Similar to how iPhone technology transformed the way we communicate, generative artificial intelligence (AI) is creating new efficiencies for the insurance industry. Most recently, Oliver Wyman has been working with leaders to augment and reinvent significant areas of their business. Here we share our latest research, a primer on generative AI, how insurers can get started, risk considerations, and opportunities and key actions for industry reinvention. 

Read More

Sponsored by ITL Partner: Oliver Wyman

With inflation showing staying power, how can your firm best harness risk, economic disruption and prepare for a potential downturn? Today’s challenging economic environment offers firms the opportunity to Reset4Value — and drive strategic repositioning, smart cost decisions, and fund their organization for a better tomorrow.

Read More

Sponsored by ITL Partner: Oliver Wyman

As the commercial insurance industry continues to evolve, insurers are under increasing pressure to streamline their operations, reduce costs and improve user experiences for agents AND insureds. The discipline of underwriting, involving assessing and pricing risk, is a critical function in this process, and the need for efficiency and thoroughness has never been greater.

One way insurers are addressing these challenges is by leveraging novel data, created by artificial intelligence (AI) to automate underwriting processes. By using machine learning algorithms to analyze vast amounts of data to predict the answers to underwriting questions, insurers can reduce the time and effort required to underwrite policies, while improving accuracy and consistency. It’s not too dissimilar from the way large language models (LLMs) like ChatGPT and Google’s Bard use vast amounts of unstructured web content to predict the next word in a conversational sequence.

Automation and data-driven decision-making also allow commercial underwriters to focus on more complex risks, while leaving routine and straightforward applications to be handled by machines. This not only increases efficiency but also allows underwriters to spend more time on high-value tasks that require their expertise and judgment. This is a MASSIVE win for underwriters who are forced into tactical, rather than strategic roles, manually processing applications rather than making value-added risk evaluations. In a low-complexity line of business like workers' compensation, a client of Planck was able to decrease processing time from hours to minutes and reduce submission errors by 29%, ultimately leading to a hit rate increase of 19%! 

Underwriting automation also improves risk management. Machine learning algorithms can identify patterns and trends that human underwriters may miss, allowing insurers to better predict and manage risk. This can lead to more accurate pricing, as well as fewer claims and losses for the insurer.

AI in underwriting also allows for more personalized risk assessments, which can lead to better pricing and coverage for clients. Insurers can use data from a variety of sources, including social media, satellite imagery and weather data, to gain a more complete picture of risk and tailor policies accordingly. This can help underwriters better understand the specific needs of each client, while also providing them with more targeted recommendations. Through a partnership with a top-three European carrier, Planck was able to identify that 75% of this carrier’s construction book of business was underinsured — leading to a 30% potential increase in revenue. 

See also: Insurers Boosting Their Use of AI

Underwriter augmentation of this sort is not a silver bullet, and it is not intended to replace underwriters entirely. Instead, if correctly deployed, AI serves to magnify underwriter abilities, enhance their effectiveness and ultimately make their jobs easier. 

Underwriting automation driven by AI-generated data can help make commercial underwriters more efficient, accurate and focused. By leveraging technology to streamline processes, insurers can improve customer service, reduce costs and better manage risk.

KEY TAKEAWAYS:

--The discussion has moved to how much analytical work and task automation are possible – and if it is feasible beyond simple repetitive tasks.

--Concerns have heightened about AI’s potential to replace humans and eliminate jobs.

--If it becomes impossible to tell if a person has created an image, email, letter or video, then the potential for fraud skyrockets, and determining liability in claims becomes more difficult.

----------

When I was asked in late 2022 and early 2023 about the implications of generative AI for insurance, my reply was always two-fold. First, I advised that it is absolutely a technology space to monitor closely, given the rapid advancement, broad application and unlimited potential. But secondly, I believed that the near-term use cases for P&C insurance were limited to more horizontal spaces and not so much to insurance-specific applications.

There is no question that even now there is value in automating and enhancing interactions. ChatGPT and similar tools are, at their root, designed for conversational AI – driving informed and automated chatbot-oriented interactions. Many types of communications can benefit from this technology – agency help desks, policyholder inquiries, claims status, internal conversations and many more. In addition, anywhere in the insurance enterprise where there is a need to summarize information, create digital material or extract data is now a possibility for AI to automate. In fact, this is more than a possibility – insurers are already deploying ChatGPT across many use cases.

