The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or positions of Legal & General America or of its parent company, Legal & General Group.

As Yogi Berra (and others) have said, “Making predictions is hard, especially about the future.” But as a leadership team we at Legal & General America (LGA) make predictions. Specifically, we carefully track external trends and distill them into a set of predictions, which then inform our recommendations.

For 2024, here are my personal top five predictions, and five recommendations for the U.S. life insurance market. 

See also: Insurance in 2030: What Does the Future Hold?

Predictions

1. 2024 will be the year that the wellbeing of the 65-plus population becomes a mainstream topic of conversation for their children 

A spike in long-term care needs, driven by pandemic deferred medical check-ups, will help adult children realize their parents have gotten older and may be ill-prepared to handle all of the financial, administrative and emotional needs.

2. GLP1 – analogs will be partially subsidized by a life insurer 

In 2024, a reinsurer or actuarial consulting firm will publish a plausible protective value paper that shows the benefits at point of underwriting for long-term use of diabetes medication for weight loss. Expect a carrier with a large block of high net worth whole life to roll out a discount on prescriptions to follow.

3. The first life insurance policy will be underwritten by a large language model (OpenAI’s ChatGPT, and Google’s PaLM) - leading to a consulting arms race

We will see the public announcement of a generative AI “co-pilot” analyzing the risk of life insurance applicants in conjunction with a human underwriter. That will trigger a tidal wave of FOMO, pushing board rooms around the U.S. to hire consulting firms and system integrators to rush out conversational AI projects in claims, customer service and new business.

4. US cash-value products will continue to shrink

Indexed universal life (IUL) insurance products, which until last year rode a wave of premium financing, will continue to face major headwinds in 2024. Combine that with annuity 1035 focused distributors, and these products could be a tough sell in this economy for nervous customers – who may be consciously conserving cashflow, e.g. increasingly switching from annual modes to monthly. 

5. Digitized underwriting will accelerate – helping make term life insurance more profitable 

LGA and a few other companies have rolled out digital underwriting processes that drastically simplify the case management burdens for distributors – essentially making it zero touch, almost 50% of the time. Fast-moving distributors will move to consolidate their term life insurance business with companies that make it increasingly profitable for them to help their advisers and customers get affordable protection.

See also: Glimmers of Good News on Climate (Finally)

Recommendations

1. Travel to see that prospect, client, partner 

The rich information exchange, high fidelity and focused attention of face-to-face business meetings trump all the advantages of Teams, Zoom, etc. .

2. Exploit generative AI through feature-rich software

While deploying a LLM is likely too large a lift for non-software engineers, everyone can easily dip their toes in the generative AI waters by using a tool like Notion or Adobe Firefly. 

3. Develop a customer engagement strategy 

Marketing content will become so affordable, and ubiquitous, that what really matters will be customer activation, and engagement with your content, driving tangible actions from your efforts.

4. Encourage your clients to come up with a plan for their parents (and in-laws)

From what to do with the dining room table, to how to fund long-term care, embrace that awkward talk with your siblings, spouse and parents about their wishes.

5. Promote the value of professional advice 

Given a flood of AI-generated op-eds and social media echo chambers, search engine research now has marginal value. Help your clients see the importance of consulting with an independent life insurance broker, estate attorney or registered investment adviser to get personalized expert counsel.

Now that I’m finished predicting the industry’s biggest impacts for the year, I’m off to play the Powerball.

 

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In the auto insurance industry, automation is no longer a distant dream. Automation is already integrated into the way insurance companies assess risks, process claims, and engage with customers. In fact, 87% of insurance providers invest over $5 million into automation and AI technology annually.

Beyond the adoption of new technologies, this evolution represents a complete overhaul of the insurance process. With automation, insurers are not only streamlining operations, they’re setting new benchmarks for efficiency, accuracy and personalized service. 

With such a substantial investment in automation and AI, the question arises: How are these technologies transforming the landscape of auto insurance? In the following sections, we’ll explore the innovations taking hold in this evolving industry.

Current Use of Automation

Automation in auto insurance has moved from a futuristic vision to a practical, everyday tool, reshaping how companies assess risks, process claims and interact with customers. The current use of automation is not just about adopting new technologies; it's about reimagining and streamlining every facet of the insurance process.

From underwriting to customer service to claims processing, automation is setting a new standard for efficiency, accuracy and personalized service, reflecting a major shift in how insurers operate and engage with their policyholders.

Below, we discuss each of these innovations in more depth, highlighting how automation is being leveraged to enhance the insurance experience.

See also: Does Generative AI Kill Process Outsourcing?

Automation in Claims Processing

The shift toward automating claims processing marks a significant leap forward in operational efficiency. AI-driven systems are now adept at evaluating claims' validity, automating damage assessment through digital images and videos and swiftly initiating payouts. This speeds up the process from claim submission to resolution and drastically reduces human error and intervention.

A notable example includes CCC Intelligent Solutions, a software-as-a-service (SaaS) platform for the property and casualty insurance industry. The company increased its use of advanced AI for auto claims processing by 60% year over year in 2023. This advancement allows quicker damage evaluation through photos, determining the repair cost and whether a car is a total loss or repairable, significantly reducing the time to resolve claims.​

Automation in Underwriting

In underwriting, the use of AI and machine learning to analyze extensive datasets – ranging from driving records to real-time telematics data – has revolutionized risk assessment. This deep analysis allows for a more accurate prediction of risk levels, facilitating the creation of nuanced and personalized policy pricing.

Liberty Mutual has collaborated with Jupiter, an insurtech that offers weather and climate analytics, to enhance its risk management capabilities for commercial insurance clients. By integrating Jupiter's sophisticated climate data and predictive analytics into its risk assessment processes, Liberty Mutual can now offer a more nuanced and dynamic approach to evaluating risk.

This collaboration enables the insurer to precisely analyze potential weather-related risks to businesses, from flood and storm damage to other climate change impacts. As a result, Liberty Mutual is better equipped to tailor insurance solutions, offering more accurate and personalized pricing.

Automation in Customer Service

The deployment of AI-powered chatbots and virtual assistants for customer service has introduced a new dimension of customer interaction. These tools offer 24/7 support, efficiently handle inquiries, provide policy recommendations and even facilitate the initiation of claims. The result is a seamless and highly responsive customer service experience that meets the modern consumer's expectations for immediacy and convenience.

For instance, Lemonade has significantly improved operational efficiency and customer engagement by leveraging AI chatbots and machine learning models. Their AI chatbot, Jim, stands out for its impressive track record, autonomously managing interactions and processing claims at a speed that traditional methods can't match.

