project budget.

9. Broaden the footprint -- Leverage your successes to broaden the footprint of generative AI throughout your organization. The technology is evolving quickly, and the potential applications in insurance are likely to expand greatly. Make sure you have the resources to broaden the footprint. When your generative AI projects succeed, you will become the most sought-after resource in your company.

See also: 3 Key Uses for Generative AI

I firmly believe that we are at an inflection point in the economy's development and that generative artificial intelligence is the major cause.

There will be players who will overhype and then fall short of lofty goals, but this AI revolution is real. The computing bandwidth available to create large language models and the many ways data scientists are experimenting with GAI techniques assure me that it is here to stay and thrive.

With this article, I've given you a solid road map to success. Good luck!

When an employee is hurt on the job, returning to work often involves more than just healing the underlying physical injury. Depending on the diagnosis and other factors, a workplace event can lead to or exacerbate mental health diagnoses such as anxiety and depression, which often extend recovery time. It’s worth discussing the role case managers play in identifying and addressing underlying behavioral health symptoms to keep claims on track.

There’s no doubt that addressing mental health is beneficial for both employees and their employers. Every $1 spent toward helping employees manage anxiety and depression results in $4 in employee productivity and reduced health claims, according to a World Health Organization study. Studies reveal the likelihood of injured employees being treated for depression is 45% greater than those who are not injured. In addition, people injured at work are more likely to become depressed than those hurt outside of work.

But even when mental health services are available in the workplace, few employees know how or where to access them. According to the Society for Human Resource Management, only one in 10 employees are aware of benefits they can access via their employee assistance program (EAP). 

Stress vs Distress

Mental health’s impact on claims typically plays out when an injured employee does not meet his or her return to work (RTW) goals. The claim management team becomes frustrated because the individual had no history of mental health issues, and injury and comorbidities were factored into the risk assessment score, part of the predictive modeling tools case managers use to determine recovery time. 

But an individual may nevertheless face fear and anxiety as a result of an injury, and that may extend to everyday events that were more manageable prior to injury, but post-injury now feel insurmountable. This is more than “stress.” It is “distress,” a form of stress that can lead to depression, confusion, poor concentration, anxiety and poor performance. People react differently to distress, and reactions are dynamic, often determined by the context of the employee’s daily life.

Let’s take a look at two scenarios.

John is 27 years old, single and living by himself. One day, he slips on the stairs and takes a fall at the office that causes a fracture in his left leg. He is quickly treated and, after a short inpatient stay, is sent home in a cast. He’s still able to drive and attend all his therapy and doctor’s appointments and has support from his parents and girlfriend, who live nearby. His medical team is astonished by his quick recovery, and he’s back on the job even earlier than expected.

If we change the context, however, the situation changes quite a bit. Let’s say John is married with a toddler. His wife has recently become unemployed, and finances are tight. His mother is recovering from a stroke and, while she’s improving, is still unable to care for herself completely and relies on John and his wife to help her out. As a result, John misses several therapy and doctor’s appointments and, in addition to continued pain in his leg, is suffering from persistent headaches. Needless to say, John’s recovery is not matching his original projections, and he’s not ready to return to work.

Without knowing the life context, these two situations look identical. What’s missing in the analysis is the ability to predict which injured employees have life stressors that will affect their recovery and return to work. 

See also: 5 Keys to a Low-Stress Work Environment in 2022

Identifying the Context; Easing Distress

Identifying these life stressors early can make a big difference in an individual’s recovery, and case managers, as well as other healthcare providers, are instrumental. A good place to start is by paying attention to physical symptoms that could indicate distress:

• Racing heart 
• Insomnia
• Dizziness or shaking
• Elevated blood pressure
• Jaw clenching and muscle tension
• Stomach or digestive problems

Assessments and conversations should consider the whole person, not just the injury. The case manager may learn of a person’s financial and other personal stressors and suggest strategies and resources that can reduce and help manage the stress.

Sometimes, it can make an enormous difference if the case manager simply listens to injured employees’ concerns and validates them. Giving injured employees permission to voice their thoughts and feelings can help reduce anxiety and depression related to isolation, boosting their ability to get back on track. People are sometimes embarrassed to ask for help. Case managers can ensure the injured individual receives the resources he or she needs to address these concerns. They can also integrate these factors into the RTW plan to offer more realistic goals based on their circumstances, helping individuals feel less frustrated and more dedicated to their recovery. 

See also: 3 New Trends in Life and Health

LASER Focus

At Genex, we use an approach we call LASER:

• L -Locating resistance that the employee might have to return to work
• A - Actively listening for verbal and non-verbal cues that might help guide the employee
• S - Selectively reinforcing the language the employee uses that indicates that they are ready for a positive change
• E- Empowering the employee to pursue the positive change, and
• R - Removing barriers that still exist to achieve that positive change

Most injuries and disabilities don’t keep people from doing what they love to do. LASER provides case managers with tools to help determine, with the affected employee, how to move forward.

But whether you formalize a strategy or simply work to understand the full situation for an employee, mental health is a real factor that needs to be considered. Sometimes, the context makes all the difference when it comes to returning to work. 

In my 20 years in the insurance technology industry, I have seen significant technology advancements but nothing as exciting as artificial intelligence (AI). 

