KEY TAKEAWAY:

--The main culprits are fraud and lawsuits, rapid inflation for building costs and a surge in extreme weather.

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Across the country, homeowners are facing heavier financial burdens when it comes to securing the coverage they need to protect their property and belongings. Rising homeowners insurance rates are making budgets tighter for millions of American households. And in certain parts of the U.S., the situation is rapidly approaching crisis mode (if it’s not already there). 

In recent weeks, hundreds of thousands of policy holders in Florida and California were left scrambling to find new insurance coverage due to insurance providers pulling out of both states. Farmers Insurance is the most recent insurer to stop offering coverage in Florida, joining dozens of others. Meanwhile, State Farm decided to stop writing new homeowners policies in California — at least for the time being. Louisiana has lost at least 20 insurance companies in recent months due to either insolvency or withdrawal.

There are many factors contributing to the exodus of insurance companies. Here, we break down some of the top reasons consumers are facing rate increases and difficulty getting coverage.

Insurance fraud and lawsuits

One reason insurance rates are rising is the ever-increasing likeliihood of fraud. According to the Coalition Against Insurance Fraud, the insurance industry suffered more than $300 billion in losses as a result of fraud in 2022.

Insurance fraud in Florida, in particular, grew rampant at the hands of roof replacement scam artists over the past few years. In many cases, these roof replacement schemes escalated to lawsuits, which resulted in even higher losses. 

According to a study by the Insurance Information Institute (III), 79% of home insurance lawsuits in the U.S. originate in Florida, despite insurers in the state only receiving 9% of the country’s insurance claims. The combination, along with other factors, has pushed the insurance market in the Sunshine State to the verge of collapse. 

See also: Fundamental Shift in Life Insurance?

Inflation

Insurance providers have also felt the sting of inflation for materials and labor. According to a survey by the National Association of Home Builders, average construction costs for a typical single-family home in 2022 were around $153 per square foot, a surge from $114 in 2019.

Natural disasters

Of course, you can’t discuss the home insurance crisis without examining the impact of extreme weather and natural disasters. Wildfires, hurricanes, winter storms and other types of severe storms can devastate homeowners and result in enormous financial losses for insurance companies.

According to the National Centers for Environmental Information, the U.S. has experienced 90 billion-dollar, weather-related disasters between 2018 and 2022 — an average of 18 per year. By comparison, in the 2010s, there were an average of 13 events per year, and just seven per year from 2000 to 2009. 

The bottom line

No single event is sending the home insurance industry into crisis. Instead, a combination of factors are putting homeowners insurance providers under extreme financial pressure, especially in certain parts of the country. And as insurers become insolvent or opt to pull out of certain states, it’s ultimately the homeowners in those regions who pay the price.

The insurance industry has historically been highly data-led. As computing capability has expanded, the ability of data science to turn traditional insurance problems from descriptive, backward-looking views to highly accurate, predictive insights has advanced.

Today, insurers continue to gain deeper insights captured more quickly than previously possible. For example, there has been fast-growing interest in using machine learning to improve claims operations via informed call routing decisions, or the ability to spot emerging problems early on and trigger the engagement of human intervention for remediation. 

In the turbulent markets that the U.K. personal lines industry currently faces, data science can, when combined with experienced decision makers, deliver a compelling advantage to ride the "perfect storm" more effectively. 

Yet, although insurers are increasingly using data to generate value, firms have so far done this with varying degrees of success. At the executive and senior leadership level, there is concern that significant investment in data science teams -- and the technology infrastructure required to deploy these methods -- are not delivering the practical, pragmatic business change or value they would like or expect.

The grace and favor once afforded to executives around data science as an “R&D” activity has passed, and the expectation of clear value from the investment is now being demanded. Close observation of the market has revealed five of the biggest drivers of underperforming data science teams:

1, Trading off accuracy and value creation

Insurers face potentially conflicting challenges between how data scientists have been trained to work and the actual needs of the business. Where model accuracy and predictiveness might be the ultimate focus for data scientists, many insurance leaders are keen to see swift and actionable insights that can result in material change and measurable value. They are also -- within limits -- more than prepared to compromise on predictiveness. 

The trade-off between model predictiveness and value continues to be a well-socialized issue. How leadership balances both requirements is not an easy problem to solve, and the time required to allow this challenge to find its natural equilibrium is not always palatable – or indeed practical or desirable.  

2. A lack of technical challenge

This is a situation that occurs with leadership who have not used advanced analytics techniques in their earlier careers -- for example, those who may have cut their teeth on GLMs and do not understand these new methods as deeply. Therefore, their ability to challenge model performance or outputs effectively is reduced. This can manifest as an inability to identify and therefore steer the team away from pitfalls and, as a result, the data science function failing to deliver sufficient commercial value. It can also present as a reluctance or slowness to apply these methods, due to fear or lack of understanding, that may affect future commercial prospects. 