In fact, Jim has made headlines by setting a world record for settling a legitimate insurance claim in an astonishing two seconds, showcasing its extraordinary efficiency and the sophisticated algorithms that underpin its decision-making capabilities.

This innovation showcases how automation is refining the claims process and elevating customer service standards, establishing a new industry benchmark to address the dynamic needs of policyholders.

See also: AI's Role in Commercial Underwriting

Benefits and Challenges of Automation in Auto Insurance

The adoption of automation in the auto insurance industry brings a host of benefits, streamlining operations, enhancing customer experiences and refining risk assessment processes.

Yet the sector faces several challenges. These require careful navigation to ensure that the potential of automation is fully realized without compromising the integrity or inclusiveness of insurance services. 

Benefits of Automation in Auto Insurance

• Enhanced Efficiency and Productivity: Automation significantly reduces the time required for underwriting and claims processing. By leveraging AI and machine learning, insurers can analyze vast datasets quickly, identify patterns and make informed decisions with greater speed. This reduction in manual tasks frees staff to focus on more complex, value-adding activities.
• Improved Customer Experience: Digital platforms and AI-driven chatbots offer policyholders 24/7 access to services, from obtaining quotes to filing claims. This immediacy and convenience boost customer satisfaction, as policyholders no longer need to navigate time-consuming call centers or paperwork.
• Advanced Risk Assessment: Automation enables the use of telematics and real-time data analytics for personalized risk assessment. By monitoring driving behavior directly, insurers can tailor premiums more accurately to the individual's risk profile.
• Fraud Detection and Prevention: Sophisticated algorithms can analyze claims and identify patterns indicative of fraud, saving time and money by ensuring fair premiums. 

Challenges of Automation in Auto Insurance

• Data Privacy and Security: The collection and analysis of vast amounts of personal data raise significant privacy concerns. Insurers must navigate stringent data protection regulations and ensure robust cybersecurity measures to protect sensitive information from breaches.
• Regulatory Compliance: The fast pace of technological advancement in automation and AI can outstrip existing regulatory frameworks. Insurers must continually monitor and adapt to new regulations designed to ensure the ethical use of AI and consumer protections.
• Customer Trust and Transparency: While automation offers efficiency, the impersonal nature of AI interactions can affect customer trust. Insurers need to find the right balance between automated services and human interaction, ensuring transparency in how AI decisions are made, particularly in claims denials and premium adjustments.
• Technological Integration and Upkeep: Integrating new technologies with existing systems can be complex and costly. Additionally, continuous investment is needed to update and maintain these systems, ensuring they remain secure against cyber threats and effective against evolving fraud tactics.
• Workforce Transformation: As automation changes the nature of work in the insurance industry, there is a pressing need for reskilling and upskilling employees. Insurers must invest in training programs to equip their workforce with the necessary skills to operate new technologies and focus on more strategic, analytical tasks.

Emerging Technologies

Emerging technologies are revolutionizing the auto insurance sector, offering unprecedented opportunities for innovation and efficiency. At the forefront, AI and machine learning (ML) are leading this transformation, each bringing distinct advantages:

AI in Auto Insurance

The advent of AI in auto insurance marks a pivotal shift toward more personalized, efficient and secure services for policyholders, underscoring the role of AI in finance in transforming the industry's approach to risk assessment, customer service and claims processing. By integrating AI technologies–ranging from predictive analytics for risk assessment to machine vision for damage analysis–insurers are not only streamlining operational processes but are also significantly enhancing the benefits delivered to policyholders.

In fraud detection, Verisk leverages AI to refine fraud detection strategies. By analyzing both structured and unstructured data, including images and text, Verisk's AI tools can detect suspicious patterns and behaviors that signal fraudulent claims. This not only significantly improves the efficiency and accuracy of fraud analytics but also protects policyholders from the indirect costs of fraud, such as higher premiums.

Moving from fraud detection to the speed and transparency of claims processing, the application of machine vision technology by Ant Financial's "Ding Sun Bao" represents a leap toward enhancing policyholder satisfaction.

Using machine vision technology, this application compares images of vehicle damage against a comprehensive database of damage levels and associated repair costs. It automates the assessment and reporting process.

Ant Financial's AI has showcased remarkable efficiency in claims processing, outperforming human adjusters by processing claims in just six seconds compared with the human average of nearly seven minutes. 

Similarly, Tractable's AI software automates the claims process through machine vision. By assessing damage through images and benchmarking them against a vast database, it provides immediate repair cost estimates. This not only streamlines the claims process but also offers a clear, immediate understanding of potential costs to insurers and policyholders alike, contributing to a smoother, more efficient claims experience.

Finally, Progressive's AI chatbot "Flo" exemplifies another dimension of AI's impact–enhancing customer service. Flo leverages natural language processing to provide instant responses to policy-related queries and claims support. This direct, immediate communication channel reflects the broader benefits AI brings to policyholders: enhanced accessibility, personalized interaction and swift service delivery.

See also: How Automation Can Address Today’s Growing Underwriting Challenge

Machine Learning

In addition to AI's transformative impact, ML technologies further refine the insurance landscape, particularly in the realm of personalized pricing models. ML's ability to leverage telematics data allows for a more detailed analysis of individual driving behaviors, ensuring that premiums more accurately reflect a driver's risk profile.

This analysis allows for the creation of Usage-Based Insurance (UBI) models, such as Pay-As-You-Drive (PAYD) and Pay-How-You-Drive (PHYD). These models are pivotal in ensuring that premiums accurately mirror the policyholder's risk profile, directly linking insurance costs to safer driving practices. The benefit here is twofold: Policyholders can potentially see lower premiums through safer driving, and insurers can foster a safer driving culture.

Ant Financial's "Auto Insurance Points" system analyzes traditional and non-traditional data points–ranging from driving behaviors to spending habits–to assign a risk score to policyholders.

This risk score is used for personalized pricing, allowing insurers to offer rates that align closely with the individual's actual risk level. This method ensures fairness in pricing and empowers policyholders to directly influence their insurance costs through their driving behaviors.

For policyholders, the ability to compare car insurance quotes becomes even more valuable in this context. With insurers increasingly adopting AI and ML technologies, policyholders can more effectively assess which policies offer the best value based on their personal driving habits and risk profiles. This comparison not only aids in finding competitive pricing but also in identifying insurance offerings that reward safer driving practices.

Future Potential of Automation in Insurance

The future of automation in auto insurance is being significantly shaped by the advent of generative AI (GenAI), which enables end-to-end claims process automation. By leveraging GenAI tools, insurance companies can automate the evaluation of claims based on uploaded images of vehicle damage, streamlining the settlement process.