Integrating AI in group insurance can produce tangible benefits for carriers, particularly by streamlining quoting and rating, optimizing resources, automating mundane tasks and making underwriting more accurate. 

Deloitte's “Future of Insurance Underwriting” report finds the deployment of automation, alternative data and artificial intelligence (AI) are the top three changes insurers need to make in underwriting to stay resilient and set the stage for growth. 

Despite such a clear need for urgency, the group insurance industry has been reluctant to adopt AI for many reasons, including regulatory challenges, the lack of available data to train AI algorithms and fears of technology replacing human workers. 

What's Holding the Industry Back? 

Transparency & Regulatory Challenges  

AI systems can be a black box, where data goes in, results come out and nobody, not even the developers of the AI, knows how it came to its conclusions. 

Here's the problem: Pure machine learning analyzes data in an iterative fashion to develop a model, and that process is not easily understandable. 

Group insurance is a highly regulated industry, and regulations are moving toward carriers not being allowed to make underwriting decisions that affect their customers based on black-box AI. 

For example, the E.U. has proposed AI regulations that mandate AI used for high-risk insurance applications be “sufficiently transparent to enable users to understand and control how the high-risk AI system produces its output.” 

Additionally, without proper transparency into how AI underwriting systems come to conclusions, underwriting teams won't trust the system to make correct decisions, and carriers will leave themselves vulnerable to the risks of being deemed biased. 

The New York State Department of Financial Services says they’re concerned insurers could use their algorithmic underwriting systems to discriminate against consumers illegally. New York’s insurance laws and similar laws elsewhere prohibit using race, national origin, lawful travel, mental or physical disabilities or traumatic experiences such as domestic abuse in any aspect of insurance underwriting.  

Group insurers must demonstrate transparency, fairness and accuracy in their AI system's pricing to maintain customer trust and retention. 

See also: AI: The Future of Group Insurance

Lack of Available High-Quality Data 

AI algorithms need to use large data sets to work effectively. 

In group insurance, brokers or employers often provide carriers with RFP information, historical claims data and census data that can be cleaned by AI tools and sent to AI-powered underwriting platforms for automated quoting and rating. 

While this data is critical for AI underwriting solutions, employers typically manage the data related to group insurance policies, which can restrict carriers from accessing and ingesting more granular employee data (e.g., medical records, health screenings, past claims data, etc.) into their underwriting systems. 

Additionally, leveraging sensitive plan member health data comes with the risk of infringing on consumer privacy and potentially leaking information to other parties. 

A study by the Office of the Privacy Commissioner of Canada says 89% of Canadians are at least somewhat concerned about people using online information about them to steal their identity, including 48% who said they are highly concerned about identity theft. 

The E.U.'s General Data Protection Regulation (GDPR) is one of the world's most extensive data compliance regulations for the insurance industry. It is designed to harmonize data protection laws across the E.U. Insurers that are based in the European Union or that process the personal data of E.U. citizens need to comply with the GDPR. The U.K also has a GDPR post-Brexit with similar concerns.

The GDPR places substantial restrictions on processing special categories of sensitive data such as race, religion, sexual orientation, sensitive health information, etc. 

Carriers and vendors must comply with data privacy regulations, ensure the confidentiality of personal health information and be transparent to customers and regulators when using external data sources to increase premiums or deny coverage. 

The Fear of AI Replacing Humans 

Many insurance executives are concerned that AI systems will replace human workers. AI will result in cutbacks for some areas. For example, recent research by McKinsey suggests that 25% of the insurance industry is projected to be automated by AI and machine learning techniques by 2025. And according to a global survey by Rackspace, 62% of insurers have cut staff due to the implementation of AI technologies.  

Yet, as AI in group insurance sales and underwriting matures and carriers gain access to new data sources rather than being replaced, many roles will be upskilled and retuned to accommodate new technologies and new ways of working. Many underwriting tasks that AI can automate are mundane, time-consuming tasks, such as converting raw data from RFPs into structured formats and manual data entry. AI can help skilled underwriters focus on more urgent and important work. 

It is estimated that AI will increase labor productivity by about 37% by 2025 by eliminating or minimizing more manual tasks and freeing current workers to add more value. 

See also: The Risks of AI and Machine Learning

Best Practices for Integrating and Maintaining AI Systems in Group Insurance 

Clearly Define Long-Term Objectives Before Integration 

Employee benefits insurers that have at least dipped their toes into AI technologies tend to use them to address narrow topics rather than high-value problems. 

When carriers do not see sufficient returns on their AI investments, they may hesitate to dedicate enough money, time and attention to generate significant financial benefits. Short-term thinking and looking for quick wins do not give AI solutions adequate time to learn and prove their value. Instead, carriers should define one foothold problem within the value chain for an AI solution to solve or an opportunity to exploit.  

Identifying the Problem:

Understanding the specific pain points that plan members and employers encounter is essential. These problems might include difficulties in navigating the insurer's website, unanswered simple queries or the inaccessibility of contact information. Identifying these issues is the first step in determining whether AI-based chatbots are the right solution.