See also: Healthcare Data: The Art and the Science

3. Naivete

There is a certain level of naivete in the approaches taken by data science teams, which stems from a lack of understanding of the very specific, niche problems faced by insurers. Model instability, for example, is where data science techniques are able to create an inherent variability (more so than with historical methods), which when deployed in an insurance context can lead to unintended and detrimental outcomes. What date scientists choose to model is sometimes misguided, so it is imperative that insurance specialists and data scientists work together, sharing goals to achieve the best outcomes for their business.

4. Managing massive model real estate

For organizations that have great data, the opportunity to model is enticing, and with well-built models the value is unquestionable. However, models need maintenance and attention as neglect risks leading to poor insight and decision making. So, with a large model real estate, it is easy for skilled pricing resource to spend a disproportionate amount of time on being glorified handle turners, rather than spending the time in generating material insights from models to create genuine business change and value. 

5. Insufficient governance and control

Data science teams can lose sight of appropriate governance. It is critical to bring together data scientists and subject matter experts to design systems that offer greater visibility of what models are doing, with more transparent governance that is sufficiently understood by the wider business and external stakeholders. The excuse of data science methods being opaque and uninterpretable is no longer an option, with the best having good control over the impact of their models. 

The U.K. insurance market is seeing an explosion in the use of data science, with both winners and losers. Bad data science is often clever people doing clever things with data, but they all too often fail to filter through the organization to drive real change and generate no commercial value. This results in poor return on investment, but more importantly a weight around the ankles of data science teams that results in reduced productivity and attrition.

Insurers that are pulling ahead of the pack are the ones thinking about how they can create the structure and culture to empower data science teams to deliver value. They also have a strategy and clear vision around team structure, what to model, deployment and maintenance, as well as having the technical expertise to ensure the implementation is robust and real business value is unlocked from data science, targeted at solving meaningful problems. Those who are successful in navigating these challenges are seeing significant tangible returns.

Way back in 2014, Wired magazine co-founder Kevin Kelly wrote, "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Boy, was he right.

That prediction was far bolder than it looks in retrospect. For the preceding nearly 60 years, an AI revolution had been much promised but was always just over the horizon. Even proponents acknowledged that there was "an AI winter." 

But Kelly saw a convergence of new forms of computing power, plus big data and better algorithms, and declared the winter over.

And here we are: A form of AI, best-known through its incarnation in ChatGPT, has captured the world's imagination, and not only every startup but just about every established company is figuring out how to fit generative AI into its business plans. 

But if there's one thing I've learned over my many years of following technology -- beyond that Kevin Kelly is a smart fellow -- it's that caution is in order whenever a new technology is supposed to take the world by storm. Events rarely play out as expected, and mistakes get made in the rush for the gold.

So, I thought I'd share thoughts based on an insightful column I recently read on the 10 biggest mistakes companies make when trying to implement AI. The column doesn't focus on ChatGPT and its rivals, which I know is the topic du jour, but the broad lessons could save a lot of us a bunch of time, effort and money.

The column, by Bernard Marr, which I recommend reading in its entirety, lists these 10 as the biggest stumbles with AI that he's seen in his extensive experience:

• Lack of clear objectives
• Failure to adopt a change management strategy
• Overestimating AI capabilities
• Not testing and validating AI systems
• Ignoring ethics and privacy concerns
• Inadequate talent acquisition and development
• Neglecting data strategy
• Inadequate budget and resource allocation
• Treating AI as a one-time project
• Not considering scalability

I'd highlight these four: 1) lack of clear objectives; 2) failure to adopt a change management strategy; 3) overestimating AI capabilities; and 4) treating AI as a one-time project. 

Lack of clear objectives

From what I've observed, the biggest issue is that every company -- certainly, every public company -- is being peppered with questions about what its AI strategy is. Not having an AI plan would be like not having a website in 2000 during the first internet boom or not having an app in the 2010s, after Apple made smartphones ubiquitous. So, every company has some sort of AI strategy -- at least, a major AI project. 

But AI is often a technology in search of a problem, and that rarely works, no matter what technology is involved. Companies need to start, as usual, by defining a business problem to be solved. Then, if appropriate, AI can be applied. Just deciding to sprinkle some AI on a business unit or process rarely accomplishes anything, and can be distracting.

For me, two of Marr's other "top 10 problems" -- lack of data strategy and not considering scalability -- fit under this umbrella. A clear AI plan for, say, auto insurance claims needs to start by looking at how AI can streamline the process. But the plan also needs to envision from the get-go how the data gathered fits into the overall corporate data strategy -- such as by being fed into the underwriting process or, perhaps, being shared with car makers so they can improve safety or lower repair costs. In addition, the AI plan needs to map out how the initial work can be scaled. Otherwise, the AI work is more show than substance.

Failure to adopt a change management strategy

Everybody likes change -- except for the change part. And AI, done right, produces major changes in how people work. So, any AI strategy of any scope needs to prepare for the retraining that will have to be done and for resistance to appear. That means those driving the change need to communicate, communicate and communicate, then communicate some more. 

Executives will also need to model the new behavior. Don't expect others to use ChatGPT, for instance, if you don't.