Simplifai has launched a generative AI tool, InsuranceGPT, which is the first large language model (LLM) specifically trained on insurance-related information, including policies, claims and customer service interactions. InsuranceGPT aims to improve the way insurance companies interact with their customers, ensuring that responses are not only quick but also accurate and highly relevant to the customers' needs.

InsuranceGPT's deep understanding of insurance terminologies and policies enables it to handle complex inquiries with ease, offering personalized advice and streamlining the claims process by identifying discrepancies in claims submissions and initiating procedures autonomously, significantly improving response times and operational efficiency.

Verisk also recently introduced an innovative, generative AI tool to expedite insurance claim processing within its Discovery Navigator platform. This tool automates the summarization of medical records for property and casualty claims, significantly reducing the time needed for claims handlers to review records. It promises up to 90% faster processing than manual methods, with up to 95% accuracy, enhancing efficiency.

CorVel has also launched a generative AI initiative through its Care MC claims platform, aiming to redefine claims and case management. This system automates tasks, such as summarizing medical documents and extracting key information, streamlining the claims process and allowing adjusters to focus more on direct interactions with claimants. This innovation marks a significant step toward more efficient and accurate claims management.

Final Thoughts

As we stand on the brink of a new era in AI in insurance, one question remains: How will these technologies continue to shape our experiences and expectations?

With examples like Verisk's quick claim review tool and Simplifai's engaging chatbot, the future is promising yet filled with challenges. One thing is for certain–future changes must balance technological benefits and security to ensure success in this evolving landscape.

In late 2001, Scott McNealy, the CEO of Sun Microsystems at the time, laid out for me a sweeping vision of remote work. Office buildings would be reduced to meeting rooms surrounded by a large parking lot. Employees would only come to the parking lot two or three days a week and would arrive around 10--avoiding rush hour traffic. They would plug their "sports utility offices" into the company network, letting colleagues know they were nearby if anyone wanted to arrange a meeting inside or just come by and bang on their window for a chat. Everyone would leave by around 3pm--again avoiding rush hour--and go home... to continue working.

McNealy had outfitted every employee with a home computer because, he said, "I do not want somebody at 10 o'clock at night who can't sleep, who wants to work because there's nothing good on TV, to not have full capability to do everything he needs to do to get the job done."

I asked: "Will people have trouble splitting work from home life?"

"There is no distinction," he said.

In my experience, McNealy was prescient about a lot of things. For instance, Sun adopted the slogan "the network is the computer" 40 years ago, long before most of us had even heard of the internet, and McNealy began describing cars as "computers on wheels" almost that long ago, well before most of us were aware of the processors being built into cars. I think he may have been right, if a bit early, about the productivity possible through remote work, too.

Certainly, something is driving the major, recent gains in productivity--up 4.9% on an annualized basis in the third quarter and a further 3.2% annualized in the fourth quarter, after a 1.9% decline during the COVID chaos of 2021 and a 1.2% rebound in 2022.

Productivity numbers are tricky, and it takes years to truly discern a trend -- the surge in the '90s from digitization, including the internet, wasn't fully recognized until 1999 -- but if my instincts are right, and we're at the start of a similar boom, then insurers face a huge opportunity and a challenge.

The opportunity is that all the efforts now underway to improve efficiency can become far more ambitious. The challenge is that they will have to become far more ambitious, because some, even many, of your competitors will seize the opportunity even if you don't. 

I realize I'm somewhat biased here, because I've been Team Remote Work for more than 25 years, since I left the Wall Street Journal and became a partner at Diamond Management & Technology Consultants. Yes, given the consulting firm culture of when-in-doubt-get-on-a-plane, I commuted from the Bay Area to headquarters in Chicago two or three times a month, but when I was home, I was home.

If my two daughters, then quite young, wanted to jump in the pool, I could just about always find time to go have my best moments of the day (and maybe theirs). When I needed to get serious, I could just shut the door to my office and get to it. The result was a happy employee and some of my best work -- the magazine my team and I produced for Diamond was once a finalist for the National Magazine Award for General Excellence, the industry's highest honor. 

Yes, many jobs require more interaction with multiple people in real time than the editing of a bi-monthly magazine does and don't lend themselves so easily to remote work. I sometimes think of the experience of my younger brother, who, like me, started on the copy desk at the Wall Street Journal but who didn't go remote over four decades -- and really couldn't have, even though the act of editing only requires a person and a computer. Instead, he commuted two hours to New York City from a northern suburb of Philadelphia and two hours home every day because, especially as he took on more senior editing responsibilities, he had to be in the mix as stories changed and as copy did or didn't arrive on deadline.

There are, of course, also many jobs that simply can't be remote -- in construction, hospitality, healthcare and more. 

But I think most insurance jobs have more in common with my experience editing a bi-monthly magazine than they do with putting out a daily newspaper or with the jobs that have to be in person. Someone underwriting, handling a claim or working with people to sell or service a policy certainly has reasons to interact with others but is doing the productive part of the work on their own.  

The jury is still out on whether innovation can happen as easily when people work remotely and whether any sense of isolation is harmful, but a lot of benefits are clear-cut. Employees are happier and thus less likely to leave. You've just given them what amounts to a raise by cutting their commute expenses and have given then back maybe an hour of each day they don't have to come in. You've made it easier for them to juggle any responsibilities with children or with aging parents and to work around any family illnesses. 

You also reduce your need for office space and for relocation expenses. (The WSJ paid to relocate me five times in my 17 years there.)

This article in the New York Times explores in detail the potential for productivity gains from remote or hybrid work. It also introduces the prospect that AI, especially recent developments in generative AI, are already feeding into productivity improvements.

Personally, I think that may be optimistic. It took close to 20 years from the time personal computers appeared in the late 1970s and early 1980s until major productivity gains from digitization registered. And, while I think there is much more low-hanging fruit to be harvested with AI than there was with massive IT projects in the '80s and '90s, I still think we're in the beginning stages of the Gen AI revolution. 

But it will happen. And I haven't ever heard anyone say insurance is an efficient industry. We've made great progress in the past decade and will continue to do so, but there is an awful lot of white space still there for innovators.

We now seem to have two waves to ride: first, the switch to remote work, then the long, deep benefits from the adoption of generative AI. 

As I often say, I take to heart the Silicon Valley mantra that you should never confuse a clear view with a short distance, and I may be violating that adage here. But even if you add a few years to my guesses, we're still in for profound change, and I think we can all aim higher.