Recently, I started cataloging all various interesting use cases of ChatGPT and, more broadly, generative AI across industries. However, I abandoned that effort as a hopeless task. There are many articles every day on how someone has used a generative AI tool to write code, pass exams, write papers, create art, images or videos, drive database queries, power conversations and more.

Within just a few months, I have seen the tone of these articles shift from the perspective that the AI output was amazing but not always accurate or high-quality to one where significant progress has been made. It was only a few months ago when it was sometimes easy to tell the difference between human- and AI-generated content; today, the task is far more difficult.

Now the dominant questions are not about whether the technologies are viable for real-world use cases. Rather, the discussion is about how much analytical work and task automation are possible – and if it is feasible beyond simple repetitive tasks (we already have RPA for that). The use cases are rapidly expanding into more complex, industry-specific areas.

This naturally heightens the concern about AI’s potential to replace humans and eliminate jobs. My fundamental view for many years has been that the AI family of technologies will augment humans and elevate the roles of industry professionals. Agents, underwriters, adjusters and others will focus on activities that require deep expertise, experience and empathy. I still believe that is true… but not as strongly as I did in the past.

See also: Google's $100B Mistake--and How to Avoid It

The other main question that arises is about the challenges of determining authorship. If it becomes impossible to tell if a person has created an image, email, letter or video, then the potential for fraud skyrockets, and determining liability in claims becomes more difficult.

The net of this blog is that generative AI in all its forms must be closely monitored by P&C insurers, and governments and the business world must develop the right regulatory/governing framework for AI. Experimentation with the technologies is mandatory. Now is not the time to sit on the sidelines and watch – things are moving too fast for that.

The recent launch of ChatGPT has underscored artificial intelligence’s newfound scale, speed and mass accessibility, as well as its long-term potential to complete complex tasks currently performed by humans. Suddenly, AI is in the headlines everywhere, drawing the attention of commentators, business executives and even policymakers. 

For the insurance industry, AI and machine learning (ML) technologies are by no means new. Across the sector, organizations have begun launching AI and machine learning programs over the past several years and are using the technologies to drive efficiencies in core parts of their business, including underwriting, risk management and claims. More recently, these efforts have intensified, driven in no small part by concerns about an uncertain economy and companies looking to “right size” their workforces.  

According to a survey of IT leaders we conducted in 2023, the insurance industry has been accelerating the use of AI over the past five years. But recently the rate of adoption has recently seen a remarkable surge, with a 30% increase in new AI/ML projects from 2021 to 2022. 62% of insurers say implementation of AI/ML has resulted in an overall reduction of their overall headcount, while other companies have focused on retraining employees whose jobs have been affected by AI. Most remarkably, 81% of insurers cited AI as their leading strategic IT priority, outpacing the use of cybersecurity at 63% and cloud at 58%.  

Over half of insurers say they are using AI/ML for product lifecycle management, as well as to drive innovation and data analysis. Intelligent search, document processing and customer engagement are other fast-growing areas. A sizable majority (65%) said they are leveraging AI and ML to improve speed and efficiency, while half say it is helping them predict business performance, and 46% are turning to these technologies to better manage risk.  

Challenges and Pushback

AI adoption across the industry hasn’t been without its challenges. First and foremost, insurers continue to face internal resistance to implementing projects. Over half (56%) of insurance IT decision makers said they have encountered pushback or scrutiny regarding the use of AI/ML in their organization. This could be the result of differing perspectives between business leaders and IT departments.  

Building trust in the results of AI/ML projects is another common issue. When asked if they feel the data that AI generates is reliable, more respondents (42%) said they only “slightly trust” the data than those (38%) who “completely trust” it. Moreover, less than half say there is sufficient governance in place to safeguard against any misuse of the technologies. 

Finally, more than two-thirds of respondents (67%) identified a shortage of skilled talent as the greatest challenge to greater AI/ML adoption. Other roadblocks include a lack of new use cases, algorithm/model failure and a lack of infrastructure necessary to support AI/ML. Despite these hurdles, 90% of insurers say they have grown their AI and machine learning workforces over the past 12 months.  