I remember when IBM was selling enough email software in the early 1990s that the CEO decreed that paper memos were out and emails were in. The idea made a lot of sense. In Silicon Valley, the approach is known as eating your own dog food. You get a sense of what your customers are experiencing. But IBM executives -- who mostly didn't know how to type -- had their secretaries type memos as usual, then simply put them in email form. Subordinates weren't fooled, and the mandated move to email fizzled.  

Overestimating AI's capabilities

How easy is it to fall victim to this problem? So easy that even Kevin Kelly got caught, to an extent, in that brilliant article from 2014. He opened the piece caught up in the glow that AI achieved when IBM's Watson beat Ken Jennings at Jeopardy! in 2011 and reported at face value IBM's plans to "send Watson to medical school." But Watson, in its initial incarnation, turned out to be a one-trick pony. It was great at the sort of natural language processing that a contestant needs to do to decipher the clues on Jeopardy! but never came close to deciphering medicine. Kelly also predicted that Google would become so good at AI that, "by 2024, Google's main product will not be search but AI."

AI can be marvelous stuff, but it's really just smart computing. Yes, it can beat Jennings at Jeopardy!, overcome Garry Kasparov at chess and perform all sorts of other marvels in structured environments. But it isn't a better soccer coach than I am -- and I don't even coach soccer. 

It's crucial to focus not just on what AI can do but on what it can't. AI isn't magic.

Treating AI as a one-time project

AI is a funny beast. It isn't really a technology, at least not in the sense that, say, telematics or blockchain is. Historically, AI has always been whatever you could imagine as possible but couldn't quite do yet. When computer scientists conquered whatever the problem was, their work became plain, old computing, and AI was defined as some new aspiration. 

When I first had people start bragging to me about the potential of AI, some 35 years ago, the sorts of things we take for granted weren't even in the realm of possibility. Siri? Are you kidding me? Google Translate? Yeah, right. 

Now, while there's plenty of work being done to keep improving Siri, Google Translate and other such tools, AI has moved on to figuring out how to estimate car damage from photos a driver sends, how to price risk for a life insurance policy without requiring a doctor's appointment and the taking of fluids, etc.

Basically, AI is a treadmill. Once you get on -- as everyone should -- you can't get off. It never stops moving.

Marr's other four points are certainly important -- not testing and validating AI systems; ignoring ethics and privacy concerns; inadequate talent acquisition and development; and inadequate budget and resource allocation -- but I think of those as downstream issues that can be addressed if the strategic umbrella is right.

I came across a great quote the other day in a book about how much Abraham Lincoln did as president to lay the foundation in the U.S. for the development of science. Lincoln wrote: "We always hear of the successes of life & experiment, but scarcely ever of the failures. Were the failures published to the world as well as the successes much brain work & pain work--as well as money & time would be saved."

As usual, I'm with Honest Abe. I recommend we learn as much as we can from the failures to date on AI projects, to clear the way for the many successes that are possible.

Cheers,

Paul

KEY TAKEAWAYS:

--Client organizations must implement regular security assessments, vulnerability management and continuous monitoring.

--They must set up controls, such as multi-factor authentication, to make it harder for criminals to compromise privileged identities in corporate networks.

--Clients need to prepare well-defined plans to respond to any cyber incident.

--And they must build strong relationships with insurers and regularly discuss industry trends.

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In the face of continuously evolving and increasingly prevalent cyber threats, organizations have recognized the importance of cyber insurance as a crucial risk management tool. However, a recent survey conducted by Delinea shed light on one prominent challenge organizations encounter when seeking cyber insurance coverage – fluctuating costs.

The survey revealed that 75% of respondents said cyber insurance premiums were increased with their last renewal. U.S. cyber insurance premiums reportedly surged 50% in 2022.

The jump is primarily driven by the rising demand for coverage in light of frequent and costly cybercrime incidents. In 2022, the FBI reported that businesses had lost over $43 billion through business email compromise attacks since 2016.

With the frequency and sophistication of cyberattacks on the rise, insurance providers have been compelled to raise premiums and impose stricter requirements to maintain their economic viability. Some companies have reduced coverage caps or limited the number of policies they offer. Consequently, client organizations face greater challenges when attempting to secure the necessary coverage.

But cyber insurers have evolved and learned from past cyber incidents, which means policies are improving and risks are better understood.

To do their part, client organizations must understand that cyber insurance is a financial safety net and not security itself. Organizations need to adopt an aggressive and comprehensive approach to cybersecurity. Cyber insurance does not make your cybersecurity better, but it may force you to reduce your risks to meet the insurance requirements.  

See also: Cyber Insurance Market Hardens

Combat Rising Cyber Insurance Premiums

Here are a few strategies organizations can implement to combat rising cyber insurance premiums:

Proactive Cybersecurity Measures: These include regular security assessments, vulnerability management and continuous monitoring. 

Privileged Access Management (PAM): Insurers are increasingly emphasizing the importance of PAM in cyber insurance evaluations. Compromised privileged identities are the most common cause of data breaches, making securing privileged access critical to reducing risk. Implementing PAM controls, such as multi-factor authentication, password management, access control and least privilege, helps organizations secure privileged access and reduce the risk of data breaches. 

Incident Response Planning: Having a well-defined incident response plan is crucial for organizations to minimize the impact of cyber incidents. Insurers may consider the effectiveness of an organization's incident response capabilities when determining premiums. 