Choosing the Right Solution:

AI chatbots are more sophisticated but also more expensive and complex to develop and maintain. In cases where simpler solutions can resolve the issues, investing in AI might not be cost-effective.

Defined Business Objectives:

Many AI projects fail because they lack well-defined business objectives. It's vital to have a solid understanding of what you want to achieve with AI-based chatbots. Is it improving customer service, reducing costs or increasing sales? Knowing the purpose of AI in your business is critical to its success.

Managing Expectations:

AI implementation often requires patience. It's not a quick fix, and it may take time to fine-tune the chatbot for optimal performance. It's important for organizations to have realistic expectations and be willing to invest time and resources for AI to deliver the desired results.

Position for Success:

Insurers that start with well-defined business objectives, a clear understanding of the problems they aim to solve and a commitment to patiently see the project through are in a prime position to succeed. Success in AI implementation often hinges on a strategic, long-term approach.

In the insurance industry, AI chatbots can bring substantial benefits by improving customer service, automating routine tasks and increasing efficiency. However, success is contingent on careful planning, problem identification and aligning the technology with specific business needs and objectives.

See also: 3 Key Uses for Generative AI

Leverage New Data Sources  

Without comprehensive historical and real-time data about plan members and the business, group benefits AI systems, such as an underwriting platform, can struggle to accurately produce quotes and rates that reflect the group's risk, resulting in financial losses. 

As reflected in Majesco’s annual SMB customer survey report, many group L&H carriers are using new data sources for underwriting, including data from prescription drug purchases, fitness trackers and social media. 

Fitness devices can track daily steps, sleeping patterns, activity levels, heart rates, calories consumed, etc. Its data-tracking capabilities and consumers' desire to share such information for incentives make fitness devices one of the most promising new data sources for group insurance underwriting. 

58% of U.S. consumers own a fitness tracker or smartwatch, and 70% of customers are open to sharing essential data on their health and exercise habits in exchange for lower premiums.  

Insurers that can effectively capture new data sources for their AI underwriting models will be able to deliver more accurate quotes, rates and personalized policies faster than the competition. 

Use Synthetic and Internal Data 

Synthetic data is not a new concept, but it is becoming a valuable resource for training AI systems. According to a report by Gartner, 60% of all data used in the development of AI will be synthetic rather than real by 2024. 

Obtaining the right data is critical to training and maintaining robust AI solutions. However, collecting quality underwriting data from the real world has historically been complicated and time-consuming for group insurers. 

Synthetic data refers to artificially generated data made by generative machine learning algorithms and statistical models. Its ability to replicate the characteristics and signals of real genomic datasets while not exposing customer information creates various opportunities for health, life and group insurers. 

Anthem, a large health insurer, partnered with Google Cloud to generate massive amounts of artificially generated medical histories, patient medical records, healthcare claims and related medical data so Anthem could scale and improve its AI systems.  

In employee benefits, carriers could extract more value from their AI-powered underwriting systems by using similar artificially generated data. 

Group insurers can produce more accurate rates and quotes that reflect the complexity and variability of real-world industry operations and employee health risks by feeding algorithmic underwriting solutions with synthetic data about past healthcare claims, medical histories, employee turnover rates and supply chain disruptions. 

Of course, group insurers can't rely solely on synthetic data for underwriting. Real-world data, such as historical sales statistics, will always be valuable for automated sales and underwriting systems. 

Tomorrow Belongs to Those Who Embrace AI 

Insurers that embrace AI can gain a competitive edge by providing more personalized services, reducing costs, streamlining processes and enhancing productivity. As AI becomes more prevalent in the group insurance industry, we can expect to see more innovation and enhancements in how insurers operate.  

Insurers face a myriad of challenges in today’s environment, routinely returning less than their cost of capital. Rising labor costs, increases in customer expectations, additional regulation of financial markets and pressure from digital native insurtechs combine to give even the most calm executive stress.

Given the nature of the high-volume and data-rich business, many would suppose insurers would quickly turn to automation and AI to combat some of their challenges. However, to date, insurers have been hampered by the inability of traditional automation technologies like robotic process automation (RPA) to handle the complexity and variability of the industry.

Thankfully, with the recent rise of natural language processing (NLP) models (including large language models, or LLMs) insurers can now deploy automation widely across their value chain to reduce back-office and front-office costs. The changes boost employee productivity and happiness by enabling them to focus on what humans (and not computers) do best and improve customer experience by allocating more employee time to customer engagement and providing greater personalization to meet customers' needs. 

See also: The Risks of AI and Machine Learning

Why Haven’t Insurers Automated More?

Automation often has the highest return on investment on processes that contain: high volumes, lots of manual effort, multiple applications, tight service-level agreements (SLAs) and severe penalties for errors. By this definition, the insurance industry should be the perfect fit for automation and AI adoption, yet struggles to adopt on a large scale. Why?

Certainly, risk aversion can make insurers conservative. Yet, this fails to explain why insurance companies aren’t turning more to AI and automation to solve their challenges as other conservative industries (manufacturing, logistics, etc.) are widely using AI, especially in the back office.

Instead, the reason the insurance industry has not seen an explosion of AI implementation stems from the lack of capability in traditional automation systems like RPA. RPA requires:

1. Highly Standardized Processes
2. Exceptions to the Rule Anticipated Up Front
3. Large Workforce of Special RPA Developers
4. Constant Communication Between Developers and Subject Matter Experts (Like Claims Agents).