Cheers,

Paul

P.S. My best work-from-home moments came when my younger daughter got home from pre-school. I'd hear the front door slam, then the sound of her backpack landing on the hardwood in the foyer, followed by her little tennis shoes slapping against the floor as she raced toward my office. I'd turn my chair to face the door, so she could launch herself into my chest and land in my lap. She'd give me the biggest hug, and we'd chat about her day. Occasionally, she'd fall asleep in my arms. I'd never dream of putting her down, so I'd take phone calls with her on my chest and even type emails or edit articles around her little body. 

I doubt anyone has ever had a better experience working in an office or come away more energized.  

KEY TAKEAWAY:

--By understanding who Gen Zalpha is, tapping into their spending power, rethinking advertising strategies and building authentic connections on social media, insurance brands can position themselves to thrive in the evolving consumer landscape.

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In the dynamic landscape of digital marketing for insurance, understanding and connecting with emerging consumer generations is key to staying ahead. 

As we venture into the era of Gen Zalpha, a fusion of Gen Z and Alpha, the insurance industry and marketers are presented with a unique set of challenges and opportunities. With their innate digital savviness and significant spending power, Gen Zalpha is reshaping the consumer landscape. 

To effectively engage with Gen Zalpha, it's crucial to understand who they are and what defines their digital behavior. Gen Zalpha represents those born from 2001 onward, combining the characteristics of both Gen Z and Alpha. They are growing up with smartphones and social media as integral parts of their lives.

The most recent social medium to rise to prominence is TikTok, with 1.1 billion active users across 160 countries. TikTok has overtaken Twitter, Reddit, Pinterest and Snapchat very quickly. It is also thought to have taken a big portion of YouTube’s user base, a figure that is yet to be determined. The potential for reaching new customers and advertising has led to the expansion of the insurance industry onto these platforms. Many insurance businesses are creating accounts on these platforms to expand their territory, build trust and attract users. 

Gen Zalpha has never known a world without the internet. They are fluent in digital communication, and social media platforms are their primary means of connection, information and entertainment. Which makes them so powerful for insurance brands to target.

Unlocking Gen Zalpha's potential requires a strategic shift in marketing approaches, embracing the digital behaviors and preferences of this unique demographic. Here are some actionable tips for the insurance industry to captivate and engage with a Gen Zalpha audience: 

See also: Revolutionizing Life Insurance Uptake in Younger Markets

Unleashing the Spending Power of Gen Zalpha

Gen Zalpha wields substantial spending power and can influence household purchases. To tap into this demographic, marketers need to employ strategies that resonate with their unique preferences and behaviors. One way of doing this is through influencer marketing.

Gen Zalpha is highly influenced by social media personalities and content creators. Collaborate with influencers who align with your brand values to authentically reach and engage this audience. A good example of this is Tom, life insurance for dads, who are targeting new dads and a younger demographic through social media and harnessing celebrity influencers to endorse and promote their product.

Rethinking Advertising Strategies for Gen Zalpha

Gen Zalpha is reshaping the advertising landscape. Marketers need to adapt their strategies to meet the expectations of this audience.

• Mobile-First Approach: Gen Zalpha's digital world revolves around mobile devices. Ensure that your advertising strategies prioritize mobile platforms, with responsive and visually appealing content.
• Data-Driven Personalization: Leverage data analytics to understand the preferences and behaviors of Gen Zalpha. Personalize your campaigns to deliver content that resonates on an individual level, increasing the likelihood of engagement. Regular interaction with your audience on social media through relevant content can provide a wealth of insight into their preferences and thoughts. This deep understanding of your customers' needs and expectations enables you to devise better marketing strategies and tailor your offerings accordingly.
• Sustainability and Social Responsibility: Gen Zalpha values brands that prioritize sustainability and social responsibility. Incorporate these values into your advertising messages to create a positive brand image and foster loyalty.

See also: What to Understand About Gen Z

Building Authentic Connections Through Social Media

To truly unlock the potential of Gen Zalpha, insurance brands must focus on building authentic connections on social media platforms. Here are some actionable tips:

• Educational Content: One of the best ways to use social media for the insurance industry is to create educational content that explains various insurance policies and their benefits. These videos should be short and engaging, with a clear focus on helping viewers understand complex insurance concepts.
• Show Personality: One thing you can do to spice up your content and make yourself stand out even more is to let your own personality shine in your videos. Show yourself as upbeat, humorous or fun, so viewers can relate to your brand. This is especially important when tackling a topic like insurance. 
• User-Generated Content (UGC): Encourage your audience to create and share content related to your brand. UGC not only provides authentic testimonials but also enhances brand visibility among Gen Zalpha's peer networks.
• Community Engagement: Foster a sense of community around your brand. Engage with your audience through comments, messages and polls. Actively participating in the online conversation helps build trust and loyalty.
• Trend Participation: Stay current with social media trends and challenges. Participating in popular trends demonstrates that your brand is culturally aware and aligns with the interests of Gen Zalpha.

By understanding who Gen Zalpha is, tapping into their spending power, rethinking advertising strategies and building authentic connections on social media, insurance brands can position themselves to thrive in the evolving consumer landscape. As marketers, it's crucial to stay agile, adaptable and attuned to the pulse of Gen Zalpha to effectively capture their attention and loyalty.

The innovative potential of AI technology is giving rise to bountiful opportunities for virtually every business sector.  

In the insurance industry, AI is projected to reach an astounding $80 billion by 2032, up from $4.59 billion in 2022 – in part a testament to AI’s profound capacities to inform data-backed decisions, optimize business operations and enhance customer experiences. 

When it comes to life & annuities insurance, AI has the potential to underpin a wide range of variables throughout the policy lifecycle, including policyholder behavior, fraud detection, risk assessment, claims processing and mortality rate predictions, as well as underwriting services. 

As with any technological tool in the process of maturation, AI has its downsides – particularly the inherent biases often embedded into the data used to train AI models. Left unchecked, these biases can lead to discriminatory outcomes, potentially putting certain individuals or sectors at a disadvantage.

See also: 4 Key Questions to Ask About Generative AI

The Bias at Hand

AI models are not necessarily predisposed to generating biases; rather, bias is a byproduct of the data used to train them. In other words, people are biased, the data we create reflects those biases and AI is not yet sophisticated enough to pick up on the discrepancies. After all, AI is purely statistical – deducing such human nuance is beyond the current scope of AI. Thus, when an AI model undergoes training using subjective data, it is susceptible to reinforcing and magnifying biases in its decision-making. 