See also: OCR Plus AI Opens New Vistas

Room to Grow 

Even though over half of insurers say they’ve already realized substantial benefits from AI/ML, the survey also makes clear that there is substantial room to grow. The list of benefits to date is impressive:

• 81% cite risk reduction and an increased understanding of customers
• 79% have seen increased sales
• 77% have used AI to create more personalized marketing
• 75% say AI has increased productivity
• 73% have seen new revenue streams and operational cost reductions
• 69% cite improved customer satisfaction
• 67% have benefited from faster time to profitability and reduced the cost of product development
• 65% say AI has made them more innovative 

These numbers are remarkable, given the technologies’ relative infancy, and would indicate that the insurance industry has just begun to scratch the surface of what AI can do. As companies assess their existing projects and become more comfortable using artificial intelligence across more parts of the organization, AI will become an increasingly critical strategic differentiator and springboard for business success.

KEY TAKEAWAYS:

--Unless AI systems are trained on accurate, unbiased data by unbiased trainers, they can make faulty decisions on underwriting and claims that create legal and ethical risks.

--If great care is taken to train and review AI systems thoroughly, they can provide a host of benefits in customer service, claims and underwriting.

----------

Artificial Intelligence (AI) is transforming the insurance industry by enabling insurers to process claims more efficiently and accurately while improving customer experience. However, the adoption of AI in claims management and underwriting, while useful, poses significant risks and challenges that insurers must address to ensure successful implementation and avoid costly mistakes. 

Big names in the tech world, such as Elon Musk and Steve Wozniak, recently penned an open letter urging labs to pause the training of AI systems for at least six months or until developers and leaders agree on better rules-of-engagement -- ensuring AI is developed to improve human life and is not weaponized or used for harm. Zurich and Lemonade are some examples of Insurers that are investigating and subsequently creating controversy around ways to leverage this technology.

Here, I will explore the risks and challenges associated with AI in claims management and underwriting, as well as strategies to mitigate them.

Risks of AI in Claims Management

AI systems are only as valuable as the data provided by their creator. Any information beyond what an AI system is fed is then inferred to create what experts refer to as hallucinations. Hallucinations can be very convincing, even though they aren’t based on good data. What constitutes “good” data from “bad” is a mix of factors such as the accuracy of the data, the perspectives, opinions and biases of the human inputting the data and who is regulating or training these individuals.

These are just a few of the risks associated with using AI. But one of the most significant is that AI can be uncontrollable. With more and more competition to create the most knowledgeable AI systems, creators are getting to the point where they can’t understand or predict their behavior or accurately explain how a decision was made. This can be problematic if humans rely on AI’s assessments to determine specific actions, such as denying or paying an insurance claim. 

Following are some other ways these risks apply to claims management:

Bias

From healthcare to recruiting, all businesses are subject to bias. The insurance industry is no exception. The U.S. Department of Commerce reports that one of the most significant risks of AI in claims management is the potential for biases, discrimination, and inaccuracy. If AI systems are trained on biased or inaccurate data, the decisions made by the AI system will be the same. This can result in unfair treatment of certain groups of individuals or incorrect claims processing, leading to reputational damage and legal liabilities.

This problem has been seen more recently regarding the use of FICO scores in the underwriting of insurance policies. Insurers are obligated to ensure that bias is removed from underwriting and claims decisions and that the information being used is accurate.

Legal/Ethical Risks

AI systems also require access to large amounts of data, including sensitive personal information, which can be vulnerable to cyber attacks and data breaches. This is a risk because it can result in significant reputational damage, legal liabilities and loss of customer trust.

There are legal and ethical risks with AI, as well. For example, an AI system denying a claim based on biased or inaccurate data could result in legal action and reputational damage. Additionally, the use of AI in claims raises ethical questions about the role of humans in decision-making and the responsibility of insurers to ensure fair treatment of their customers.

Overcoming the Challenges of AI in Claims Management

The first step in overcoming the challenges of AI in claims management and ensuring successful implementation is to review the data for accuracy and quality. It is crucial to confirm where the data came from, how it was vetted and how sensitive data is protected. This includes regular monitoring and auditing of AI systems to identify and correct errors and biases.

With the amount of content that is created online, it’s easy to pull data that stem from questionable sources. These quality checks can allow insurers to identify and correct inconsistencies in the data. Data can also change over time, so your data must be current and relevant. Routine checks will enable you to do this.

Next, you must understand how the AI system was trained and by whom. Industry experts are better-equipped to provide the knowledge AI systems are fed versus those with just tech experience.

Collaboration between humans and AI systems is another key to ensuring fair and accurate claims processing. Insurers should establish clear guidelines for when and how human intervention should occur in claims processing and confirm that humans are adequately trained to understand and collaborate with AI systems.

