Did you hear that sound? It could be opportunity knocking. Despite the slowdown in merger and acquisition activity in the first part of 2023, the climate for growth in the insurance sector is promising, now and into 2024. The chance to grow or sell a business can present itself quickly, so it’s wise to be prepared. How can you be ready when opportunity comes knocking? 

Outlook for M&A in insurance

The early-year turmoil in the banking industry, due in part to rapid acceleration in interest rates since March 2022, had a cooling effect on M&A activity in the first half of 2023. Many signs, however, point to a potentially better atmosphere for deal-making. The long-predicted recession still has not occurred, inflation is cooling and mortgage interest rates have declined. In the insurance sector, deals continue to be made, even in this challenging environment. PwC describes insurance M&A activity as resilient despite macroeconomic headwinds and predicts insurance deal activity to remain very active through the remainder of the year. 

See also: Combating Healthcare Insurance Fraud

How to be ready when opportunity knocks

If expanding or selling your agency is in your future, it makes sense to prepare yourself and your business so you can act quickly if the right opportunity arises. Here are five key steps that will help you to be ready.

1. Review and reflect on what your business needs. Do you have strong producers and enough of them? Are your technology tools – and the staff who manage them – up to date? Is there an area of service you’d like to expand into but haven’t had the capacity or talent pool to handle? Having a wish list of attributes to look for, including staff with specific skill sets, will make it easier to recognize a good acquisition target.

2. Be conscious of your company culture and values. Insurance is relationship-based, so working with people who share your company’s ethics and approach to business is vital. Whether you want to acquire or sell, finding the complementary fit for current and future employees will lead to a more successful transition. 

3. Have your house in order. Nothing can sink a deal faster than incomplete or inaccurate financial records or problems with regulatory compliance. Lenders like to say, “Run your business every day as if you’re going to sell it.” Make sure that all records are up to date and complete, and that you’re in full compliance with all industry regulations. It will save time, money and headaches in the long run.

4. Maintain a strong cash position. In insurance deals, the majority of the valuation in a deal is cash flow, rather than physical assets. If you’re a seller, being able to demonstrate a strong and predictable cash flow makes your business an inviting target for acquisition. If you’re looking to buy, being in a liquid position can help facilitate a deal quickly.

5. Stay in close contact with your lender. It’s never too early to let your lender know you are looking for opportunities to buy or sell, even if you don’t have a specific deal identified. Your lender can help you get a general deal structure in place so you can respond quickly when a potential acquisition target becomes available. Setting up a structure also reduces the chance of surprises arising that could derail the deal.

If you’re intending to sell, especially as part of an internal succession plan, you may need to hold some of the debt. Lenders can help put together the bones of a deal that can be fleshed out over time as your plans become more definite. The length of time a deal takes is almost always up to the borrower and how quickly they respond to requests for information and documentation. Communication is critical.

While market forces – unemployment, inflation, the possibility of recession and interest rates – are always uncertain, opportunities for growth are always available. The temporary slowdown in M&A deals of the past several months means there is capital out there waiting to be deployed.

Whether you’re looking to buy, sell or facilitate succession, following management best practices and staying in close communication with your lending partner will help you meet opportunity when it comes knocking.

Whether the sport is football, soccer, basketball or even eSports, all have one thing in common — they require insurance. 

While policies for participants need to be considered separately from the policies for the teams and the leagues, in the end, nearly every aspect of sport is insurable. Some policies are straightforward. Some policies are unique to sports. And some policies are becoming harder to find. 

Business of Sports 

While the on-field activities get most of the attention, an army of professionals works behind the scenes to conduct the business of sports. All of those business tasks must be insured as they would be for any other business. 

These policies begin with the team’s general liability and umbrella policies. These cover the team if someone gets hurt on team property, at a game or during an event.

One high-profile case came up earlier this year when a New England Patriots fan died in the stands after getting punched by an opposing team’s fan.  

It looks like the initial autopsy is showing that the punch was not what caused the death. But say it did. In a case like this, it is easy to imagine the stadium being on the hook for a lawsuit. Lawyers would almost certainly claim that the venue didn’t provide enough security or oversight, or that one of the people involved in the fight was overserved alcohol. 

The general liability policy would likely pay up to defend the case and potentially pay any eventual settlement or judgment. 

The front office also has to buy the other policies any other business would, such as health insurance for the coaches and business staff, (but typically not the players). The same goes for workers' compensation. 

Teams and venues also carry property insurance in case something like a hurricane rips the roof of a stadium. 

And in that case, the team’s business interruption insurance would kick in and help the team relocate and help them find another venue to play in while the home venue is being repaired. Insurance might also cover the inevitable loss of income between what could be earned at a home stadium and the adopted home. 

Post-COVID, these business interruption policies typically have exclusions for infectious diseases. 

Players' Health Insurance

On the players' side, the health insurance policies tend to be collectively bargained, so the players have a separate policy from the rest of the organization. Every player in the league would have the same health insurance, and when they change teams, they stay under the same program.

Workers’ compensation is also complicated for players. Because they are technically working when they are playing, there needs to be a policy in case they are injured on the job. There are very few workers’ compensation policies willing to take on the risk of player injury, meaning, in most leagues, the policy is negotiated and purchased leaguewide. 

Players’ Salary Insurance 

One of the policies that is perhaps unique to sports centers on players’ salaries. 

Each league approaches these policies differently based on their collective bargaining agreements. 