Within the L&A insurance sector, such artificial misunderstandings – or hallucinations, as they have come to be termed – can result in a variety of negative outcomes, starting with unequal pricing. For example, if a particular racial or ethnic group has had historically higher mortality rates, AI might unfairly charge them more, even if their individual risks vary.

Bias-compromised training data can also influence AI to recommend inadequate coverage. In this scenario, some individuals face restricted access or outright rejection when seeking insurance coverage due to associations with certain regions or socio-economic backgrounds deemed as higher-risk.   

Furthermore, biased AI models tend to induce a lack of inclusivity, meaning that they fail to cater to the unique needs of diverse customer groups equitably. For example, these models may not adequately account for the unique needs of individuals with specific health conditions, resulting in a limited range of available annuity options. 

Course Correction 

Detecting bias in AI is a complex process that starts with identifying the biases that exist within the originating data as well as the biases that accumulate – or even multiply – through continuous training. Addressing these errors calls for insurance companies to test their AI algorithms, monitor outcomes and fix any unfair patterns on a continuing basis. Attempting to do so only after the data has been selected and the AI trained would be counterproductive.

Thus, insurance companies should strive to leverage AI models that exhibit straightforward and transparent reasoning – such models enable close scrutiny of algorithmic outcomes and foster trust-building with clientele. Furthermore, AI models can be engineered to produce more equitable outcomes by factoring in datasets from various demographics and geographic regions. In short, despite AI’s capacity to streamline insurance processes and scale up productivity, human oversight remains vital in rectifying biases that AI inadvertently weaves into outcomes.

This is doubly important given that insurance businesses, their respective data and the use cases of AI in the L&A insurance landscape keep evolving. Such dynamics require insurers to keep their fingers on the pulse of AI’s limitations. Industry organizations such as the National Association of Insurance Commissioners are addressing these challenges by establishing specialized working committees, such as the Accelerated Underwriting Working Group and the Big Data and Artificial Intelligence Working Group.

See also: Eliminating AI Bias in Insurance

Bypass the Bias

Considering the dynamic nature of business models and data, AI bias represents a formidable – but not insurmountable – challenge for insurers within the L&A sector. 

While creating a pristine and proper training dataset may be difficult, the solution lies in embracing responsible AI development practices such that they yield impartial solutions that align to the needs of diverse customer bases. In doing so, insurance carriers can bypass the bias, steering the industry toward a new paradigm wherein products are not just affordable and accessible but equitable for all.

FROM THE EDITOR  The horrific combined ratios in homeowners, auto and some other lines have drawn even more attention than usual to underwriting. So this month's ITL Focus is especially timely. To suggest some ways to improve profitability, I turned first to Jess Keeney, chief product and technology officer at Duck Creek Technologies. In our interview – which I encourage you to read in full – she talks about the importance of personalizing underwriting and says AI makes that possible in new ways. In particular, AI can pull together more data than the underwriter previously had access to, which allows for a deeper understanding of the client and of the risks. AI also can automate routine tasks, giving underwriters more time to dig deeper into an application.  I also pulled together the six articles I've linked to below – just a few of the really meaty articles we've published recently on underwriting – and think they all help illuminate a better path.  Bill Deemer and Bobby Touran of Rainbow lay out the possibilities for continuous underwriting, in a two-part series. At the moment, we do a deep dive the first time we see an application and update the file at renewal time, but they explain how technology allows for continuous updates. Why wait until renewal to realize that a restaurant client, say, has added alcohol sales?  Michael Reilly of Accenture challenges underwriters to truly go paperless. Yes, he says, we've eliminated all the file cabinets full of paper, but we've just switched to what he calls "digital paper" – PDFs. Excel spreadsheets, etc., which still make it too hard for underwriters to find all the information they need.  Jacob Grob of Tensorflight says insurers are reaching outside their walls and increasingly drawing on third-party data that can make underwriting far more precise. Adam Cherubini of Send explores how to attract the next wave of talent to underwriting – a key concern. Neeraj Kaushik of Infosys McCamish Systems lays out the particular challenges in the cyber world, which is constantly changing but where insurers seem to be doing a lot of things right. Neil Chapman and Serhat Guven of Willis Towers Watson describe the opportunities – and perils – of automated pricing. I hope you find the interview and those articles as smart and helpful as I do. Cheers, Paul  "Continuous evolution defines underwriting," asserts Jess Keeney, Duck Creek's Chief Product & Technology Officer. "Innovation demands perpetual vigilance; over my 15-year tenure in B2B product delivery, I've never uttered the phrase, 'Mission accomplished.'" Read the Full Interview "We are here all day every day because we want to serve people in businesses, and underwriting is obviously core to that. The best thing we can do with a new technology like Gen AI is to embrace it and figure out how to leverage it in the best way possible. Usually, new technology is met with fear. But the faster we can understand the best use cases for generative AI and implications for underwriting, the better. " — Jess Keeney Read the Full Interview READ MORE The Promise of Continuous Underwriting Typically, a risk is underwritten, bound... and forgotten. But new streams of data and automation allow for continuous underwriting. Read More 'Intelligent Ingestion': Time to Truly Go Digital The industry kids itself about having gone paperless. In fact, we still use the same processes we used in the 17th century. It's time for a change. Read More How External Data Is Revolutionizing Underwriting Combining external data streams with internal analytics creates a comprehensive view of properties that legacy methods can't match. Read More 'How to Captivate the Next Wave of Underwriters The answer is a strategy that blends technology, talent development and alignment with the values of the younger generation. Read More Risks, Trends, Challenges for Cyber Insurance Cyber underwriters face a myriad of risks, emerging trends and formidable challenges in crafting robust policies. Read More The Keys to Automating Pricing For all the undoubted benefits of automating insurance pricing, experience shows that success isn't just about throwing technology at a problem.  Read More FEATURED THOUGHT LEADERS Bill Deemer Bobby Touran Michael Reilly Jacob Grob Adam Cherubini Neeraj Kaushik Neil Chapman Serhat Guven

As Chief Product & Technology Officer (CPTO), Jess leads Duck Creek's product vision to drive value for customers, partners and system integrators. She is a champion of improving the customer experience and leads with a forward-thinking attitude toward deepening the connection between products. Jess has more than 15 years of experience delivering B2B products at an enterprise scale across multiple industries.

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Paul Carroll

You’ve written for us about the need for personalization in insurance. Could you start us off there?

Jess Keeney

At Duck Creek, one of our missions is to humanize insurance technology. We're in the business of providing protection for people and businesses, and you can't do that without personalization. It's key to understanding buying patterns, making sure we meet the customers where they are, reducing the burden by making it easier to understand what that protection is, and understanding what coverages we can provide and what the risk factors are for the buyer and for insurance carriers.