An independent review board inside an organization should also be implemented to test the technical efficacy of the AI system and confirm it’s working as expected. This board can also determine the moral and ethical implications and whether the system should exist in the first place.

By implementing these strategies, insurers can overcome the challenges of AI in claims management and leverage the benefits of AI to improve the efficiency and accuracy of claims processing, enhance customer experience and drive better decision-making. 

See also: 'AI' or Just 'I'? Most Adaptable Will Win!

Benefits of AI in Claims Management

While AI poses risks and challenges, it also presents significant benefits in customer service, claims and underwriting.  

Customer Service

One of the primary benefits of AI is that it can process large amounts of data quickly and accurately, reducing the time and resources required for customer service. Here is how AI can enhance the overall customer experience:

• Use chatbots or answer bots to answer customers' questions quickly, reducing wait time
• Submit policy changes and other simple endorsements on behalf of the customer
• Notify the agent or customer of any outstanding items, such as payroll audits
• Note any significant rate increases at renewal and automatically re-shop the policy
• Create and forward policy documents, such as ID cards, declarations pages or COIs

Improved customer service can help to protect margins on commissions as a result.

Claims Management

AI can process claims end-to-end and eliminate customer or agent frustration throughout the entire claims process by:

• Managing First Notice of Loss (FNOL) and First Report of Injury (FROI) correspondence
• Classifying, indexing, extracting and relaying claims data into agency/carrier systems
• Screening for potential fraud and validating eligibility
• Calculating and setting reserves and paying claims
• Identifying, servicing and following up on time-sensitive activity with adjusters, such as legal demands within demands packages
• Forwarding explanations of benefits (EOBs) and policy information
• Identifying accounts for recovery or subrogation based on the value at stake

Conclusion

With the challenges involved with AI and the potentially significant impact it can have on the world, there should be checks and balances to mitigate moral, ethical and legal concerns. As the AI open letter so succinctly states, rather than a race to the top, all future research by AI labs should focus on how to make the systems accurate, trustworthy and safe.

With the challenges involved with AI and the potentially significant impact it can have on the world, there should be checks and balances to mitigate moral, ethical, and legal concerns. By taking a break to establish these standards, we can be better prepared to use AI in the future and continue to revolutionize the insurance industry.

KEY TAKEAWAYS:

--In the midst of all the enthusiasm, it's right to imagine your full range of strategic innovation options in the context of your company’s particular markets, strategy and key business drivers.

--But don't fall in love with the one or two you think are the best. Instead, break them down into smaller pieces and run those opportunities through a rigorous screening and experimental process.

--Test with inexpensive, rapid prototypes (not pilots) and be extremely disciplined about which should be dropped, which merit continued exploration and which should move into the pilot phase.

----------

AI is having quite a moment. One high-profile AI app, ChatGPT, has become the fastest-growing consumer app in history. It garnered over a million users in its first five days and hit one 100 million active users in just two months. That’s faster than TikTok, which took nine months to reach the same mark, and Instagram, which took 2.5 years.

ChatGPT is one of many recently launched applications using machine learning techniques to identify patterns from very large data sets to generate content. As its name implies, ChatGPT uses a text-message-like user interface to “chat” with the user. It can generate and tune intelligible (though not always correct) responses to seemingly any prompt, and it can do so in the form of essays, jokes, poetry, recipes and even computer code in response to user direction. Other apps can generate content in video, art and audio form.

The race is on to leverage the underlying AI platforms, tools and techniques in a wide range of consumer and industrial applications. Microsoft has invested $10 billion in OpenAI, ChapGPT’s developer, in hopes of using ChatGPT to help free it from its laggard status in the very lucrative business of internet search. Other tech giants, like China’s Baidu, also see this as an opportunity, and are investing heavily in similar capabilities. Not to be left behind, Google has made clear its intention to respond in force with its own suite of deep AI capabilities. A range of other industrial applications are being explored and debated, including in media, healthcare, education and finance.

Is it time for insurers to “go big” on this technology, too? The short answer is no. But, insurers shouldn’t just take a wait-and-see attitude, either. Instead, as I counseled in a previous article, insurers should follow Peter Drucker’s advice on innovation:

“Don’t subscribe to romantic theories of innovation that depend on ‘flashes of genius.’ Innovation begins with the analysis of opportunities. The search has to be organized and must be done on a regular, systematic basis.”