Engagement With Cyber Insurance Providers: Building strong relationships with insurers and regularly discussing industry trends and risk mitigation strategies can help organizations gain insights and negotiate more favorable terms. 

As cyber threats evolve and organizations increasingly rely on cyber insurance for financial risk management, the rising costs of cyberattacks pose challenges for the insurance industry and organizations alike. By demonstrating a commitment to risk reduction and implementing comprehensive cybersecurity strategies, organizations can manage financial risks associated with cyber incidents. Together, organizations and insurers can combat the escalating costs of cyberattacks and ensure the availability of comprehensive cyber insurance coverage now and in the future.

I've spent most of my career helping organizations build comparative advantage at the intersection of strategy, technology and innovation. I now believe both individuals and organizations should aim even higher.

We have the good fortune and awesome responsibility of sitting at the inflection point of the Fourth Industrial Revolution. For better or worse, ever better and cheaper technological building blocks, including pervasive connectivity and computing, AI, robotics and genomics, are blurring the lines of the physical, digital and biological worlds. They are already reshaping industries and societal patterns, and the transformation is accelerating.

Our individual and organizational opportunity is to guide our little slices of the universe toward the better, and away from the worse, potential outcomes. In doing so, we can help build a collective future with greater health, sustainability and prosperity. We can build a world we can proudly leave to our children and their children.

In this post, I am sharing the video and slides from a recent webinar where I explored this theme and offered three lessons drawn from my recent book, "A Brief History of a Perfect Future," written with Paul Carroll and Tim Andrews. (Thanks to Zoom for hosting and sponsoring the webinar as part of its monthly "Work Happy" series.)

Here are the three lessons, in brief:

1. Make a Third List. 

In addition to the daily and weekly to-do lists that many keep, develop a "third list" of your biggest, most ambitious goals. These are the audacious goals you and your colleagues want to accomplish in the next five, 10 or even 20 years. They might even be goals that can't be realized during your tenure. But they should be goals you are always on the lookout to materially advance in your time, whenever possible.

In my presentation, I talked about how Rahm Emanuel and his predecessors as mayors of Chicago had the restoration of the Chicago River on their third list. Through a combination of long-term master planning, patient zoning, opportunistic development and political savviness, they shepherded a decades-long transformation of Chicago's slimy, concrete-entombed downtown riverfront into the magnificent Chicago Riverwalk.

See also: 6 Words to Focus Your AI Innovation Strategy

2. Embrace the Laws of Zero. 

Seven technological building blocks—computing, communications, information, genomics, energy, water and transportation—are advancing exponentially in capability and, on a relative basis, headed toward zero cost. That means we can plan on being able to throw as much of these resources as we need to at any problem to address it intelligently. Success in doing so would bring us closer to what my coauthors and I call the Future Perfect.

But the building blocks are not the buildings. It is easy to imagine these capabilities being used to exacerbate societal problems in areas such as health, equity, civility, privacy and human rights.

3. Write Your 'Future History.' 

As the saying goes, "If you don't know where you're going, you might not get there." "Future histories" are narratives that help illustrate and crystallize a desired future scenario. Rather than predicting some abstract or fantastical future, they aim to describe an ambitious yet attainable scenario by a specific date. The target date should be far enough out so you don't worry about short-term noise, constraints and implementation details (yet) but near enough to allow realistic estimates of what is technologically possible. Working backward to today, you can chart the possible paths to that future.

For example, when President John F. Kennedy declared in 1961 that the U.S. would put a man on the moon by the end of that decade, he rallied the nation to achieve a complex challenge that might otherwise have taken many decades. Kennedy’s narrative was a magnificent example of a “future history.” With vivid strokes, JFK described an ambitious yet attainable goal by a specific date. His narrative captured public imagination and support and, as he said, “served to organize and measure the best of our energies and skills.” Working backward from Kennedy’s future history, an extensive public/private partnership laid out the path to invent the future Kennedy envisioned. This included, in no small part, developing and integrating a host of new technologies and capabilities, such as in materials, propulsion, guidance, control, communications and safety.

Here's a video that further explores future histories.

* * *

Today, the world finds itself facing challenges much more daunting than going to the moon, such as in climate change, war, health, equity and poverty. But we also have near magical building blocks and tools to augment our human ingenuity. It is the opportunity of a lifetime.

ITL: 

A couple of years ago, there was loads of enthusiasm about embedded insurance, but that seems to have lessened, at least in some quarters. To start us off, could you please tell us where you think we stand at the moment? 

Chris Bassett: 

Embedded insurance isn’t particularly novel. There have long been forms of insurance that are made available at the point of sale. There’s bancassurance, for instance. You buy coverage when you’re hopping on a plane. There’s the whole warranty model.  

You're capitalizing on the endorphins associated with making a purchase and the perception of the risk of losing that asset. What’s interesting is that a study found that consumers perceived the risk of loss of a particular item was around 7% while the actuarially calculated risk was 3% or 4%, so consumers may well be overestimating the potential for loss, which leads them to consider purchasing a warranty. 