For example, in the NBA and NHL, player contracts are guaranteed. If a player sustains a career-ending injury a week after signing a 10-year contract, the player is still paid a full salary for the duration of the contract.

In these cases, teams can get disability policies for players. They come with big deductibles and really only cover the team in the event of a season- or career-ending injury. They are also expensive, so teams don’t insure everyone on their payroll — just the top players. 

The NFL is a little different because their collective bargaining agreement lets teams cut players before the end of their contract. Many contracts have a certain portion guaranteed, but outside of that, if a team cuts someone a year into a five-year contract, the remaining four years don’t have to be paid out.

Subsequently, not many NFL teams insure contracts outside of unusually large guarantees or signing bonuses. 

Specialized Disability Insurance

When it comes to specialized cases, there are disability policies covering an athlete’s dominant arm or legs, for example. 

These policies are typically custom-written by large insurance syndicates.  

The cost and availability for these specialized policies really depend on the athlete, their income, their sport and any pre-existing conditions and inherent risks they face, said Mark Di Perno, president of Sportunderwriters.com. 

Players can also protect themselves from off-field injuries through so-called 24-hour disability policies. These would protect the athlete if they were injured outside a sporting event, such as during a ski trip. 

In sports such as the NFL, individual athletes often purchase policies to protect themselves in case they are hurt and their team cuts them rather than paying out the remainder of their contract. 

Women-Focused Coverage

Women playing professional sports face a unique set of challenges, which at least one insurer has developed a policy to address. 

Willis Towers Watson has introduced their women in sports coverage, which evolved from the existing professional sports policies but was tailored to meet the demands and needs of women. 

These policies offer additional benefits, including for pregnancy complications that stem from an accident due to training or playing, as well as a post-partum benefit to aid the player in returning to their profession, said Tiffany Peña Santos, senior associate, accident and health broker for WTW. 

The women-focused plans also include mental health support, including post- and pre-natal depression, and a benefit that helps women retrain in another field of work if they have a career-ending injury. 

The polices even offer assistance with childcare expenses. 

“We can’t forget the fact that there is a large pay gap in the sports industry,” Peña Santos said. “Elite male footballers, for example, would be unlikely to have childcare bursary included in their insurance policies, whereas elite female footballers may need this to get back on the pitch.”

2024 and beyond

The hottest issue facing sports insurance right now seems to be head injuries. 

There have been thousands of lawsuits filed regarding concussions and traumatic brain injuries in nearly every contact sport. 

In the highest-profile case to date, the NFL settled a class action lawsuit for more than $1 billion in 2013, which many in the industry believe marks more of a harbinger of things to come than it does the end of an issue. 

Subsequently, many insurers are either not quoting policies or they are including a traumatic brain injury exclusion or coverage limitation. 

There are still a handful of policies available with exposure to head injuries, but many experts have wondered out loud whether the industry can sustain the long tail of claims coming from these costly payouts. And without insurance for head injuries, many wonder what the future for contact sports could hold.

KEY TAKEAWAYS:

--Generative AI can improve auto insurers' claims processes, optimize customer interactions and recommend plan changes. 

--Some companies are slow to adopt because of inaccuracies and bias issues, privacy challenges, intellectual property concerns and heightened regulatory scrutiny.

--Like any new technology, generative AI needs effective guardrails. Once the right procedures are in place, it can provide value across the customer lifecycle for auto insurance companies.  

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As an insurance professional, you’ve likely watched the rise of generative AI. But has your employer taken advantage of generative AI yet? If not, maybe they should be.

Although auto insurance professionals have used AI for decades — particularly in the form of machine learning and predictive modeling — generative AI is a new frontier. By creating original content using patterns from existing data sources, generative AI tools like ChatGPT and Bard offer endless applications for auto insurance professionals. Used effectively, generative AI tools can help auto insurers create highly personalized programs that cater to customers’ unique needs, building stronger relationships and increasing efficiency in the handling of common tasks.

But as with other emerging technology solutions, adoption of generative AI isn’t a linear path. 

The concerns about potential inaccuracies and imaginative limitations in using these tools are causing decision-makers at auto insurance companies to hesitate to fully embrace the technology.

Ultimately, ChatGPT is like a driver-assisted autonomous vehicle. It requires a driver willing to invest in its possibilities of efficiency and personalization, unafraid to sit at the forefront of technology innovation. At the same time, however, these tools also require a driver who is prepared to assess outputs and intervene when necessary — a human at the wheel of a new technology always performs better than the technology on its own. 

Why generative AI is worth your effort: Optimizing the customer lifecycle 

So far, auto insurance companies have only scratched the surface of generative AI’s potential. Even industry leaders that have already started using generative AI can benefit from being more open-minded and creative about how they can use technology to improve future operations. 

For example, generative AI tools can create automation and back office efficiencies by summarizing and synthesizing common insurance content and data. You can use these capabilities to speed up marketing content delivery, code generation, training and other documentation resources.

However, generative AI’s true power lies in its ability to deliver value to your customers — helping improve your customer experience, as well as your acquisition and retention metrics. 