How might an organized, systematic approach to explore and leverage ChatGPT and other AI capabilities look? Here’s the outline of a three-stage approach that builds on many of the ideas that I’ve offered in previous IIS research articles.

1. Think Big

Thinking big means considering a full range of possibilities. Rather than accepting the widespread exuberance (or the emerging backlash) surrounding AI, make sure to understand your strategic innovation options in the context of your company’s particular markets, strategy and key business drivers. Consider both incremental and disruptive possibilities, including how developments might change customer preferences, enable new markets and upend business dynamics. Be willing to start from a clean sheet of paper. Don’t be too proud to explore downside and even doomsday scenarios, including how new developments might alienate your customers, dent your existing advantages and challenge your existing products.

What might be opportunities, for example, to automate repetitive tasks, help underwriters make better-informed decisions, improve the accuracy and speed of claims processing and enhance customer experiences? What are applicable lessons to take from Lemonade, which uses AI to automate the underwriting process and to handle claims processing. It also uses chatbots to communicate with customers, providing quick and convenient service. Allstate is using AI to prioritize claims for expedited handling. Metromile has long used AI algorithms to analyze driving data to help determine a customer's monthly insurance rate, offering more affordable insurance options to low-mileage drivers. Metromile’s AI capabilities were a key factor in its acquisition by Lemonade. Fraud detection is another area where AI is having a significant impact. AI algorithms can analyze large amounts of data, including claims data, policyholder data and social media, to identify unusual patterns and behavior that may indicate fraud.

See also: When You Have Too Many Good Innovation Ideas

AI might also be used to better address very large “change the world” kind of growth opportunities, such as in cyber-security and climate change. For example, AI algorithms can analyze large amounts of data to identify potential cyber risks, such as vulnerabilities in software and hardware, or unusual network traffic patterns. Insurers can use this information to help their clients better understand their cyber risk profiles and to develop more effective cybersecurity procedures, policies and products. AI algorithms can analyze vast amounts of data, such as weather patterns, satellite imagery and historical claims data, to identify patterns and trends that may indicate an increased risk of natural disasters. This information can be used to improve climate risk assessments and to develop more accurate pricing for insurance products.

It is important to explore not only the upsides but also the downsides and fault lines of this technology. AI tools are clearly powerful, but they also can inadvertently perpetuate bias due to flawed data or poorly designed algorithms. They can stimulate concerns about privacy, misuse of data and nefarious use by bad actors. And AI can provoke regulatory and customer backlash about automated rather than human-based decision making.

2. Start Small

Don’t fall in love with the ideas that come out of the “think big” stage. Resist the tendency of jumping on the bandwagon and going “all in” on one or two seemingly big ideas. Instead, break them down into smaller pieces and run those opportunities through a rigorous screening and experimental process, such as the one I proposed in my previous IIS article.

In the terminology of innovation portfolio management, the key to starting small is to pare all those potentially good ideas into a small basket of “option-creating investments” (OCI). OCIs are the earliest stage innovation initiatives. Companies typically pursue them to explore business opportunities that might yield large returns in the future. They resemble financial options—from which the name is derived—in that they are relatively low-cost, exhibit high risk and offer tremendously high potential returns but do not commit the organization to full-scale implementation.

It is important not to pursue too many OCIs. That’s because even though such efforts should be relatively low in cost, they will demand a lot of senior management attention. No truly disruptive innovation can succeed without sufficient  senior executive attention to protect the efforts from corporate antibodies as necessary, to terminate them when they don’t pan out and to allocate the considerable resources for scaling if appropriate.

3. Learn Fast

After thinking big and starting small, aim to “learn fast” by taking a scientific approach to innovation.

Many companies think that experimentation consists of setting up pilot projects to validate concepts before they’re rolled out. But this is a dangerous approach. Pilots aren’t the answer, at least not on their own. Once something gets anointed as a “pilot,” it’s no longer an option—it’s the destination. Pilots are typically touted as “the answer,” rather than the testing of a possible answer. Assumptions are inevitably made that the pilot will work and will be quickly rolled out at scale. Senior executives invest political capital, often promising eventual success to the board, investors and the press. Team members are judged based on the pilot’s success or failure. All this means that pilots are typically overbuilt. There are no graceful ways to kill them. And, even course corrections are hard to make because any significant changes are seen as a sign of defeat.