The challenge is: How can we make insurance a natural part of an overall transaction? We shouldn’t just say there’s a pull at the point of sale that we can capitalize on. Embedded insurance shouldn’t just be a bolt-on. The idea behind the “point of design” approach is to find a way to weave the insurance into a purchase and make a meaningful connection. 

ITL: 

Where do you see opportunities to do that? 

Bassett: 

The first thing is to think about the design of the insurance product. Then we should also think about the long-term experience for the buyer. 

We were looking at this with a large jewelry firm that sells through boutiques. They sell very, very high-end watches and jewelry. We explored completely redesigning the purchasing experience, including the idea that an insurance component was bundled in as part of the warranty and would tap into specific emotional triggers associated with the purchase.  

You could actually take a step back and say, What if we redesign the entire product with insurance and potentially other financial services in mind? You want there to be such an obvious fit that it wouldn’t make sense to purchase the product and the insurance separately.  

An example of this might be, say, Nike shoes that have health and wellbeing coverage built in. The design questions that you have around what makes a really appealing sort of sportswear are similar to the sorts of questions that underwriters can draw from in thinking about what this particular risk profile looks like. And there would be a natural affinity among people who bought the shoes, so you could build a community aspect around them. 

You could also go beyond the point of sale. Let’s say you buy a car, and insurance is bundled in at the point of sale. There could also be an on-demand component. Maybe you later see the value of adding coverage, perhaps for long-term disability, and then take advantage of additional safety features in the vehicle. Maybe you tie home insurance together with risk prevention services and let people turn their coverage on or off, depending on whether they’re there.  

So, from a “point of design” perspective, you can design a new sort of insurance product, you can weave an asset or service together with an insurance product in a way that aligns their value propositions or you can do a combination of the two and possibly include an on-demand component.  

ITL: 

Let me ask about some of the objections I’ve seen raised about embedded insurance. I’ll start with agents and brokers. Don’t they get cut out? Won’t they block the trend or at least slow it greatly? 

Bassett: 

Yeah, absolutely, that’s an issue. But there is the potential to create enormous value for brokers and agents. We've looked at this in terms of vehicle telematics, more sophisticated home-related sensors and so on. The amount of information that agents and brokers now have access to about policyholders allows for a very different relationship. 

To give you an example, we looked at small commercial truck fleets and saw that, beyond just helping policyholders improve their driving, brokers and agents could work with them to improve their general risk profile. Agents and brokers can become kind of a risk management coach and help clients reduce premiums. There's also the potential to gather a lot more personality-based information, about how people are living, how they’re behaving and so on. That provides an opportunity to look at different products that might interest customers. 

Embedded products can create a continuing relationship that allows for data collection and engagement. That creates long-term opportunities even if there’s a short-term cost. 

ITL: 

That’s an interesting way to look at the issue. What about the complexity? What happens when I go to a jeweler who wants to sell me insurance, but I already have a homeowner’s policy that covers my belongings? 

Bassett: 

You’d look to design complementary coverage. With the jeweler, for instance, we were dealing with rings that cost hundreds of thousands of dollars and that weren’t covered under homeowner’s policies, even though many buyers thought they were. 

ITL: 

How about an objection that somebody raised in an article published with us recently? He wrote that, if the insurance becomes simple enough that I can just purchase it as I buy the ring or the plane ticket, then it becomes a commodity, and the airline or jewelry chain could easily swap out my insurance and swap you in. That would mean all the leverage in the relationship would go to the retail partner and make for a bad deal for the insurer. 

Bassett: 

We spent a lot of time looking at travel insurance, and, yes, there’s an enormous amount of control in the hands of the cruise lines and airlines as distributors. The key for insurers is to capture more information around clients’ personalities, around their risk preferences, and then reverse engineer to look at their value systems and build risk profiles.  

You say, Okay, you bought travel insurance with us, and we know from the cruise line which events you chose to go on and sort of the nature of your movements on the ship, so that leads us to believe that you might be interested in these types of coverages or these types of services that we can offer.  

This comes back to the difference between thinking about the point of sale and thinking about the point of design. Embedded insurance has got to be more than just the transaction. 

ITL: 

Any final words? 

Bassett: 

Outside of automotive, where some initial partnerships and exploration are happening, I haven’t seen a lot of activity. But I think there's a compelling opportunity for insurers to start working with technology firms to look at emerging technologies and see how they might enable embedded insurance offerings.  

I also think all organizations, including insurers, need to start building partnership muscles. The ability to identify the right kind of partners to work with and to build strong connections with them tends to be fairly weak. Companies need to work at better identifying partners and at codeveloping solutions.  

ITL: 

People don't think of developing partnerships as a skill that has to be developed. But it is, and building strength takes time. I think your description of the need for partnership muscles is spot-on. 

Thanks, Chris. 

Phoenix just wrapped up a month in which the high temperature exceeded 110 degrees Fahrenheit every day — many who simply fell in a parking lot had to be treated for burns from the asphalt. Water temperatures off the Atlantic coast of Florida exceeded 101 degrees, near the maximum recommended for hot tubs. The average global temperature on July 6 was the hottest on record, and the U.N. says we've entered "an era of global boiling."