When you layer generative AI into your customer interactions, you can: 

1. Improve claims processes: Generative AI can streamline your customer claims process by extracting and categorizing information from claim documents and other data sources, such as driving data before and after a crash. While this process might require customized generative AI models, the investment can be worthwhile because of the volumes of unstructured claims data insurance adjusters must manually organize. Beyond organizing this claim information more quickly and effectively, you can also use generative AI to personalize messages based on the claims information customers provide. This is a win-win: The technology improves the customer experience, reduces the claims adjudication cycle time and enhances your brand reputation.
2. Optimize customer service interactions: By combining a generative AI virtual assistant with insights about your drivers, you enable more personalized, efficient customer interactions. For example, a generative AI chatbot can automate contact center interactions based on information about your customers’ needs, driving behavior and coverage, providing customers with relevant, personalized answers and information — and saving you time. 
3. Recommend plan coverages: Coverage selection can be an overwhelming experience for auto insurance shoppers. Generative AI helps you analyze customer information and driving data to generate personalized insurance policy recommendations and accelerate the selling process with increased bind rates. The result? You simplify shopping experiences and allow consumers to pay for what they need, using generative AI to personalize suggestions with a customer’s unique needs, lifestyle and risk profile at the center.

While these are three prominent use cases, there are many more applications of generative AI, including risk assessment, fraud detection, trend prediction and modeling. 

What’s preventing auto insurance companies from fully embracing generative AI? 

If generative AI boasts these benefits for auto insurers, why has industry adoption been slow?

Risk aversion, regulatory issues, competing priorities and the novelty of generative AI have all prevented auto insurance companies from incorporating generative AI solutions in their marketing, claims and sales efforts. For starters, a lack of understanding among decision-makers and an absence of in-house generative AI expertise may prevent many businesses from taking advantage of the technology. 

And like many other white-collar workers, auto insurer employees may worry about losing their jobs to automation. As a result, they may hesitate to rely on generative AI solutions. It’s important to remember that generative AI tools are still nascent technology. So, it’s understandable that auto insurance employees are risk averse toward the technology and haven’t yet taken advantage of it in their work. At the same time, it’s important to remember that AI can augment an employee’s tasks without fully replacing their entire position — these technologies need a human at the wheel. 

Leadership teams at auto insurance companies must be prepared to address the concerns that have arisen around the use of generative AI tools. Although one in six Americans have used generative AI, most view AI unfavorably, leading to the distrust of tools like ChatGPT. Some of the skepticism about generative AI in insurance is justified because it can introduce biases, inaccuracies ,and security risks. Commonly cited risks include:

• Inaccuracy, ethics and bias issues: Generative AI models are only as accurate as the data they’ve been trained on and some can “hallucinate” inaccurate information. Left unchecked, generative AI models may reference and even propagate offensive and controversial content. As state legislatures move to ban the use of credit-based scoring algorithms and auto insurers work to combat bias in the quoting process, decision-makers at auto insurance companies will need to remain vigilant about this tendency when bringing on generative AI solutions, offering clear guidelines and best practices to employees. 
• Privacy, security and confidentiality challenges: Your generative AI technology vendor may store user data after intake, which can lead to hacks, leaked information or personal details accidentally being made public. This presents a risk to auto insurance companies that hold sensitive personal data about drivers.
• Intellectual property concerns: Generative AI tools may produce computer code and other work not protectable by your existing IP rights, such as copyrights and patents. 
• Heightened regulatory scrutiny: As more auto insurance companies take advantage of AI, the NAIC is monitoring its usage and considering regulations. 

These risks may sound overwhelming at first. However, delaying adoption can increase distrust and cause businesses to fall further behind. Companies that shy away from generative AI tools now might trail competitors using these tools to reduce expenses and improve customer experiences. So it’s best for leadership teams at auto insurance companies to focus on adapting to these tools now by investing in reskilling and retraining while setting up the right guardrails and security measures.

Take full advantage of AI with a governance strategy

To address generative AI concerns and take advantage of its benefits, your organization can start small with clear guardrails and then adopt and mature a governance strategy. From there, you can monitor regulatory changes, collect employee and customer feedback and use any early learnings to inform and shape your strategy over time. 

Regardless of your strategy, human involvement and oversight are critical as your organization adopts generative AI. Your teams should carefully review inputs and outputs for accuracy, fairness and bias. You’re more likely to generate usable results from a generative AI tool if the input is accurate — like real driving data — or if your data is peer-validated. While it might be easier said than done given the potential scope of inputs and outputs found across insurance spaces, it’s worth the effort.

Likewise, when consumers know exactly how and when their data is being used and how it benefits them, carriers can more effectively use data to create personalized auto insurance programs. Transparency around data usage and human involvement in generative AI benefits everyone involved. 

The takeaway: Like any new technology, generative AI needs effective guardrails. Once the right procedures are in place, it can provide value across the customer lifecycle for auto insurance companies. 

With these wheels, generative AI gets you where you want to go

Your imagination is the limit when it comes to generative AI, which can be both an opportunity and a hindrance. 

If your business opts to invest in generative AI, you have the power to define how to responsibly use generative AI tools to improve experiences for your customers, provide increased business value and offer more efficient processes to employees. But the process starts with making sure you have the right inputs, like accurate and timely driving data, and ensuring you have guardrails in place.

Dream big and make sure a driver is always behind the wheel of your generative AI technology.

While Scott Van Pelt opens his version of "SportsCenter" on ESPN with "The Best Thing I Saw Today," I can't limit myself to just one thing here. I've seen a whole bunch of smart things over the past week that I'd like to share as a sort of grab bag. 

Several relate to AI -- even though we all feel like we're inundated with news about the field. The one that could be most important for insurance concerns what may be a breakthrough in how we forecast major storms -- three models based on a new approach were extremely accurate in forecasting the path of Hurricane Lee in mid-September, beginning when the storm was thousands of miles from North America. 