Unfortunately, these requirements often prevented many processes from being suitable for automation, especially processes like claims. A typical claim may contain a litany of different process “paths” based on the unique situation. In process terms, these are called “exceptions.”

Traditional automation is unable to understand the complexity of such situations or handle all the various edge cases that a human can easily handle. Adding to this problem, traditional automation requires hiring armies of “RPA developers,” highly paid individuals able to take a proposed process and translate it into the programming language (even if drag and drop) that automation can understand. Exceptions contained in most insurance processes, including customer onboarding, claims processing and underwriting, mean developers would be constantly required to try and program each and every one of the edge cases. In the past, it made more sense to simply outsource many of the processes to offshore centers.

This left insurers in a bind. Labor costs (including outsourcing) are rising faster than premiums. In addition, by outsourcing these processes insurers are unable to profit off the rich data created when such processes are digitally recorded. Thankfully, new AI and automation has arrived.

See also: Insurers Boosting Their Use of AI

A New Era: The Subject Matter Experts in Control

The rise of natural language processing and LLMs like ChatGPT have eliminated one of the biggest headaches with AI and automation: the need for developers. Instead, new automation systems are built with NLP at the core – making the building and management of even highly variable, complex automations accessible for the subject matter experts. Doing so eliminates many of the problems insurance processes have typically faced.

1. Even exception-heavy processes like claims can be automated

Subject matter experts like call-center agents, brokers and underwriters know their business well. If something unexpected occurs, more times than not they’ve encountered it before. Because automation is now all in English, new technologies make it easy for the subject matter experts to handle any exception simply by conversing with AI. Now, even complex processes with lots of variability in documents or rules (like insurance claims) can be automated, driving significant cost savings through employee reassignment or capacity creation for growing segments.

2. The deep industry knowledge and experience of an aging workforce can be captured as a rich data source.

In today’s data-driven economy, knowledge of a business’s processes can yield significant efficiency gains in process re-design and improvement. However, because of the vast quantity of scenarios and incidents that can occur in the coverage of insurance businesses, capturing a view into the processes can be too expensive relative to the ROI. However, with NLP-based automation, the automated activities (and the SME’s responses to exceptions) are recorded in English in the form of a “business journal.” This data represents a rich treasure-trove of data on which prompt based queries can be run using LLMs. Executives can now gain on-demand answers to core questions about their business, notice trends and make more effective decisions. It’s precisely here where legacy insurance providers have an advantage relative to entry insurtech players. The volume of processes, if captured properly, can provide far more market data at scale than new players have, providing essentially key market research.

3. Front-office employees can spend more time focused on customer relationships.

Customer service scores continue to decrease in the insurance industry across sectors. This decline stems from frustration in claim creation, delays to claim resolution and a feeling of the customer “just being a number.” Now, NLP-based automation technologies solve these problems. In a recent example, one contact center saw a decrease in average handle time (AHT) from 513 seconds to 209 seconds by automating the account lookup, note taking (from voice to text) and primary actions taken. With this sort of automation, insurers can enable their people to genuinely focus on their customers, develop trust, provide personalized care and improve both employee and customer experience alike.

The new era of NLP based automation provides a rich opportunity for an industry struggling to contain costs, improve customer experience and fight off new competition. Traditional limitations to legacy automation systems like RPA are unlocked by NLP-based automation. Now, automation and AI are accessible to insurance subject matter experts, boosting productivity and allowing for even highly variable, complex processes to be automated.

As more is automated, variable costs will decrease significantly in this high-volume industry, data relative to the working and market will be captured efficiently and a better customer experience will drive retention, cross-sell and up-sell opportunities.

KEY TAKEAWAYS:

--Allianz Commercial analysis of large cyber losses shows the number of cases in which data is exfiltrated is increasing every year – doubling from 40% in 2019 to almost 80% in 2022, with 2023 significantly higher.

--The frequency of cyber claims will increase about 25% this year.

--Ransomware activity alone was up 50% year-on-year during the first half of 2023. So-called Ransomware-as-a-Service (RaaS) kits, where prices start from as little as $40, remain a key driver. Ransomware gangs are also carrying out more attacks faster, with the average number of days taken to execute one falling from around 60 days in 2019 to four.

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Following two years of high but stable losses, 2023 has seen a worrying resurgence in ransomware and extortion claims as the cyber threat landscape continues to evolve, Allianz Commercial warns in a new report.

Hackers are increasingly targeting IT and physical supply chains, launching mass cyber-attacks and finding new ways to extort money from companies, large and small. Most ransomware attacks now involve the theft of personal or sensitive commercial data for the purpose of extortion, increasing the cost and complexity of incidents, as well as bringing greater potential for reputational damage. Allianz Commercial analysis of large cyber losses shows the number of cases in which data is exfiltrated is increasing every year – doubling from 40% in 2019 to almost 80% in 2022, with 2023 significantly higher.