Challenged by climate change, home insurers in the U.S. have lost money in five of the past six years and continued to rack up losses in the first half of 2023, according to an article that appeared in "the Wall Street Journal over the weekend. Insurers are raising rates as fast as they can, while narrowing coverage or even exiting troubled markets, the article says, but it quotes a colleague of ours from the Insurance Information Institute as saying underwriting losses are expected to continue through 2025.

"Progressive, for example, said catastrophe losses last month ate up 92% of home-insurance premiums earned" because of severe weather, the article said.

By raising rates and reducing coverage, insurers aren't just tackling their business problems but are sending signals to homeowners that will encourage them to reduce their risks from severe weather. Increases of thousands of dollars in premiums tend to grab people's attention, and media coverage is amplifying the message. 

Still, I think insurers need to greatly accelerate two incipient, data-related initiatives. Insurers need to be much more specific in their risk assessments and communicate them to clients — don't just tell me what my premium is; tell me that the vegetation around my house and my shake roof have increased my wildfire risk and thus my premium by X percent. Insurers also need to work with regulators to show that they have to be able to use predictive models to set rates, rather than base them entirely on historical data.

Setting rates for the 2020s based on historical data doesn't work when, according to the WSJ piece, insured damage in the U.S. from severe storms, wildfires, floods and other natural disasters averaged $40 billion for the 2000s and $54 billion for the 2010s (adjusted for inflation) while the figure has topped $90 billion in each of the past three years.

I'm especially sensitive to the recent heat wave because I had my own brush with wild weather over the weekend in Washington, DC, one that ended happily only because of an alert brother-in-law.

The weather has been exceptionally hot for DC, reminding me of visits to Florida — blistering, muggy days have repeatedly turned into thunderstorms in the late afternoon. On Saturday, my elder daughter and I stepped out of the National Gallery of Art into a moderate rain to walk a half-mile to a Metro stop to go see one of my sisters and her family for dinner in Arlington, Virginia. But as soon as we started to cross the National Mall — where there is no shelter to be found — thunder and lightning erupted, and we were in the middle of the most violent rain and windstorm that I may have ever experienced. Metal signs tore loose and starting flying at us from behind, and we began running. 

We made it to the Metro — where we stood, so we left our puddles on the floor, not the seats — and the storm subsided by the time we got to Arlington. As my sister drove us to her house, though, we had to take several detours because any number of massive trees had been uprooted and fallen across residential streets, blocking them. Most of the traffic lights were out because Arlington had lost power in many areas—- as had some 200,000 people in the DC area. Fallen trees and severed branches had crumpled some roofs.

My sister's house was undamaged, partly through the luck of the draw but also because her husband had become concerned about a massive branch of an old tree that was drooping toward their roof. He had a tree service cut the branch off a week before the storm hit. 

If everyone were as prudent as Erik, the world would be a safer place with much lower insurance premiums. In the absence of that possibility, though, insurers can push people in his direction. 

A recent McKinsey article explores two ways, in particular, that AI can lead to better underwriting and, as a result, better understanding of and mitigation of risks.

The article quotes an executive from ZestyAI as saying its analytics tools can let carriers tell a customer, “'We are giving you a wildfire score of eight because you have 70% overhanging vegetation, you have a wood shake roof and you are on a 15-degree slope.'” The executive added: "The former two, you can change. The last one, you cannot. And then when renewal season comes, the insurance company can say, 'Dear homeowner, have you made those recommended changes to mitigate your risk?'”

Another ZestyAI executive said it's key to model the risk property by property. He said: "I think even the DOIs [departments of insurance] would agree here, [that] the traditional risk models spread risk across large regions versus having a laser-focus on the individual property. So in the case of the traditional model, for example, everything east of I-95 in Florida might be considered all the same risk, when, in reality, it’s not."

Specific information about risks, along with an opportunity to address them, could go a long way. I could even imagine doing something akin to what OPower (now a part of Oracle) does with energy bills: letting people know how they stack up against their neighbors in terms of energy-efficiency. One of the cofounders told me years ago after we were on a panel together that his earlier work on political campaigns had shown him that the best way to get people to vote was to send them a note showing how their voting record compared with their neighbors'. He took that idea from politics to energy, where he nudges me every month and surely nudges many of you to think about how you compare with others.

Imagine how that might work with, say, wildfire risk: "Your risk puts you in the XX percentile compared with similar families in your area...."

You could even extend the idea to communities, given that your wildfire risk rises or falls based on what your neighbors do: "Your community ranks in the XX percentile compared with similar communities in the Sierra Nevadas...."

Peer pressure can be very effective.

The other key piece is that, as one of the ZestyAI executives said, the models for risk have to be predictive, "trained on the right amount of loss history.... That’s where the future has to go: Risk assessment has to be rooted in property-specific insights, and it has to be forward-looking. It can’t be just stochastic simulation."

His colleague said that will be a challenge for regulators, who want to deal in facts, not projections: "You will see a collision between an exponentially improving product versus a pretty static or maybe linearly improving regulatory landscape and IT landscape. I think you can build 10 times better products, but if you can only bring them to the market at the pace at which regulators would approve them, it’s still going to be a challenge."