As the Washington Post reported, "The models are orders of magnitude faster and cheaper to operate than conventional, government-run weather models. While AI models don’t yet provide all the capabilities needed for operational forecasting, their emergence portends a potential sea change in how weather forecasts are made."

Let's have a look. 

The new models take me back to the late 1980s and into the 1990s, when "expert systems" were the cutting edge in AI. The basic idea was that you find an expert and build a system around that person's (or those persons') expertise. You ask or observe how that stock trader, or plant operator or whomever made decisions in as many circumstances as you could imagine, then build software that would make those same decisions in those same circumstances. The problem was that you could never imagine all the circumstances or draw all the expertise out of the person, who often had developed gut instincts over time that they'd act on in, say, a stock market crash but that they wouldn't know how to articulate ahead of time. So the limits of so-called rules-based systems became clear.

The breakthrough came when computing power became so plentiful that AIs could be turned loose to simulate a nearly unlimited set of possibilities and to see what responses were optimal. This simulation approach is what has led, for instance, to the AIs that have defeated the world's best Go players. The initial AI was a bit of a hybrid -- it built on a base of human expertise. That was then surpassed by an AI that was simply given the rules of the game and learned by playing billions of games against itself. That, in turn, has now been surpassed by an AI that started without even being given the rules as it started its simulations.

Go players talk with reverence about Move 37 in game two of a series that an AI, AlphaGo, played against Lee Sedol, a top-ranked player, in a series that the AI won 4-1. The AI's move went against all the precepts of Go that have been taught for centuries but, analysis now shows, was brilliant.

The switch from expert systems to endless simulations and machine learning is, very roughly speaking, the change that may be beginning with weather forecasting for major storms.

At the moment, the two main approaches -- one developed in Europe, one in the U.S. -- operate based on data and knowledge that has been collected and developed over decades and that has been turned into extremely elaborate models. A supercomputer needs perhaps an hour (and a lot of electricity) to conduct trillions of calculations and turn those formulas into a forecast about the path and intensity of a hurricane.

The new models -- produced by Google, Microsoft, Nvidia, Huawei and a number of startups -- start with the extensive data on weather conditions that are collected for the supercomputer-based models but ignore all the formulas that the supercomputers then use to generate forecasts. The new models are built on deep analysis of decades of prior weather data and, based on the patterns discerned, can produce a forecast on a desktop computer in a minute, or even seconds.

In the case of Hurricane Lee, the new models accurately predicted on Sept. 10 that it would make landfall in Nova Scotia six days later and were ahead of the established models in suggesting that the hurricane might travel close enough to Cape Cod to produce severe weather. 

The hope is that the speed and low cost of operating the new models will also allow for what are called ensemble forecasts. The new models could be used to generate a whole series of forecasts based on slight variations in the weather data it's fed -- which can be imprecise -- and generate a range of forecasts that would provide a more robust look at how a storm might behave. 

"Ensemble forecasts from conventional models can miss extreme events, such as excessive rainfall or heat, because they are limited to about 50 simulations due to the time and cost of generating them," the Washington Post article said. "AI could enable the generation of much larger ensembles in as little as a few minutes, potentially leading to more useful forecasts and risk assessments for emergency managers, the general public and numerous industries.

"'Our hypothesis is we can easily now scale up with AI models to thousands or tens of thousands of ensemble members,' Anima Anandkumar, senior director of AI Research at Nvidia, said in an interview."

Given the normal trajectory of technology, I can imagine this new sort of model moving from hurricanes to other sorts of severe storms, including tornados and derechos, which have historically been less predictable. In time, I could even imagine these models being used to warn of the sorts of severe thunderstorms that dumped 25 inches of rain on Ft. Lauderdale in April and that hit the New York City area with as much as 10 inches of rain last week. This sort of severe storm is a relatively new phenomenon, apparently related to high temperatures in nearby ocean waters, but AI could well recognize the signs that what looks like a routine thunderstorm could actually last for many hours.

One storm obviously doesn't prove anything about the new models, but they're off to a good start, and AI tends to improve rapidly once it gets its arms -- brain? -- around something. Any improvement in forecasting does, of course, increase the odds that people can protect themselves and their property, assisted by their insurers.

That explanation took longer than I expected, but let's still get to the other smart things that caught my eye in the past week:

--The clearest example I've yet seen of ROI from generative AI, from an interview in Fortune with Erik Brynjolfsson: 

"In a study that colleagues and I conducted, a company with a call center did a phased rollout of a large language model—generative AI—that gave suggestions to some of the workers [as they responded to callers], but not to others. So we got a kind of controlled experiment. The people who had access to the technology were dramatically more productive. It was about 14% on average, but the least experienced workers were about 35% more productive within just a couple of months: a big, big change.... Customer satisfaction dramatically improved.... The employees seemed happy. They were less likely to quit—much less turnover."

--A smart framework for thinking about investments in generative AI, from my longtime friend and colleague Tim Andrews. He says we are still in the Institutional phase of his i3 model, characterized "by the need to go to large institutions for access." Next will come the Individual phase, when "the technology is affordable but not cheap and generally requires some expertise to install and maintain it." Finally, there will be the Invisible stage, where "the technology disappears from view and becomes embedded in just about everything possible....Interest in the technology itself wanes, except when broken or missing....  A lost internet connection is painful because of the interruption of a video call or streaming movie, not because of the underlying internet technology." 