Cyber claims frequency has picked up again this year as ransomware groups continue to evolve their tactics. Based on claims activity during the first half of 2023, we expect to see around a 25% increase in the number of claims annually by year-end. The attackers are back, and focused again on Western economies, with more powerful tools, enhanced processes and attack mechanisms. Given this dynamic, a well-protected company is necessary to stand up to the threat, and, increasingly, the most important element of this is developing strong detection and fast response capabilities.

See also: Role of Ransomware in Cyber Insurance

How is ransomware risk evolving?

According to the Allianz Commercial report, "Cyber security trends 2023: The latest threats and risk mitigation best practice – before, during and after a hack," the frequency of cyber claims stabilized in 2022, reflecting improved cyber security and risk management actions among insured companies. Law enforcement agencies targeting gangs, together with the Ukraine-Russia conflict, also helped curtail ransomware.  However, ransomware activity alone was up 50% year-on-year during the first half of 2023. So-called Ransomware-as-a-Service (RaaS) kits, where prices start from as little as $40, remain a key driver in the frequency of attacks. Ransomware gangs are also carrying out more attacks faster, with the average number of days taken to execute one falling from around 60 days in 2019 to four.

Data exfiltration can significantly add to the cost of a loss or cyber claim. Such incidents can take longer to resolve, while legal and IT forensics can be extremely expensive. If data has been stolen, companies must know exactly what data has been exfiltrated and will likely have to notify customers, who could seek to claim compensation or threaten litigation.

This year has also seen several large mass ransomware attacks as threat actors used exploits in software and weaknesses in IT supply chains to target multiple companies. For example, the MOVEit mass cyber-attack, which exploited a data transfer software product, affecting millions of individuals and thousands of companies, contributed to the increase in the frequency of claims in 2023 to date, affecting multiple policyholders simultaneously.

Growing number of public cases

In the past, the number of cyber incidents that became public knowledge was low. Today, hackers threaten to publish stolen data online. Allianz Commercial analysis of large cyber losses (€1mn+) shows that the proportion of cases becoming public increased from around 60% in 2019 to 85% in 2022, with 2023 set to be even higher. 

With potentially costly financial and reputational consequences, companies may feel under more pressure to pay ransoms where data has been stolen. The number of companies paying a ransom has increased year-on-year – from just 10% in 2019 to 54% in 2022, again based on analysis of large losses only (€1mn+). Companies are two-and-a half times more likely to pay a ransom if data is exfiltrated, on top of the encryption.

However, paying a ransom for exfiltrated data does not necessarily resolve the issue. The company may still face third party litigation for the breach of data, especially in the U.S. Indeed, there are few cases where a company should believe that there is no other solution other than paying the ransom to be able to re-access its systems or data. Any impacted party should always inform and cooperate with the authorities.

See also: Tackling the Surge in Cyber Premiums

The importance of early detection and fast response

Protecting an organization against intrusion remains a cat-and-mouse game, in which cyber criminals have the advantage. Allianz analysis of more than 3,000 cyber claims over the past five years shows that external manipulation of systems is the cause of more than 80% of all incidents. Threat actors are now exploring ways to use artificial intelligence (AI) to automate and accelerate attacks, creating more effective AI-powered malware, phishing and voice simulation. Combined with the explosion in connected mobile devices – Allianz Commercial has seen a growing number of incidents caused by poor cyber security in this area – attack avenues only look likely to increase.

Preventing a cyber-attack is therefore becoming harder and the stakes higher. As a result, early detection and response capabilities and tools are becoming ever more important. Around 90% of incidents are contained early. However, if an attack is not stopped in the early stages the chances of preventing it becoming something much more serious and costly greatly reduce.

Companies should direct additional cyber security spending on detection and response, rather than just adding more layers to protection and prevention. Only one-third of companies discover a data breach through their own security teams. However, early detection technology is readily available and effective.  Cyber breaches that are not detected and contained early can be as much as 1,000 times more expensive than those that are, with Allianz Commercial analysis showing that early detection and response can stop a €20,000 loss turning into a €20mn one.

The buzz surrounding generative AI has permeated every industry, and the insurance sector is no exception. With capabilities such as creating human-like responses and generating insightful outputs, generative AI technologies have brought forth a unique blend of opportunities and challenges for organizations.

From streamlining various business operations, such as content creation, to spurring debates about its potential impact on employment, generative AI has firmly embedded itself in the strategic discourse. Paul Carroll, editor at Insurance Thought Leadership, emphasized in his May 2023 commentary, “Generative AI: Coming Faster Than You Think,” that a balanced perspective on the short-term and long-term implications of these technologies is vital for shaping future business paradigms in the insurance industry. He writes, “The rule of thumb about breakthrough technologies is that they’re overestimated in the short term but underestimated in the long term.”

Understanding generative AI necessitates a look beyond its surface-level capabilities. It is pivotal to comprehend that these models do not “think” autonomously; rather, their outputs mirror the quality of their training data and the effectiveness of human-generated prompts. Therefore, there is and will be a constant need for a human-machine loop to exist and work together.

See also: 5 Ways Generative AI Will Transform Claims

Harnessing the Power of Language Models in the Insurance Industry

Generative AI, particularly through large language models (LLMs), can generate coherent and seemingly intuitive responses to user queries. This is achieved by training on vast datasets, understanding grammatical structures and learning word sequences, which enable the AI to predict forthcoming words in a sentence and familiarize itself with industry-specific terminologies.