The executives did express some optimism because ZestyAI recently got an AI model accepted as part of a rate filing in California. Winning approval took three years, but ZestyAI hopes that approvals will become simpler as regulators get more comfortable with AI and predictive models.

Carriers have their work cut out for them as they try to reverse years of losses on homeowners insurance while dealing with increasingly severe weather. But AI that enables property-by-property risk assessments and that allows for better predictive modeling (subject to regulatory approval) will increasingly help carriers and customers reduce risks.

In the meantime, we can all just try to be more like Erik.

Cheers,

Paul

In today's insurance industry, the abundance of paperwork can be overwhelming. However, the adoption of digital solutions has brought about advancements in paperless options. While traditional paper-based processes have been replaced by digital alternatives, the emergence of intelligent document processing (IDP) presents new opportunities and challenges. IDP has the potential to revolutionize the insurance industry, saving time and costs while ensuring accuracy and efficiency.

Take the Work Out of Paperwork
The manual processing of documents, such as rekeying information or extracting data from third-party systems, consumes valuable time and financial resources for insurance carriers.
Consider these findings*:

Underwriters spend up to 5-8 hours a week on non-underwriting tasks, such as building manual reports, rekeying and extracting data from third-party systems

26% of quote information produced while rekeying is inaccurate

26-50% of pricing processes still rely on spreadsheet calculations

Intelligent document processing  emerges as a game-changing technology that can address these challenges. Unlike traditional automation tools, IDP leverages Artificial Intelligence (AI) and Machine Learning (ML) to scan unstructured information and read documents in various formats, simulating human-like interaction without requiring extensive human intervention. The benefits of IDP include the ability to process documents up to 25 times faster, work 24/7, and achieve remarkable accuracy.

Unlike traditional Robotic Process Automation (RPA), IDP takes a different approach. It doesn't depend on predefined rules or templates for document processing. Instead, it uses AI and ML to scan and interpret unstructured information from documents in multiple formats, mimicking human-like understanding and comprehension. This advanced technology has made significant progress in automating tasks that were previously deemed impossible to automate. As a result, these once-challenging processes are now becoming increasingly commonplace in the insurance industry.

Versatility of Intelligent Document Processing

IDP offers a wide range of capabilities that cater to the diverse needs of the insurance industry:

• Data extraction: IDP employs a combination of Optical Character Recognition (OCR) and Natural Language Processing (NLP) to process various forms, including KYC forms, tax documents, and SEC filings. By scanning documents for specific terms or words, it extracts relevant data accurately.
• Data classification: Documents can be categorized based on their format, content, and attributes, allowing for efficient organization and retrieval.
• Verification: IDP validates and verifies data, ensuring accuracy and completeness, minimizing errors that could lead to potential risks.
• Error reduction: Through automated error detection and correction, IDP enhances data integrity and reduces the likelihood of inaccuracies.
• Digitization: Paper documents can be digitized and securely stored and retrieved electronically, eliminating the need for physical storage space, and facilitating easy access.
• Integration: IDP seamlessly integrates with existing systems and workflows, streamlining document processing and enhancing overall operational efficiency.
• Compliance and risk management: By protecting sensitive data and ensuring confidentiality, IDP assists in maintaining compliance with regulatory requirements and mitigating risks.

Top 5 Ways Intelligent Document Processing Can Help You 

1. Document Processing

IDP enables automated matching, uploading, categorization, and verification of policy applications, claim submissions, contracts, invoices, reports, receipts, and emails. By "reading" each document, extracting key data values, and entering them into underwriting systems, it accelerates the insurance submission triage process and enables handling more requests with existing resources.

2. Policy Administration

Throughout the life cycle of an insurance policy, IDP plays a crucial role. It supports initial policy processing, manages endorsements or riders, performs audits to ensure accurate pricing based on potential exposure, and resolves customer queries effectively. Additionally, IDP automates premium reminders, data validation, and policy uploads, optimizing the policy issuance process.

3. Claims Management

The claims process involves multiple stages, including First Notice of Loss, document review, data extraction, assignation of adjustors, claim uploading, and fund disbursement. IDP streamlines these steps by automating document handling from various stakeholders, reducing manual intervention, and increasing efficiency.

4. Underwriting 

Underwriting is another document-heavy process where underwriters extract and review thousands of documents before entering them into a downstream processing system. An intelligent system "reads" these documents like an underwriter, finds relevant data, and enters the appropriate data into the system, freeing up underwriters' time for higher-value work—resulting in improved productivity and underwriting accuracy. 

5. Invoice Processing

Automating invoice processing can save significant time and resources. IDP can extract, separate, and integrate data from invoices into accounting systems, eliminating manual data entry and reducing errors, leading to substantial time savings.
Intelligent Document Processing presents a transformative opportunity for the insurance industry to enhance efficiency, reduce errors, and deliver superior customer experiences. By leveraging AI and ML technologies, IDP streamlines document processing, optimizing various areas such as claims management, policy administration, underwriting, and invoice processing. As the insurance industry continues to embrace digitization, IDP will play a crucial role in shaping a paperless future, maximizing operational effectiveness, and enabling insurers to stay competitive in an evolving landscape.
 