Instead of jumping into production-quality pilots, you should achieve your early learning with prototypes that are all chewing gum and baling wire. The difference between a prototype and a pilot is there’s no possibility or expectation that a prototype will turn into a final version of the product or service. Prototypes are just tests to explore key questions, such as whether the technology will work, whether the product concept will meet customer needs or whether customers will prefer it over the competitive alternatives. Prototypes should aim to provide real insight that informs further learning and development. Each stage should minimize costs and maximize flexibility.

See also: AI Offers Big Step Up in Underwriting

In most cases, you’ll wind up doing several rounds of prototypes as you search for vulnerabilities and reduce uncertainties about technology and product viability. You’re looking for evidence of both potential success and failure, but all you want to learn is whether you know enough to kill the project (let the option expire), to continue exploring (renew the option) or to move forward into the next phase of production (exercise the option).

Discipline is critical — if the results after a round of prototyping are still unclear, be very specific about what issues need to be explored. Then lather, rinse and repeat—until you’ve resolved all the key questions.

Once you’re confident that you’re ready to exercise your option and proceed in a specific direction, don’t be tempted to go straight to full production and rollout. That can get you into trouble. Instead, this is the time to do a pilot.

* * *

Thinking big, starting small and learning fast won’t guarantee success. AI encompasses a complex set of technologies and capabilities at the advanced edges of computing. Figuring out your company’s strategy in the context of the immense opportunities and challenges that AI amplifies will be challenging, as it will be for your competitors. In the end, business is a contact sport. Some companies win; some companies lose. Taking a disciplined, long-game approach to innovation will put you in best position to win.

Independent insurance agents constantly face the challenge of finding new prospects to grow their agency's business. While casting a wide net may be appropriate in some circumstances, strategic targeting of commercial sectors with consistent hit ratios can lead to more efficient prospecting and, ultimately, higher conversion rates.

Having been a commercial lines producer for 25 years, I realize identifying industries with consistent hit ratios and prospecting to them can be challenging, but it is crucial for building a robust book of business. During my time as an agent, I developed specializations in retail auto parts and private colleges and universities in Kentucky. This enabled me to concentrate my marketing efforts and outreach toward these sectors. Through this focused approach, I gained a deep understanding of these industries, establishing myself as an expert in the field.

In my role at AHA Insurance Network, I conduct a two-day course designed for agents who want to specialize in writing small commercial business. A part of this program is dedicated to educating them on prospecting for industries with consistent hit ratios. I have highlighted some of the same course topics here and offer insights on identifying potential clients and industries with the most reliable hit ratios, as well as common pitfalls to avoid.

Industries With High Hit Ratios

In a highly competitive landscape, some agents may think targeting niche industries, such as golf courses, will be the remedy. However, this may not provide a long-term solution, as the market can be limited by your geography. Regardless of your location, there is an abundance of contractors such as electricians, plumbers, HVAC technicians and excavators who are vital to any construction or repair project. Therefore, I recommend pursuing contractors, especially artisan contractors who are physically present at the job site. Unlike general contractors, who outsource the work, artisan contractors are favored by insurance carriers because they are more regulated.

Developing a Hit List

Developing a hit list for a specific market typically involves examining carriers and their performance in that market. You'll want to identify the specific class of business you're interested in targeting, such as plumbing contractors, and then gather data on the carriers that write those types of policies. This data might include information on each carrier's market share, loss ratio and hit ratio in that market.

Once you have this information, look for carriers with a hit ratio of 30% or higher in that market. These are the carriers most likely to be successful in writing policies for that class of business. Once you compile a list of these companies, along with any other relevant information, such as their contact information, underwriting guidelines and any unique features of their policies, you’re ready to begin your outreach.

Developing a hit list requires access to comprehensive data on carriers and their performance in specific markets. This information can be obtained from a variety of sources, including industry reports, market research firms and insurance industry associations. Some insurance agencies also have access to proprietary data that can help inform their hit list development.

AHA Insurance Network members can request a lead list for specific classes in certain counties or ZIP codes. Members can review the lists to identify carriers they already use for direct appointments as well as the 10 most promising classes. Alternatively, agents can explore online directories or professional associations to identify potential clients within specific industries.