--A mind-boggling stat on the homeowners' insurance crisis, suggesting that we have a long way to go before it's resolved:

"First Street estimates that 39 million U.S. homes are insured at artificially suppressed prices compared with the risk they actually face."

--Five myths about customer loyalty, from Jon Picoult, one of my go-to's on the topic, including:

"Myth #1: Satisfied customers are loyal customers.

"Satisfied customers defect all the time. In a widely cited customer experience study, Gartner found that 20% of customers who said they were satisfied with a particular company also said that they planned to shift their business to another provider. This is why customer satisfaction is really a one-way ticket to the business graveyard. To cultivate true, long-term loyalty, businesses must do more than just satisfy customers – they need to impress them, thereby cultivating the repurchase and referral behavior that is the lifeblood of any thriving company."

Cheers,

Paul

In today’s business landscape, time saved is money saved. Insurance companies must carefully evaluate time-consuming areas where automation can be most effectively implemented. One crucial aspect that demands attention is the billing and payment experience, as it represents one of the few touchpoints insurers have with their customers.

The adoption of paperless billing has gained popularity as a convenient and reliable way to receive communication. By digitizing bill delivery, insurance carriers can save costs associated with printing and mailing, as well as minimize time spent on manual tasks like envelope stuffing. Additionally, this shift towards digital processes contributes to reducing an organization’s environmental impact.

By leveraging these cost-effective measures, insurers can improve efficiency and optimize their financial performance. To estimate potential savings, utilize the Paperless Estimated Savings Calculator, which helps determine the cost benefits of having more policyholders receive their bills online.

Calculate Your Savings

Sponsored by ITL Partner: InvoiceCloud

Check out Majesco’s latest research report to better understand the challenges P&C insurers are facing in today’s pressure packed market. Uncover insightful tips and opportunities to create a competitive edge through new technology and operational strategies. 

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Sponsored by ITL Partner: Majesco

Underwriting is undergoing a major transformation thanks to new technologies like artificial intelligence (AI) and machine learning (ML). For decades, underwriters relied solely on historical data to assess risk and determine coverage. But in today's rapidly changing world, historical data is no longer enough.

To stay competitive, underwriters now need to leverage AI and ML to unlock deep insights from both structured and unstructured data. These technologies allow underwriters to identify risks earlier, price policies smarter, and operate more efficiently.

AI and ML are revolutionizing underwriting across three key areas: automating repetitive tasks, generating real-time insights, and evolving the underwriter's role. With intelligent automation, underwriters can reduce costs, improve customer satisfaction, and adapt to changes faster. The future underwriter will act as custodian, integrator, and collaborator to drive even greater value.

Underwriting Modernization

Insurance companies have been working to automate repetitive underwriting tasks for many years. But new technologies like robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML) are taking underwriting automation to the next level.

RPA tools can be programmed to handle high-volume, rules-based underwriting workflows. This includes gathering data from multiple sources, filling out application forms, verifying information, and routing tasks. By automating these routine processes, RPA systems free up underwriters to focus on complex decision making, improving efficiency and allowing underwriters to process policies faster.

AI and ML inject intelligence into automation by enabling systems to learn, improve, and make complex choices over time. While AI extracts insights from text, ML detects patterns in data that humans might miss.

Used together, these technologies can replicate underwriter expertise at scale in numerous areas:

• Data Collection: Collect diverse data from both structured and unstructured sources to gain a comprehensive view of risk factors. By incorporating real-time data from multiple channels, underwriters can stay on top of emerging exposures.
• Insights: Generate insights from large volumes of data quickly using AI and machine learning. By detecting patterns and correlations, underwriters can better predict potential losses and model risks.
• Risk Selection: Make informed risk selection and pricing decisions tailored to specific cases using granular, AI-enhanced metrics. Automated risk rating incorporates more variables for more accurate coverage and premiums.
• Automating Workflows: Streamline routine tasks like application processing and renewals with conversational AI chatbots. Automating high-volume, repetitive workflows improves underwriter productivity and customer satisfaction.
• Behavioral Analysis: Identify potential misrepresentation and fraud earlier using AI-enabled behavioral analysis techniques. Advanced analytics improve underwriting accuracy and efficiency from the initial review stage.

By streamlining repetitive tasks with intelligent automation, underwriters gain capacity to focus on higher return tasks, operate more nimbly, and drive greater value.

The Exponential Underwriter

Advances in automation are changing the role of the underwriter. With the help of new technologies, underwriters are evolving into "exponential" professionals who can drive greater value. Deloitte calls this the rise of the exponential underwriter — “a multi-skilled professional leveraging new data sources and emerging technologies to be more efficient and proactive in defining future organizational processes.” Intelligent automation empowers underwriters to become “exponential”— adding more value than ever. 

Intelligent automation systems powered by AI and machine learning handle many of the routine, time-intensive underwriting processes including:

• Parsing insurance applications
• Assessing straightforward risks
• Screening applicant information
• Evaluating recommendations provided by underwriting software
• Gathering information from field staff
• Determining premium coverage

Adding AI not only changes the speed at which it’s done but changes the scale at which it’s managed. In fact, an Accenture study found that tech-enabled underwriters can perform higher value tasks and perform better. The exponential underwriter can:

• Complete complex risk assessments
• Evaluate ambiguous or borderline cases
• Make pricing decisions on non-standard policies
• Identify cross-selling opportunities
• Produce quicker turnaround on more standard quotes
• Access knowledge for risk-based decision making
• Rate and price risk better
• Retain existing policyholders 

The exponential underwriter will act as conductor of the underwriting orchestra — leveraging automation while focusing on high-impact areas to drive greater strategic value. AI will help underwriters predict and price risk more accurately, identify operational issues or opportunities in real time, and make more and better-informed decisions. Currently, only 30% of an underwriter’s time is spent assessing risk. The rest of the time is spent collecting, combining, and reviewing documentation for submission. New tech will free up their time enabling them to focus on high-value cases that need human insight and decision making.