If you'd like to learn more about how you can automate document processing and stay competitive, contact us.  

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

KEY TAKEAWAYS:

--One-off or standalone solutions acquired for specific tasks, incomplete conversions from one system to another and failures of large-scale modernization initiatives have resulted in insurers being invested in a myriad of systems and applications. Some work, some sort of work, some don’t work at all, but in a Lego sort of scenario, taking out the systems that don’t work is nigh on impossible because the “blocks” are now foundational to the insurer’s infrastructure.

--The solution lies in investing in specific areas that touch the customer and focusing on incremental change -- not attempting to do everything at once. It also requires a no-code/low-code environment, a more agile data platform and the right service provider.

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The future of insurance (and maybe the world) is personalized, streamlined and effortlessly automated. It rests in the capable hands of application programming interfaces (APIs), integrated systems, automation and insightful, democratized data. It is driven by digital transformation and the need to invest in systems and applications that will grow with the business and ensure increased data accessibility and visibility.

Over the years, insurance companies have amassed considerable, and complex, technology infrastructure, which is slowly failing. One-off or standalone solutions acquired for specific tasks, incomplete conversions from one system to another and failures of large-scale modernization initiatives have resulted in insurers being invested in a myriad of systems and applications. Some work, some sort of work, some don’t work at all, but in a Lego sort of scenario, taking out the systems that don’t work is nigh on impossible because the “blocks” are now foundational to the insurer’s infrastructure. Many insurers do want to find a way to consolidate data and integrate systems, but building connected systems through integration is hardly ever a simple task.  

‍To ensure system integration and automation deliver on so many promises, there are five key steps insurers should take.   

01: Meet Customer Demand With Intelligent Investment

Customers are more demanding today than ever, and customers will only become more demanding, more insistent on seamless, customized solutions. There will be an inevitable “lift and shift” to companies meeting increasing and evolving customer demands more efficiently.  ‍

Investments into digital transformation are the natural first step toward successful integration. In recent research, McKinsey finds that companies focusing on marketing and sales, underwriting and pricing, policy servicing and claims -- four areas that affect the customer journey, the customer experience and customer value -- are most likely to see measurable return on investment (ROI). 

See also: Insurers Turn to Automation

02: Focus on Incremental Change

The insurance industry juggles data challenges unique to its offerings and business structures. Most insurance solutions run for many years, introducing legacy data problems that affect efficiency and access. And, unfortunately, it is difficult to increase data mastery if significant percentages of solutions are legacy-driven and inherently complex.‍

The answer lies in incremental change to systems, data integration and the implementation of a platform capable of blending legacy with innovation. This hybrid approach minimizes disruption while ensuring the organization continues to move forward. With the right technology and service provider, incremental change can help the business adapt and evolve to ensure longevity. 

03: Create Flexibility With a Low-Code/No-Code Environment

Custom code, while great upon initial release, can grow stagnant with time or as requirements change. Unexpected delays, struggles with development processes or even data changes and growth, can affect written code and cripple a business’s ability to adapt to solutions and systems on demand. With a low-code/no-code environment, businesses can ensure data and operations remain agile and adaptable to new system requirements or as sales and marketing efforts call for segmentation. 

0‍4: Use a Platform That Empowers Data

Investment into a modern data platform is about more than just checking boxes, disrupting competitors or driving the business toward trends. It is about enabling and empowering every silo and solution within the business. This is the key to unlocking the door to a scalable, extensible and enterprise-ready solution that sits at the very center of your dataverse. What is needed is a solution that is powerful and future-proof. This does not equate to disruptive, destructive or expensive. It equates to elegant, intuitive and intelligent.

Investing in a solution that enables the efficient use of data allows for the hyper-personalization of policies, the creation of customized customer journeys and the ability to refine insurance policies into bespoke products. Such investments provide the freedom to innovate and the ability to truly explore intelligent decision-making. This requires more than just a giant box of tech; it needs to be backed by strategy that will help achieve the right value, improve speed of delivery and ensure investments can move dynamically with evolving business requirements. 

See also: What’s Beyond Robotic Process Automation

‍05: Choose a Solution Provider That Simplifies

Successful system integration and modernization requires more than just a digital overlay. It asks that the organization stop seeing technology as a magic cure-all for legacy data complexities. It isn’t. The real cure lies in finding solutions that simplify complicated situations and take every part of the customer’s organization into account to ensure the seamless flow of information across silo, system and solution.  

Often, this means finding a capable, compatible solution provider that can be trusted to help the organization increase data transparency and implement rules and policies that align with integration and data usage and ensure the workplace and workflows are managed intelligently.‍

Insurance enterprises today must meet the challenge of modernizing legacy data, automating business processes and building connected systems. In one scenario, the solution is a hybrid data automation platform implemented in a low-code/no-code environment. Insurers that are going to make it to the next level of this game need powerful technology and insurance-savvy expertise to grow and to make the most of data resources, today and in the future.