Building Relationships

If you want to excel in a specific industry, it's crucial to network within that industry and join the relevant associations. This strategy worked well for me when I managed an agency serving the automotive retail industry, particularly the independently owned auto parts stores affiliated with Carquest. I attended conferences and meetings to establish myself as the go-to insurance expert for these accounts by connecting with the district manager of this company and getting involved with the Kentucky Indiana Automotive Wholesalers Association.

This approach requires regular, active engagement in associations at the local, regional and national levels. Find a carrier that will collaborate with you and become well-versed in the industry’s language and nuances. This approach enables agents to position themselves as experts or at least come close to being one. 

See also: The Future of the Independent Agent

Staying Current on Industry Trends

It is crucial to regularly read trade publications and use online resources to stay current on industry changes, such as the recent change in Indiana regarding underground gas storage tanks. In Indiana, gas station owners are now required to have pollution insurance, whereas it is not required in Kentucky. This knowledge is essential when assessing an account to make sure it has the necessary coverage. An agent can remain ahead of the game and competitive by staying informed through trade publications, carrier newsletters and meetings with underwriters and marketing reps.

Mistakes to Avoid

It’s great to have written almost all the golf courses within a geographic area. But then what? Independent agents need to expand their reach beyond a certain geography or sector to continue growing the business. This might involve expanding the geographic area or including another class of business. It's important to ensure there are enough prospects to sustain an agency over the long term and not just in the immediate future.

Final Thoughts

For generalists, which is probably the case for most independent insurance agents, the key is to never stop learning. Find a mentor and develop relationships with underwriters on both the E&S and standard carrier side. Don't be afraid to ask questions of a mentor, marketing reps and carrier underwriters. They are amazing resources for information and guidance. Everything is constantly changing in commercial lines insurance, so asking questions and learning as much as possible is essential.

Independent insurance agents face many challenges when it comes to developing a robust book of business. With the right strategies and a focus on industries with consistent hit ratios, success in the small commercial space is attainable. Agents can position themselves as experts and grow their businesses in a sustainable way by developing specializations, building hit lists, networking within industries and staying current on trends.

Remember, success is not just about casting a wide net but about targeting the right prospects and cultivating meaningful relationships.

Amid the buzz surrounding ChatGPT, it’s helpful to consider another application of AI that seems to have hit a wall: self-driving vehicles. 

Google’s Waymo unveiled the first driverless car in late 2015, and so many bullish predictions followed. For example, Lyft President John Zimmer predicted half the company’s rides would happen in fully autonomous cars by 2020. 

What’s the reality? A recent expert panel discussion in the Wall Street Journal pegged the commercial availability of Level 5 self-driving vehicles (i.e., zero human input required, zero restrictions) at 2035. 

Progress has been made. Today, over 60% of cars sold in the U.S. have Level 2 capabilities, equipped with dynamic cruise and lane assist (Tesla’s Autopilot is Level 2).

A question worth asking is: What is the Level 2 reality of AI in insurance today?  

The first use case is the automation of rote work that people--especially young people--don’t want to do, at seemingly any price. American Family, for example, raised their minimum wage to $23 an hour for mid- and back-office positions, in moderately priced Wisconsin. And they’re automating in response. 

The second use case is telematics and AI on the product side. Progressive’s pioneering Snapshot usage-based auto product is the leading example here. Another is Hartford Steam Boiler’s use of sensor data and AI to help predict machine breakdown and prevent the costly freezing and bursting of pipes.     

A third use case emerging is operational intelligence.   

What’s really going on in that sea of cubicles? What’s really working in work from home? A landmark study from IDC asserts inefficiency costs the average business 20% to 30% of revenue annually. Unclear boundaries, poor system integration, complacency and redundancy are the culprits, and they exist because leaders lack insights into them. If you can’t measure it, you can’t manage it.  

With platforms such as ours at Skan, combining computer vision and AI, measurable insights have arrived. 

Viewed one way, digitally observing humans and the tools they use as they’re using them is about Big Brother surveillance. Viewed another way, it’s about player stats toward continuous player and team improvement. As analytics have affected every major sport, operational intelligence is changing insurance by creating new ways to evaluate, build and manage human teams.    

See also: The Key to 'Augmented Intelligence'

Operational intelligence also enables new modes of work. A younger (or any) person may not be interested in processing claims for $23 an hour, but they seem interested in a gamified version of claims processing where they get paid for results, outputs, competing against their peers in the next cubicle or 10 states or an ocean away. Maybe the solution to the rising hourly wage problem is doing away with the hourly wage? 