The Future of Underwriting

Underwriting is undergoing a transformation driven by new technologies like automation, AI, and advanced analytics. While machines handle routine tasks, the underwriter's role will evolve.

Rather than focusing on specific policy transactions, future underwriters will be custodians of the entire underwriting process. They will oversee and optimize systems that integrate data, analytics, and software recommendations to produce accurate quotes at scale.

Underwriters will become integrated P&L professionals — using technology to underwrite policies in a way that balances risk and revenue judiciously. They will collaborate with systems and tools that enhance their capabilities.

This new, exponential underwriter will complete policies quickly, accurately, and cost-effectively by conducting sophisticated risk assessments augmented by automation. Underwriting will remain human-centered but enhanced by technology.

By embracing the possibilities of future tech, underwriters can focus on high-value analysis while systems automate the rest — driving growth, mitigating risk, and exceeding customer expectations.

Contact us if you would like to learn more about how intelligent automation can drive underwriting transformation. Download the ebook - Beyond the Bot: How AI Is Ushering in the Next Wave of Automation for more in-depth insights on the future of automation

Murray Izenwasser, Senior Vice President, Digital Strategy

At OZ, Murray plays a pivotal role in understanding our clients’ businesses and then determining the best strategies and customer experiences to drive their business forward using real-world digital, marketing, and technology tools. Prior to OZ, Murray held senior positions at some of the world’s largest digital agencies, including Razorfish and Sapient, and co-founded and ran a successful digital engagement and technology agency for 7 years.

Sponsored by ITL Partner: OZ Digital Consulting

Engaging millennial customers has been a strategic priority for insurers. With most millennials in the prime of their careers, they are increasingly focusing on long-term financial goals, health coverage, auto insurance, asset protection and so on. 

As per Statista, millennials constitute the largest generation group in the US with a 72.24 million population - a vast market that insurers cannot ignore.

But millennials come with certain demands. 

• They prefer personalized and customized solutions and products and a transparent approach to guiding them through these solutions
• They are increasingly comfortable using digital platforms and enjoy the ease and convenience it delivers
• They demand quick and seamless services

These needs are driving the transformation of the insurance industry today - with carriers deeply invested in getting their customer experience right and striking the right note with their target demographics. 

While they build omni-channel experiences and seamless hassle-free interactions, they are sinking deep in another crisis:

Attrition of workforce. 

83% of agents quit within the first three years! And with baby boomers retiring; the average age of an agent in the United States is 59. The insurance sector is in the race to find the right talent from the millennial and Gen Z population to steer its course in the coming decade. But it is easier said than done. According to Zippia, the insurance industry turnover for 2021 is 26%. And as Insurance Business magazine says, the industry is unable to hire fast enough to replace the people leaving. 

The reasons for attrition are many: compensation and benefits, work-life balance, the perception of insurance as a traditional, unchanging sector, the lure of attractive tech jobs and more. As insurance carriers rebuild their traditional image and rework their compensation and benefit programs, here are a few gaps they could focus on closing, in parallel. 

• Your workforce is millennial too. The newer agents that are being hired are from the same demographic. So focusing on customer experience is just winning half the battle. Insurers should revamp workforce experience as well. After using intuitive apps and platforms for day-to-day chores like ordering groceries, or books, or laundry pick up - using legacy apps and multiple platforms at work becomes a deterrent. They expect the same ease of use and seamless workflows and processes at work as well. Insurers, if not already, should invest in tech capabilities that can speed up day-to-day operations for their teams.
• Tech can play a role in work-life balance. With aggressive targets and deadlines, agents feel burned out. According to an article in the Independent Agent magazine, the second largest reason for agents to quit the industry is because of poor work-life balance. Tech can be a great enabler in streamlining the day-to-day activities for an agent. For example,
  • Giving them access to mobile-first applications so they don't need to jot down notes in a diary and come back to the office desktop to update it
  • Leveraging tools that can automatically capture their activities, take notes, set up meetings faster and allow them to share information at the click of a button
  • Nudge them on important dates, meetings, reminders etc. so they are more efficient through the day

Building a tech ecosystem that can remove laborious tasks and make them doubly productive in an eight-hour workday can help them check off items on their tasklist and get back to other priorities. 

• A learning environment builds career paths. As younger workforce steps into the industry, insurance carriers must build robust onboarding programs to help build the skills and know-how to be successful insurance sellers. Beyond onboarding, they must build an active learning environment to help agents unlock their potential and grow in their roles. As the tenured agents retire, it is important to capture their experience and best practices to cascade to the newer teams. Establishing coaching networks that pick up best practices and best next steps from the star performers in the team and share with the larger teams can proliferate best selling practices. 

As the insurance industry ushers in a decade of digital transformation, reinvention and sharper customer focus, it becomes equally important to focus on the internal stakeholders and give them a roadmap for growth. A mix of healthy organization culture, compensation, well-rounded benefit programs and the right technology support can pave the way for the ‘great retention’.