Personalization also leads to increased efficiency and what I like to call “higher-reward work” for human workers. It also improves accuracy for insurance companies.

Paul Carroll

How does generative AI fit improve that personalization?

Jess Keeney

With its natural language processing, Gen AI can communicate in human form and at a much larger scale than we've seen before — and in real time. It’s going to help in lots of great ways.

One of the areas is in summarization or categorization of information -- at scale and faster than a normal human pace, also making it more digestible for the people who are consuming the information, while making sure it’s presented within the normal workflow without adding a delay.

That improvement can show up in anything from how we're assessing and processing claims, to how we're underwriting policies, to how we're collecting premiums, and ultimately making better-informed decisions.

Paul Carroll

It seems to me that there are two main ways that Gen AI will contribute to personalizing underwriting. You’ve briefly mentioned both of them. First, the underwriter gets more information faster. Could you talk a bit about how that plays out?

Jess Keeney

You can incorporate more data sources without going through manual processes. You can improve the thought process of risk analysis, how you look at historical risks, and incorporate new risks from any new dataset. And those datasets are prolific and pervasive across the ecosystem, whether it's the Internet of Things (IoT), including telematics, or really anything that can allow insurers to adjust a premium based on actual behaviors.

Obviously, the prime example of what we can now track is driving patterns. But more and more, we're seeing computers interpreting visual information, satellite information, wildfire information, anything in the geo- or economic-political climate that would have an impact.

Another use case is how insurance carriers are evaluating and prioritizing new business. They have the ability to set the right priorities. What should we be processing first because it might have a greater impact on our profitability? What is a faster detection of fraudulent activities to reduce the risk of financial losses?

Paul Carroll

The second piece I wanted to ask you about is what you refer to as the higher-reward work: the idea that if you take a lot of the manual stuff off my plate then I can do other things that would personalize the underwriting and lead to better decisions.

Jess Keeney

That change allows you to have a better understanding of the business and what's going to give you a competitive edge. You get a better understanding of the market trends, of what's coming. You have more time for understanding changing customer behavior and can test to see what’s going to result in a different buying pattern. You can inform product development and provide insights into pricing strategies because you're not sitting there doing manual repetitive tasks.

You’re looking at better engagement with the underwriter, better job enjoyment, reduction of repetitive costs, also potentially reduction of human error. You’re removing operational cost, and people enjoy working more on strategic application of their thought process and how they can help the business.

Anything that can be automated should be automated. And that gives us a lot more time to focus on the newer complexities that we should be getting ahead of.

Paul Carroll

Insurers worry a lot about the talent gap, but people who are enjoying the work more are more likely to stay, and it becomes easier to recruit people, right?

Jess Keeney

If you talk to people graduating from college and say, “Hey, do you want a job where you're doing the same set of manual tasks over and over again?” not a lot of people are going to jump at that opportunity. But if you go to that same group and say, “Hey, do you want to use artificial intelligence to really understand how to do customer segmentation and understand new buying patterns for people of your generation?” that's a far more enticing job offer.

GenAI can help new talent and the new generation enjoy work because it spurs more meaningful and creative work for everyone. Also, if used properly, it can help enable more fulfillment and lead to a more engaged workforce that has opportunities for all.

Paul Carroll

I’ve asked you about two areas. Are there others where you see Gen AI playing out in underwriting?

Jess Keeney

The other obvious one is better customer support and feedback. If you give underwriters more time, they can see where they might be losing people out of the funnel, where people are not concluding with a purchase. You can also use a chatbot or do something in human language form at the spot in the cycle where people are dropping and ask, “Do you need more explanation? Do you understand what this means for you? Do you understand the risks? Do you need more protection?” Even if you don’t have an actual human asking those questions, you’re creating an avenue to offer additional products and services if they're in the wrong one. You also create cross-sell or upsell opportunities because you understand the customer better.

Paul Carroll

That's interesting. Somebody a while ago told me that the interesting thing to her about generative AI is that, while AI always had a brain, it now has a mouth. If an underwriter decides, let’s say, to turn down a risk, they might want to offer some explanation that could create another opportunity, whether then or down the road. But that takes time, and there are usually more pressing priorities. With Gen AI, you can have it generate an explanation that can give you a pretty good starting point and greatly reduce the time the underwriter spends on that explanation.

Jess Keeney

Exactly. I think of this like something that has probably happened to all of us in our online shopping. You get to checkout and decide you don’t want something. Two days later, you get prompted: “You left something in your cart.” When you return to the site to look, you maybe buy that product or purchase something else while you’re there.

I think that is where we're going with insurance products, as well. If you're in the wrong area, you're not going to complete that funnel. So, what can the insurer offer instead, because you have a better understanding, based on customer segmentation and understanding the personalization that you've offered them? What are the other products that they might be willing to work with you on?

Paul Carroll

Any final thoughts on how AI can improve underwriting?

Jess Keeney:

The only other thing we really haven’t focused on is just making more informed, better decisions.

Paul Carroll

It seems like this is a time when that message resonates even more than usual, because underwriting results have been so bad in so many parts of the industry in recent years, especially in homeowners and auto.

Jess Keeney

Agreed.

Paul Carroll

Any Final Thoughts?

Jess Keeney

We are here all day every day because we want to serve people in businesses, and underwriting is obviously core to that. The best thing we can do with a new technology like Gen AI is to embrace it and figure out how to leverage it in the best way possible. Usually, new technology is met with fear. But the faster we can understand the best use cases for generative AI and implications for underwriting, the better.  

Nothing has ever seen this sort of pace of adoption. It's going to be amazing.

Paul Carroll

Thanks, Jess.

Today’s world is confusing, replete with mixed and conflicting signals. Reports of low unemployment and high inflation have muddied forecasting for a recovery or a looming recession and leaves us unsure of what to expect: traditional economic cycles or permanent change. Either way, such signals foster uncertainty and test our objectivity on a daily and even hourly basis.

The P&C insurance market is displaying its own mixed signals, making it difficult for insurance business leaders to plan and adjust, not to mention the tremendous disruption to agents and customers. Large and persistent rate increases contrast with the most recent improvements in combined ratios and net income. Tighter underwriting actions and lack of insurance availability is creating challenges in navigating the complex landscape by all stakeholders.

See also: How to Respond at Inflection Points

The Insurance Clock

Back to the question: cycle or permanent change? Despite P&C’s desired state of steady, profitable growth, the reality is a constant tension and cycle of writing new business followed by turning the dials for profitability. In just the last 25 years, major shockwaves from 9/11 terror attacks, wars and the housing and financial market collapse have tested industry cycles, yet the clock still ticks ahead, perhaps with more elongated recoveries after these shockwaves.