Generative AI introduces transformative capabilities to the insurance industry, offering innovative solutions across various verticals including customer service, risk management, product development, claim processing and marketing. It enables the creation of sophisticated, personalized customer experiences through intelligent communication. 

This enhanced responsiveness to human input is widely considered to be one of the most significant advantages of generative AI. Rather than reinventing the wheel each time a new communication or document is needed, these technologies can draw from an insurer’s entire library of archived communications in a matter of seconds, generating a strong initial draft that can then be refined by a human author.

Despite the myriad applications, it is imperative to navigate the implementation of generative AI with a keen emphasis on ethical use, regulatory adherence and data security.

Enhancing Customer Communications Through Sentiment and Readability Adjustments

To the general consumer, selecting the right insurance product can be confusing and stressful. Insurers must take great care with their communications to ensure they are easily understood. In times of great distress, even more care needs to be taken. When a denial of claim or coverage is required, using distinctly negative language, such as the words “no,” “illness,” “poverty” and “death,” have been shown to release stress hormones in the brain of the person reading it. These stress chemicals impair judgment and can reduce reading comprehension by up to four school grade levels, compounding the challenges faced by consumers when communications are not clear. Generative AI can step in to support the rewriting of content in plain language or to align to a targeted reading level, which is important given that half the U.S. population reads at or below an eighth-grade reading level.

Generative AI, by leveraging natural language processing (NLP) for sentiment analysis, can help adjust the emotional tone of communications, ensuring that even negative messages are conveyed in a manner that will be received as well as possible.

See also: 3 Key Uses for Generative AI

Amplifying Business Needs With AI: The Imperative of Choosing the Right Partner

Navigating through the intricate pathways of generative AI, particularly in the context of the highly regulated and nuanced insurance sector, brings forth several challenges that can be mitigated by aligning with a skilled technology partner. The deployment and management of generative AI entail not only a deep understanding of the technology itself but also an awareness of the regulatory, ethical and data security nuances associated with its application in specific use cases within the insurance domain. A proficient AI technology partner, with a wealth of expertise and experience across varied implementations, can deftly navigate through these complexities, ensuring the AI applications are compliant, secure and effectively tailored to meet specific organizational objectives.

Moreover, the insurance landscape is characterized by dynamic shifts influenced by regulatory changes, market trends and evolving customer expectations. Engaging with an adept AI partner ensures the generative AI applications are not only attuned to the current needs of the organization but are also scalable and adaptable to accommodate future evolution.

The partner can facilitate continuous learning and adaptation of the AI models, ensuring they evolve in tandem with shifting trends and regulatory norms, thereby securing a sustainable and forward-looking AI strategy. Implementing AI is not a one-off project, but a continuous journey of learning and adaptation. 

The World Economic Forum estimates that by 2025 approximately 463 exabytes of data will be produced every day. To put that number in context: All the words ever spoken in human history can fit into FIVE exabytes.

Data is powering disruptive business models that account for sizable portions of our modern economies.  

Data as we know it has gone through a number of transformations since the computer era began. Between 1969 and 2000, an era we'll call Data 1.0, data was largely confined to specific applications, such as online booking systems and payroll automation. The era of Data 2.0 arrived when business leaders realized that by sharing information across the organization, workers could be empowered to make more-informed decisions. This era sparked advancement in analytics and data warehousing and the rise of new methods, such as master data management (MDM).  ‍

The advent of social media and cloud computing and the explosion of mobile has now given rise to Data 3.0. With more data being produced than ever before, digital transformation is rapidly creating the businesses of the future.  

With 4.8 billion (yes, billion with a “b”) internet users, people are generating data every minute of every day. But it’s not just us as individuals who are creating all this new data. The rise of Internet of Things (IoT) devices, which are able to connect to the cloud and share data with other devices, will mean connected humans will be massively outnumbered by connected machines, with some predicting 75 billion connected devices by 2025.  

‍Disruptive businesses, such as Airbnb, Amazon and Uber, harness data and use it as the very architecture of the services provided. If traditional sectors, like insurance and banking, hope to keep up, it is imperative to find ways of leveraging the available data to deliver a better experience, a better product or even a whole new way of doing business. 

See also: Why Becoming Data-Driven Is Crucial

You Can’t Use It if You Can’t Find It (Share It, or Trust It)

The growing reliance on data has taken the conversation about access and management out of IT and placed it firmly in the boardroom. CEOs now realize data management is as critical to future success as finance and talent management. Most importantly, leaders now understand that effective decision-making is based on accurate data.  

Unfortunately, many organizations face a real challenge, with data trapped in separate systems and silos, both internally and off-site, and in a variety of forms. A company may be generating terabytes of useful information, but it can’t be analyzed if it can’t be located. Data fragmentation has become one of the biggest operational challenges facing companies looking to accelerate digital transformation.  

‍More than just preventing good internal decision-making, fragmented or poorly managed data can affect how a business interacts with partners and within ecosystems. In a recent survey, Gartner found that 65% of decisions made are now more complex than just two years ago and involve more stakeholders than ever before. Gartner says that decisions need to “...become more connected and that sharing of data and insights across organizational boundaries is critical.” 