Sponsored by ITL Partner: Vymo

The adoption of digital technologies is driving a remarkable transformation in the auto insurance industry, which has long been associated with complex paperwork and lengthy processes. One example is the advent of digital vehicle titling to streamline insurance claims management for total losses and unrecovered thefts, offering convenience and efficiency while providing substantial economic benefits. This new digital solution, based on blockchain, is revolutionizing the industry and benefiting policyholders and insurers alike.

Same-Day Service

In an era of instant gratification, policyholders expect same-day service. Digital titling delivers just that. By automating manual processes and reducing reliance on paperwork, a digital titling platform enables insurance providers to process total loss claims and obtain sellable titles within a day, an amazing improvement from the previous standard of 60 days. Claims documents are delivered promptly, enabling policyholders to make informed decisions and stay up to date during the stressful occasions of a total loss or theft. This increased efficiency and responsiveness enhances customer satisfaction and strengthens insurer-policyholder relationships.

Without the conveniences of digital titling, insured parties are subject to the “time tax,” a phrase coined by the CEO of Champ Titles, Shane Bigelow, to describe the massive time commitment of securing a title with physical paperwork and a DMV visit.

“We need to reduce the time tax that each consumer pays as they wait in line to process paper and deal with DMVs," Bigelow says. "Because each state has unique systems, laws and procedures, a solution that can work with legacy systems but provide a powerful, flexible and, most important, digital system of record is needed.”

Advantages for Insurers

While policyholders benefit significantly from digital titling, insurers win, too. That is because digital titling isn’t just about a digital title but rather about the full digitization of the titling process, which has always been paper-ridden and slow. Costs associated with printing, mailing and physical document storage are eliminated. Automation of processes minimizes manual labor requirements, resulting in operational efficiency and reduced administrative expenses. By removing redundant tasks and streamlining workflows, insurers can reallocate resources to enhance customer service and improve innovation. As a result of carriers getting paid faster, insurance rates decrease. All parties benefit.

In addition to cost savings, insurers can encourage environmentally responsible practices by embracing digital vehicle titling. Currently, each state's motor vehicle department uses an average of 15 million sheets of paper annually for this process. Going digital not only eliminates paper usage but also streamlines end-of-life vehicle processing. Traditional titling often leads to prolonged storage of totaled cars, which may contain hazardous materials like engine fluids and battery acid. Leakage of these substances poses significant threats to ecosystems and groundwater. However, with an expedited digital titling process, carriers can recycle vehicles on the same day, reducing environmental risks and supporting material reuse.

West Virginia Clearinghouse

The launch of West Virginia’s National Digital Titling Clearinghouse on July 1, 2023, is perhaps the biggest leap in vehicle titling in over 20 years. This innovative concept, born from the West Virginia DMV, with technology created by Champ Titles and payments facilitated by Tyler Technologies, acts as a central hub for national entities like insurers and online car retailers to digitally acquire, store and transfer out-of-state vehicle titles—safely, accurately and efficiently from anywhere in the country.

West Virginia Gov. Jim Justice said, "I am excited about this initiative, which will bring millions of dollars to the state of West Virginia and position us as a leader in innovation, as we have been so many times before. I’m proud that West Virginia is the first state that’s setting the template for others to follow. My thanks go out to the legislature and to all those involved at the DMV for making this a reality.”

This groundbreaking platform not only establishes West Virginia as a pioneer in vehicle titling and registration solutions but also serves as a model for other states seeking to streamline their titling processes. By embracing this system, other states can unlock the benefits of improved efficiency, cost reduction and enhanced accessibility.

As digital titling continues to evolve and reshape the insurance industry, both policyholders and insurers can look forward to a future that is more efficient, customer-centric and economically beneficial. These advancements in digital titling are ushering in a new era of streamlined motor vehicle operations and enhanced experiences for everyone involved.

Dr. John Sviokla is co-founder of GAI Insights. He previously was a strategic adviser at Manifold and former senior partner and chief marketing officer of PWC. He has almost 30 years of experience researching, writing and speaking about digital transformation — making it a reality in companies large and small. He has more than 100 publications in many journals, including Sloan Management Review, WSJ and the Financial Times.

Insurance Thought Leadership:

You just put on a big event on generative AI. You also co-wrote a recent article in Harvard Business Review on how businesses should think about it. Could you start us off with an overview of how you’re seeing the key issues?

John Sviokla:

If a big part of your cost base depends on manipulating what I call WINS – words, images, numbers and sounds – your whole business is going to change.

And critical functions will change for lots of other businesses, too. Drug discovery is going to be completely different. IBM and Moderna can look at four orders of magnitude more molecules by using generative AI as opposed to a traditional computational model; instead of 100,000 molecules, they’re now looking at more than a billion. For some companies, marketing will change drastically even if the majority of their cost base is not WINS work.

Every meeting is going to completely change. For one thing, all the meeting notes will be captured automatically, as will all the to do’s and the follow-ups, and the synopsis will be generated by the AI.

We’ve reached a threshold. We haven’t completely passed the Turing test, but we’re pretty close. [Posed by British mathematician and computer scientist Alan Turing in 1950, the Turing test concerned whether a human could tell whether they were conversing with a computer or another person. If the person couldn’t tell the difference, the machine was said to have passed the test.] And everyone can talk to a generative AI, almost no matter what language they speak.

Insurance Thought Leadership:

AI always had a brain, but now it has a mouth, too. Eyes and ears, as well.