The Insurance Clock best illustrates these cycles and was introduced by Paul Ingrey, former Arch Capital Group director and board chairman, and still applies today. It is a sophisticated view beyond the simplified hard market, soft market labels. For instance:

• At 1 o’clock, “ROI Peaks Out” with few questions asked and falling prices. This feels like pre-pandemic conditions.
• At 4 o’clock, “ROI Sinks,” with horrible results and underwriting losses exceeding investment income.  Perhaps this was 2022-23. 
• From 7 to 10 o’clock, prices are up sharply, and capacity becomes expensive, which fits squarely with today’s market scenario. 
• Finally, from 11 to 12 o’clock, there is combined ratio profit leading to Euphoria at the very top of the dial. Hiring, happy producers, prices stop rising.

Source: Paul Ingrey, Arch Capital, 1985

Underlying this turbulence is the age-old question: Is this simply a cycle, or are we witnessing lasting changes driven by new technologies, altered consumer expectations, post-pandemic inflation and weather changes? To understand the current state of the P&C insurance market, it is necessary to delve into these key factors.

Rate Increases

One of the most noticeable trends in the P&C insurance market has been the steady increase in insurance rates. The drivers behind these rate increases are multifaceted. Post-pandemic inflation has led to higher costs across the board, from materials to labor. Additionally, the frequency and severity of natural disasters and extreme weather events have been on the rise, leading to increased claims payouts. Insurers are responding by adjusting their pricing models to reflect these new realities, which has resulted in higher premiums for policyholders. 

Emerging and growing cost factors are real and may not yet be fully reflected in today’s rates. Namely, social inflation, nuclear verdicts, aging drivers and related auto injury costs, new car technology and repair/replacement costs and auto repair technician shortages are obscured by the two obvious culprits: inflation and weather losses.

Positive Insurer Profitability Picture

Despite these challenges, several insurers are beginning to see positive signals in profitability, particularly looking at Q4 of 2023. This improvement can be attributed to several factors, namely higher rates and fewer storms during the end of 2023. Additionally, more rigorous underwriting standards are leading to healthier books of business.

Net income and combined ratios among several leading carriers are making what looks to be an impressive rebound, with Progressive, Chubb, Travelers and GEICO among the most vibrant, to name a few. Progressive’s most recent combined ratio dropped to 87.3. Allstate’s combined ratio for homeowners’ insurance during Q4 was down 30.8 points to 62, and their auto line fell to 98.9 in Q4. Allstate CEO Tom Wilson said “improved auto profitability and mild weather” drove the fourth quarter turnaround. However, “rate increases will continue to be implemented to keep pace with loss trends and improve margins in states where we have not yet achieved rate adequacy.”

The improving profitability picture in Q4 2023 is a welcome development for insurers, as it indicates that efforts to adapt to changing market conditions are bearing fruit. However, it remains to be seen whether this trend will continue in the face of inflation and weather wild cards. 

See also: 5 Key Mistakes in Long-Term Planning

Lack of Insurance Availability

Insurers are becoming more selective about the risks they underwrite, creating a lack of insurance availability in high-risk regions, leaving homeowners and businesses vulnerable. Higher deductibles, less coverage, push toward wind pools and flat participation in NFIP’s flood insurance program despite more flooding is becoming the norm. The widening protection gap, underinsured and uninsured are highly concerning and defy the risk-transfer proposition of insurance.

The lack of insurance availability is a complex issue with no easy solutions. Insurers are caught between the need to manage their risk exposure and the desire to provide coverage to those in need. Finding the right balance between is crucial to ensuring the long-term sustainability of the insurance market.

Recurring Cycle or Permanent Change?

According to the Insurance Clock, there is little need for panic, in fact, good reason for optimism. Although one might interpret the return of a stable market not so far away, just two to three hours, according to the clock, that does not seem remotely possible. Yes, raising rates has always served as the ultimate relief valve and the fastest way to recovery. Bolt on some re-underwriting actions, and the combination becomes the fixer of all underpriced or unfavorable risk selections. Once rates become adequate, risk appetite increases, and carriers revert to a growth mindset in what compares to financial yoyo dieting. At least, this is how it has worked in the past.

Things are different now as inflation is proving persistent and as catastrophic weather exposure is accepted as here to stay -- more frequent and more severe. Certainly, things are vastly different from 1985 when the Insurance Clock was invented. Still, there are continuing shockwaves from COVID-19, which reshaped the economy and changed the workforce in terms of participation and shortages. Population migration to CAT-exposed Southern states and away from cities like San Francisco and New York are shifting exposure in real time. Changing social attitudes and risks are happening at a quicker pace. Massive rate increases and pull-out from markets and whole states is widespread. Although it is difficult to forecast all future COVID-related impacts, those identified are more pronounced and longer-lasting, bringing the cycle into question. 

Arguably the most significant changes since the Insurance Clock was introduced in 1985 are those resulting from new technologies. These include: cell phones, World Wide Web, notebook computers, the personal digital assistant, VOIP, Amazon, eBay, WebEx, WiFi, Google, Blackberry, Bluetooth, Apple iPod, Wikipedia, wireless internet, application programming interfaces (APIs), wireless high-speed internet access, Facebook, YouTube, Twitter, social media, Apple iPhone, Apple iPad, Skype video conferencing, tablet computers, Amazon Echo, VR headsets, facial recognition, IoT, AI, 5G, cloud computing, blockchain, quantum computing, virtual meeting software and virtual hybrid events. The combined impact of these advances is unprecedented in how deeply and widely it has permanently changed almost every aspect of the insurance industry, leaving one to consider how new technology might ballast current trends or how much worse would today’s results be without all these advances.

We believe that the traditional insurance cycle is no longer as relevant as it once was, primarily because underlying market conditions have permanently changed – and will continue to change. In this environment, insurance leaders must become more entrepreneurial, encourage and embrace innovation and reshape their organizational cultures to be the same.

Carriers should seek to develop strategic partnerships and alliances with more nimble technology-enabled partners. Recruiting, hiring, training and upskilling strategies should be revisited to ensure appropriate alignment of required skills today and tomorrow. And diversification of product and even business focus could be appropriate as a means of spreading the risks presented by changing and unpredictable future conditions.      