In fact, Gartner predicts that in 2023, organizations promoting data sharing will outperform peers on most business value metrics. However, this compelling opportunity seems out of reach for most companies because, according to Gartner research, less than 5% of data-sharing programs can currently correctly identify trusted data and locate trusted data sources.  

See also: Data Mesh: What It Is and Why It Matters

Taking Back the Power 

When it comes to operationalizing the many benefits of Data 3.0, leaders need to first look at ways to regain control of existing or internal data. This will help optimize decision-making processes, as well as maximize the many benefits of working more closely with ecosystem partners. ‍

Findings of the Dresner Advisory Associates’ 2020 Data Pipelines Market Study published in Forbes show that over 80% of enterprise business operations leaders say data integration is critical to operations. What’s more, 65% of organizations prefer to deploy data integration solutions from cloud platforms or hybrid cloud.  

Modern hybrid integration platforms (HIPs) offer complex data processing with simplified usage and are simpler, faster and more powerful than traditional legacy-bound data solutions. HIPs can also be customized to meet specific goals, to reduce the need for businesses to invest in highly skilled developers and tech support teams by simplifying multiple data management functions and to operationalize data for other systems to use while continuing to advance the company’s broader digital transformation agenda.

There is no doubt that managing data is swiftly becoming one of the most important jobs in every business. There is already an extreme shortage of data scientists across industries, and the gap is growing every day.

With every human producing an average of 1.7MB of data every second, not having the right data solutions in place could be the most expensive mistake a business can make. HIPs can rapidly automate data and create a unified system that allows for the complete transformation of communication, ensuring the ability to get the right data to the right person at the right time to take advantage of critical insights and opportunities.

The property and casualty business is constantly evolving, with occasional seismic shifts that can transform the market – and require quick adaptation. The proposed insurance reforms in California are a prime example of such a transformative moment.

The proposed reforms could lead to much brighter days ahead for both insurers and California consumers. To navigate these changes and make the most of the opportunities presented, P&C insurers must review and adjust their strategies in the market.

This article explores three actions insurers can take to excel in this shifting landscape.

See also: Growing Number of Uninsurable Risks

Write the Lowest-Risk Properties in "At-Risk" Areas

Many carriers have likely put forward new or revised rate filings compliant with the April 2023 deadline of Regulation 2644.9 – and are awaiting California Department of Insurance (CDI) approval of their filing. Until these approvals are received, some carriers may hesitate to write in “at-risk” areas.

That may be an overly cautious approach. 

The reforms proposed would require admitted carriers to write policies in the wildfire-prone parts of the state – and do so for at least 85% of their statewide market share. For example, if a company provides 10% of policies across California, they would be required to provide 8.5% of the coverage in "at-risk" areas.

We expect smart insurers to race to write policies for the lowest-risk properties in these high-risk areas.

Why is this critical?  According to HazardHub data, approximately 25% of properties in the CDI-defined "at-risk" areas are likely profitable with current rates.

Insurers don't have to wait for their last filings to be approved before acting. Identifying these lower-risk properties in "at-risk" areas can give insurers a competitive edge while demonstrating goodwill through early compliance.

Acquire Advanced Analytics

The second action is understanding the importance of high-resolution analytics compared with the existing low-resolution analytics used by most insurers.

Traditional analytic approaches may no longer suffice as they are often (a) insufficiently granular, (b) rely solely on backward-looking data and (c) consider too few variables to assess and differentiate wildfire risk. They also tend to lump all properties into the same risk level across large census blocks or ZIP code regions.

Insurers that quickly embrace more sophisticated approaches will be able to differentiate risk using considerably more variables – and do so at the specific property parcel level instead of the census or ZIP code level.

Insurers that leap forward with advanced analytics will be able to accurately identify, price and manage risk at a granular level, giving them a significant competitive advantage.

See also: Data-Driven Transformation

Reset Strategy and Refile Rate Plans

The third action revolves around resetting strategy and reevaluating rate plans. This will be necessary to align with the proposed reforms and the changing market dynamics. Some steps insurers may take here include:

·         Assessing Current Market Share in the At-Risk Area: Evaluate existing market presence in California's "at-risk" areas, understanding the proportion of policies held in high-risk regions.

·         Defining Risk Appetite: Reexamine and define their risk appetite, recognizing that insurers will be able to charge premiums commensurate with risk.

·         Realigning Marketing Strategies: Adjust marketing strategies to effectively reach and serve homeowners in the state-defined at-risk wildfire regions.

·         Differentiating Offerings for Competitive Edge: In a market where not every insurer can compete solely on price, explore ways to provide added value, such as speed, ease of service and innovative coverage options. This differentiation will attract customers and help insurers meet their "at-risk" market share target.

As California moves forward with these proposed insurance reforms, it is clear that the market is entering a transformative chapter. Insurers must strategically adapt to the changes to excel in this evolving landscape. It is imperative for them to evaluate their market presence, redefine their risk appetite, revamp marketing strategies and explore avenues for differentiation. Embracing state-of-the-art risk analytics is essential to their success, equipping them with the capabilities needed to precisely identify, evaluate and price risk

The Equal Ventures Insurance Index is a quarterly summary of insurance equity performance and trends. We’re back to reflect on the insurance/insurtech ecosystem in Q3, which turned out to be a fairly meaningful quarter in terms of both price action and industry news.