New ways to manage, new ways to work. As Progressive uses AI to transform driver intelligence into new premium and profit, insurers are using AI to transform operational and worker intelligence into new margin.  

Come 2035, we may indeed have Level 5 self-driving cars, as well as Level 5 claims processers and sales agents operating at new levels of efficiency and effectiveness. Until then, let’s use the tools at hand to empower humans to find new ways to manage and work in service to other humans.

Let’s focus on replacing time-based cultures with cultures based on performance underpinned by intelligence. Let’s create modes of work that are more engaging and ultimately rewarding for the employee and the enterprise.

It’s challenging for insurers to differentiate themselves through products and services, but data and analytics can enable them to break free from commoditization with better and faster decisions. This potential is reflected in increasing levels of investment in data and analytics across the industry; these investments now exceed 0.7% of direct written premium (DWP), on average. Recent Aite-Novarica Group studies indicate that over half of all insurers are replacing or conducting major enhancements to their data environments and associated capabilities.

According to an Aite-Novarica Group survey of insurer CIOs, the average property/casualty insurer is spending 4.4% of DWP on IT. Of that, 17% is spent on data, resulting in average spending of 0.68% of DWP. (For more information, see the Aite-Novarica Group report Property/Casualty Insurer IT Budgets and Projects 2023.) The term “data” typically includes data warehouses, business intelligence (BI), predictive analytics, third-party data and practices such as master data management.

Where Insurers Are Investing

Insurer business units are prioritizing further expansion of BI and analytics over all other capabilities in 2023, reflecting the fact that carrier capabilities tend to lag in this area. Many insurers face data access challenges, resulting in significant effort expended for sourcing and merging data to produce reports. Data quality issues also affect insurers’ ability to receive the full benefit from analytics efforts. 

When asked to self-assess on various system capabilities, insurer CIOs indicated that the biggest gaps are in data and digital (which has a significant reliance on data). For larger property/casualty insurers, customer relationship management (CRM) is emerging as an area of concern; more than half of insurers rated their capabilities for CRM as “poor” or worse, up from over 40% last year. CRM systems are highly reliant on access to quality data. For midsize property/casualty insurers, customer portals and predictive analytics remain pain points.

To achieve their data objectives, insurers will need to improve their data maturity. Creating and maintaining a trusted source of data and analytics within an insurer requires competency and coordination across multiple disciplines. Insurers with mature data capabilities have access to unique insights and apply them in all aspects of their business to further competitive advantage. 

The True Meaning of “Data Maturity”

Aite-Novarica Group has developed the Insurance Data and Analytics Maturity Model (iDAMM) to facilitate an insurer’s journey to high levels of data maturity. The iDAMM provides a framework for insurers to evaluate their data-related capabilities and establish a plan to reach data maturity by assessing their data organization and capabilities across seven dimensions and 21 subdimensions, including leadership and organization, data governance and architecture and technology management.

The model uses three stages of maturity: Traditional, Evolving and Transforming. Insurers are likely to have different levels of maturity across different model elements. Moving into a more mature stage is a function of organizational and technological capability, not duration. Like transformations in other parts of the insurer, data and analytics innovation requires enabling technology, organizational change and executive sponsorship.

Sustaining data mastery requires an insurer to establish and maintain a data culture. Data cultures are those in which data is fully democratized, data literacy is high and tactical and strategic decision making are largely data-driven (but still informed by intuition). Further, all users value data highly and act as corporate data stewards in maintaining high data quality and protection.

See also: Achieving a 'Logical Data Fabric'

Building a Data Culture

Building a data culture is challenging. Insurers that have achieved data mastery will have a culture grounded in data and analytics that can survive leadership changes and shifts in business focus. This means that everyone cares about data quality, as everyone understands innately the value of information and insights. Strategic and tactical decisions are heavily influenced by signals in the data, though intuition developed through industry experience also plays a role.

Insurance industry data masters derive significantly more value from their data than other insurers, putting them at a major competitive advantage. Insurers that wish to achieve and sustain data mastery should assess where they are in their data and analytics journey, define a target state and plan investments and initiatives to bridge the gaps between them. The iDAMM is a useful tool to assess an organization’s current level of maturity and align that with where the organization seeks to grow.

For more information on achieving data maturity, see Aite-Novarica Group’s new report Establishing and Sustaining Data Mastery: Introducing the Insurance Data & Analytics Maturity Model (iDAMM).