 

Anthony (Tony) Kuczinski is a highly regarded executive leader with over 38 years of (re)insurance experience, 34 years of which were with Munich Re in numerous senior roles, including 15 years as President and Chief Executive Officer of Munich Reinsurance US Holdings. He also served as Executive Advisor to the Munich Re Board of Management for MR US Holding (MRUS), the NA Property and Casualty operations of Munich Re (DAXI: MUV2). Prior to Munich Re, Mr. Kuczinski was Chief Operating Officer of NY Marine and General Insurance Company (NYM), a publicly traded insurance group now part of Pro-Sight Insurance Group, and he worked in the audit practice for the public accounting firm of Coopers & Lybrand (now PWC).

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Paul Carroll:

How would you characterize the evolution of insurtech over the last decade?

Tony Kuczinski:

At Munich Re's U.S. business, we were early participants in the insurtech wave. I never believed insurtech would disrupt the insurance industry, and in retrospect, it didn’t. However, I firmly believed we needed to be involved in this space. Over the last decade, innovation has changed dramatically. While insurtech hasn't disrupted the industry, it has changed the industry for the better, specifically for the more forward-leaning companies and those focused on strong underwriting fundamentals and those keenly focused on their clients. Insurtech has made us faster, more nimble and more technologically savvy. It has also made us more client-focused.

The early insurtech players believed they had a different model that would make the industry dinosaurs obsolete. This turned out to be a misconception on their part. Many insurtechs stumbled over the years. Others realized early on that the risk-sharing model they wanted to disrupt wasn't what they thought and shifted more toward being MGA-oriented or distribution-oriented.

Insurtech isn't gone. It just didn't materialize as initially anticipated. Valuations were high in the early days, then stumbled, and there was a back-and-forth kind of situation for some time. And the jury is still out for the risk takers among them. However, I think they made the industry better in the long run.

If you invested in insurtech, and collaborated with them, you probably learned some lessons about being nimble and agile. You probably learned to deploy technology differently than you did in the past. You also likely became more intensely focused on the customer and the service elements you bring to them.

Looking forward, I still think there's a role for insurtech, but I think it's going to take a different form. It's going to be more about getting better at what we do. How do we help to mitigate risk? How do we get better at identifying risk? The industry will evolve into a predict-and-prevent model as well as a financial reimbursement model. How do we get better at servicing the client and at providing this service more efficiently? And how do we deploy effectively artificial intelligence in the insurance space today?

Paul Carroll:

Why do you think the big tech companies didn't jump in and disrupt the insurance industry?

Tony Kuczinski:

Early on, I think there was a lack of appreciation for two things in the industry: capital and regulation. There is a big capital need to be a strong player in the industry, and it's a highly regulated industry. Even the non-standard businesses aren't free from regulation. I think there was a recognition after a while that this is a difficult model to emulate. There was also a realization that the long-term-return proposition would dilute the hefty returns many of the big tech companies enjoy today.

The tech players dabbled a lot and are still dabbling in this space. However, I think they're looking at how to impact the insurance space more as a service provider or as an enabler, not as an insurance entity. I still think that's possible. It's clear to me that AI could absolutely help, whether it's robotic processing or better analytics. It could be a game changer.

Paul Carroll:

So the companies that improve insurers, rather than trying to replace them, are the ones thriving at this point?

Tony Kuczinski:

I think that's an accurate statement. I think there's a new phase of this. Those that took the new business model into places like cyber technology and cyber coverage have done pretty well. They focused not only on the risk element but the predict-and-prevent aspect. They're fairly new players, and you could call them insurtech firms. They've taken the distribution model and enhanced it with good technology to help with mitigation, identifying the risks and making us more aware of what the risk could be and how we mitigate those risks going forward.

On the enabling side, every industry is looking at AI in a very different way today. They're using enhanced technology to make information much more usable to the industry. I think that's a place where the industry is more likely to partner as opposed to just build themselves.

Paul Carroll:

Could you tell me more about how the new model works in cyberspace?

Tony Kuczinski:

On the cyber side, the cyber MGAs [managing general agencies] that exist today weren't around 10 years ago. They all came into existence with the entry of insurtech. Most of them came into the industry with some underwriting know-how, but more importantly, they came with technology know-how and tools as well as loss-control features that help provide not just an insurable product but also ways of mitigating the insurance exposure that's out there.

Paul Carroll:

Could you talk about the biggest successes that you witnessed or were involved in, and why you think they worked?

Tony Kuczinski:

So far, the cyber entrants are a success story. They're more of a success than the beginning days of some of the risk takers that jumped into this space that just didn't get it right. There are very few players that started out as insurtech that made a big impact in the marketplace. I would say there were several failures, more on the full-stack side. When you get to the MGA space, I would say there were more successes. And when you get to the very focused enabling aspect, there were or will be more yet.

Paul Carroll:

You said something that resonated with me partly because my mantra for 25 years, since I started writing about innovation, is: Think big, start small, learn fast. And you talked about how you got involved, at least to dabble, and become aware and participate and learn. How would you recommend people think about innovating now?

Tony Kuczinski:

The phrase that you used about think big, start small, learn fast resonates with me. But I would say to quit fast, too. If it's not working, you need to pivot or change. Here's where I think insurtech has helped and where legacy companies get it wrong: I think innovation is here to stay. Over these last 10 years, incumbents went all in on innovation, then, little by little, people backed away. When I say back away, I don't mean they're walking away from innovation. Instead, I think their approach changes to let's just use innovation as a constant theme and a constant way of improving the way we deliver our products or service, how we connect with a client, how we underwrite our business, how we mitigate the losses that could affect the business.

Paul Carroll:

Could you tell me more about your vision of prevention?

Tony Kuczinski:

Predict-and-prevent is a great term. We're in the risk business. We know the current risks. And those current risks are continuing to get more and more complicated and bigger. But we don't know all the risks yet to come. So we need tools that will help us to navigate both are the things that we should be focusing on as an industry and predict those things that could go wrong and build tools or resiliency to mitigate or eliminate them. This is the most important part of the insurance industry’s value proposition.

Paul Carroll:

Any final thoughts?

Tony Kuczinski:

We are in one of the best underwriting environments that we have seen in the insurance industry for quite some time. And there's still some legs to the strength of this hard market.

This is also one of the times when most traditional companies will spend the least amount of time on innovation and the future. My warning to the industry is, you should always be focusing on the future. Maybe a little bit less or a little bit more in certain markets or cycles, but you should never lose sight of the focus on the future and how you need to deal with what's coming, as opposed to where you are now.

In the end, we are a noble industry that brings people, businesses and communities from harmed to whole because of what we do. No industry plays this role better. But this carries an obligation to continue to look into the future and work on the strategies we will need in place to continue to deliver strong, positive outcomes.