Q3 Headline Summary

• In Q2, we noted that insurtech carriers rebounded and outperformed; this trend reversed sharply over Q3, as the insurtech carriers in our index were hit by rising loss ratios and the resulting deceleration in growth.
• Digital brokers once again underperformed their legacy peers, most likely dragged lower as the broader market traded sideways, signaling risk-off for most of small-cap tech.
• YTD, insurtechs and digital challengers have outpaced their legacy comparables, but the spread in YTD performance narrowed significantly in Q3 (so much for a quick path to valuation recovery…).
• Legacy brokers and P&C insurers were, on average, flat over the quarter, mirroring the broader market (and also masking larger continuing trends that we discuss in greater detail below).

 

Takeaways from Q3 Performance

Brokers continue to face a favorable environment as rates trend higher. The legacy names were flat on average in an otherwise choppy market. The index was dragged down by WTW, which reported lower margin and reduced guidance in Q2 earnings in part due to increased investments in talent — pointing to a broader trend in talent management that affects the insurance industry. On the digital challenger side, GSHD enjoyed another quarter of massive outperformance compared with the digital distribution peers we track it against and expanded its already outsized EPS and EBITDA multiples. The lead-gen and digital acquisition companies, on the other front, continued to underperform and for the most part traded sharply lower.

In the P&C carrier segment, observations from Q3 performance highlight a number of powerful and secular trends affecting the industry– chief among those trends is the extent to which property loss and cat exposure is pressuring margins and spooking investors.

Performance of the volatile insurtech carriers demonstrates this trend. LMND started the quarter strong, after catching a bid from its “synthetic agents” initiative as an opportunity to improve cash efficiency and accelerate growth. But the stock fell off a cliff (and hit all-time lows) after posting higher loss ratios driven by cat exposure in its homeowners segment and announcing reduced reinsurance coverage. As a result, the company announced it would slow growth while waiting out new rate filings/increases. This is likely the responsible thing to do but not at all what investors were looking for. The chart below shows the magnitude of the homeowners losses before/after cat losses.

See also: Lemonade's 'Synthetic Agent' Nonsense

HIPO performance tells a similar, if not exaggerated, story about destabilizing cat risks. HIPO reported elevated cat losses attributed to TX hail, and like LMND, made a commitment to slowing growth while rates increase. But shortly after earnings, HIPO abruptly announced an immediate and unprecedented pause of all new business while they “evaluate catastrophic risks, geographic diversification and enhanced underwriting…” Unsurprisingly, their stock fell more than 50% over Q3.

Importantly, the trend toward higher property cat losses was not limited to the volatile insurtechs. National carriers continued to exit the CA and FL markets, a trend that started making headlines in Q2. Alarmingly, a greater share of homeowners are forgoing insurance due to cost or accessibility, and there is an increasing reliance on public insurers of last resort (a trend aptly described by a CA insurance industry group last month as a “slow motion train wreck”).

But even outside of these hardest and most disrupted markets, carriers are grappling with increased nat cat event frequency and higher exposure. In the wake of the unstable and disorderly reinsurance renewals market earlier this year, carriers landed with less reinsurance coverage overall (and therefore more exposure to cat risks). At the same time, more conservative reinsurance contracts left carriers with higher retentions and more exclusions to unnamed storms and secondary perils. 2023 turned out to be a particularly active year for severe convective storms — exactly the type of unnamed, moderate-frequency events that carriers are more exposed to due to reinsurance constraints (and exactly the type of non-coastal property losses that HIPO and LMND referenced). Losses from named storms, on the other hand, for which reinsurers bear more risk, have (so far) been relatively subdued. As a result primary carriers faced losses in Q2 as much as 40% above normal, whereas reinsurers effectively enjoyed a windfall from higher rates and below-average loss. This explains both the volatility across P&C carriers in the index, as well as the outperformance of reinsurers.

See also: Insurtech Startups Are Doing It Again!

Beyond property insurance, in a market with reduced access to reinsurance and continuing claims inflation, specialty insurers and brokers continue to take share from the admitted market. Commercial carriers with outsized E&S lines continue to outperform carriers in admitted segments. KNSL, a pure-play E&S carrier (and therefore not part of our index) trades at >2.5x the earnings multiple of our legacy carrier index. As we’ve written about before, it’s up ~60% YTD through Q3 and points to a long-term shift toward specialty risk and niche commercial underwriting expertise. Commercial rates continued to increase in Q3, though the pace of rate growth decelerated sequentially, possibly pointing to more stability for carriers in the quarters ahead.

As a reminder, the purpose of this brief (and oversimplified) analysis is to share high-level trends for insurtech investors, and not to comment definitively on valuations or market expectations. That said, we believe it will be instructive to see how insurance equities fare in Q4, and we note that insurtech carriers appear to be struggling from similar macro trends that are affecting the P&C industry at large. Whatever happens, it is clear that controlling property losses will continue to take on increasing importance for both legacy carriers and digital challengers.