Sviokla:

In addition, every person now becomes a team, with the AI helping. And that’s huge.

I think we’re at a Henry Ford moment. A lot of people talk about his assembly line, but they forget Fred Taylor, whose knowledge management was the first large language model for business, because he broke all processes down into their tiny parts so they could then be put back together in the most efficient way. If you look at what Taylorism did, what Fordism did, it took the unit cost of everything way, way down and pushed quality way up. Generative AI will do the same.

Ford also doubled the going wage, which I think is a lesson for today’s businesses as they reap the benefits of generative AI. Surplus can go only three places: to the customer, to the investor or to the labor. I would encourage people, if they want to be more than one-and-done, to provide a lot of the surplus to labor.

Insurance Thought Leadership:

In terms of effects on insurance, I can certainly imagine the sort of thing you're talking about in, say, underwriting or claims. You're taking in all this information, and you're using the AI, and you're getting smarter. You’re also on a steep learning curve and are getting smarter about how to get smarter. Is that how you’re thinking about this?

Sviokla:

Well, yes. If you look at insurance companies, some are heavy on customer interaction and on indirect cost. All that stuff gets completely reengineered. Completely.

The chatbots we have now are lobotomized. The new chatbots have a brain. And the filtering will happen up front. You won’t have to go through a phone tree and get routed to a human when the system fails. The AI will know up-front that something is too complicated for a machine and will route the call to a human expert.

Someone at our event used the new technology to greatly speed up the handling of calls in a call center. There aren’t any third-level service reps any more. It’s all down at the first level now. Customers are happier, and he got a return on his investment in a month.

I know someone at a major health insurer that has already deployed over a dozen large language models doing things like customer support and regulatory compliance.

Then you've got all of what today is dark data: data on what people look up in FAQs, the questions they ask about how something works or what a policy means and so on. All that data will be analyzed by the AI and turned into insights that can be used throughout an organization.

You also have folks like Tomorrow.io, which is launching a new set of satellites that have fine-grained operational weather data. They're down at the fleet level, saying, Don't roll those trucks in North Texas for the next two hours because you're going to have flash floods. That kind of stuff.

AI, in general, and generative AI, in particular, enhance both sides of the Law of Computability [a formulation of John’s that he describes at length here]. The AI increases your ability to digitize through pattern matching and machine learning. It also increases your understanding of whatever you’re analyzing. The effect is like the Law of Accelerating Returns.

And what’s going to happen in health insurance when you can take the continuous analog data on my heart and combine it with data from my medical record along with the image data from my MRI? What the hell are we going to find? I have no idea. But I bet we find some cool stuff.

A lot of knowledge work is like discrete manufacturing, but you’re going to have a continuous flow. It’s like the switch to Ford’s River Rouge. Ford had to redesign everything, and we will, too.

Insurance Thought Leadership:

If you're running a big insurance company these days, how do you get started? I mean, this has to be kind of intimidating for people who are not technologists. They sort of know they need to do something, but what do they do?

Sviokla:

Scientists have a term, “inattentive blindness” [defined as “occurring when an individual fails to perceive an unexpected stimulus in plain sight, purely as a result of a lack of attention.”] It’s like what happens in the famous gorilla video. [Viewers are asked to count how many times people in white shirts, as opposed to black shirts, in a group pass a basketball to each other, and 80% fail to notice that someone in a gorilla costume walks into the scene, stops in the middle of the group to look at the camera, beats his chest and slowly walks off. If you haven’t seen it, you can find it at www.theinvisiblegorilla.com.] What drives inattentive blindness is high pressure, lots on your mind, basically overcrowding of your head. That's pretty much the definition of an executive.

So the first thing is every senior executive or every executive in the company needs to spend five hours personally using the stuff.

Then you need to expand your network, because your innovation space is really constrained by who you interact with. You do quarterly updates to make sure you’re on track. You fund the rebels to make sure you’re exploring all your options. You do future back planning – you figure out where you want to be in five years, or whatever the time frame, then ask yourself what you need to do today to create the opportunities that will get you there.

You start with customer service, where you’re guaranteed to get an ROI within a year. Then you take the surplus and put it into your talent. You want to become a talent magnet because whoever becomes a talent magnet will have a massive advantage. It’ll be hard for people to catch up if you reinvest in the talent and the data. Your unit costs will just keep getting so much better. It’ll be like Ford in the early days. Nobody can catch me. It’ll be like Toyota more recently. They walked people through the factory and showed what they were doing, but other companies still couldn’t do what Toyota did because they didn’t have the organizational capability.

Because insurance is largely a WINS business, those that start early and keep investing in their people will be on a different learning curve, while others simply won’t progress as fast. And the scale effects here are enormous.

If you can get your organization to constantly be in dialogue with the machine to improve, that will enable a new management approach. People just aren't used to doing that. But your organizational systems will know how to learn faster than the competition, and you can amplify the living daylights out of your learning capability.

Insurance Thought Leadership:

Any parting thoughts?

Sviokla:

We’ve all seen the returns that technology breakthroughs can generate. As of three weeks ago, Nvidia, Microsoft, Apple, Amazon, Meta and Google/Alphabet had a combined market capitalization of $10.1 trillion. If you combine Japan, the world's third-largest economy, and Germany, the fourth largest, that's GDP of $8.4 trillion.

But I think generative AI is bigger than the internet. The internet lowered transaction costs and facilitated a lot of innovation, but we’re now reapportioning work between machines and humans in a fundamental way and can rethink all our management processes.