FROM THE EDITOR  Wandering the floor at this year's InsureTech Connect, I had a hard time finding a booth that didn't somehow tie into generative AI. But I think two aspects of the technology are getting short shrift, so I was delighted to be able to explore them in this month's interview, with Megan Pilcher, a senior vice president and the insurance go-to-market leader at IntellectAI.  First is that generative AI doesn't just let you do work faster. It helps you do work that you otherwise wouldn't get around to.  For instance, Megan says: "As an insurance carrier, you'd love to have documentation on every risk that comes through the door because you are going to see that risk again. But when an underwriter prioritizes their work, documenting the accounts they did not write is a less than desirable task. We can start using AI to do that documentation and provide a summary. When the risk comes back the following year and a different underwriter picks it up, they can get a rundown....   "With today’s manual processes, someone only pulls [loss run] information if a decision has been made that at least they want to quote the risk. But would there be value in doing it at the beginning of the process, extracting loss information on risks that you would have weeded out? What could your actuaries do with that data? Could their predictive modeling be different if we were able to provide them loss data on every submission that comes to the door?  "When all the data is being manually keyed in, you're not going to get all that information; not for a quick decline. But maybe it's a year later, and you start thinking about getting into a particular class of business, or a particular line of business, and you wonder, how many submissions would you get? What would the losses be? How would you need to price it? Now you have historical data to use for evaluation." That's powerful stuff.  I realize that we'll always be limited by how much our audience is going to consume. Just because generative AI can churn out novels doesn't mean anyone will take the time to read them. But the kind of data synthesis that now becomes possible means that important, previously buried information and trends can be easily brought to the surface.     Second is that generative AI makes it easier to integrate information into someone's workflow -- addressing a truly hard problem.  If you go back to the early days of personal computers, starting 40 to 45 years ago (yes, it's been that long), the revolutions were just better forms of existing tools. The electronic spreadsheet replaced the paper one. Word processing software supplanted typewriters. PDFs were better forms of fax machines. And today, most of the focus among insurtechs is on building better tools.  The problem is that the focus on tools leads to a herky-jerky workflow. An underwriter in the middle of a file has to launch a tab and open a tool, use it and then integrate the result back into their work -- then open another tab for another tool, and so on. The same goes for pulling in data. The underwriter taps into a source, cleans the data so it fits into their workflow, incorporates the data... then moves on to the next external source.  Generative AI, while in its early days, will make it far easier to incorporate tools and external data. The tool will simply pop up, within the workflow, as needed. So will the information from external sources.  Megan explains: "When I think about all these tools that are coming up in insurance, the other part that's super critical is, how do you tie them into your underwriting workbench, so they’re part of your underwriters’ flow? Non-underwriters love to invent tools for underwriters that we think will make their life easier. Especially when tools are newer, they're almost always piloted outside the underwriting workflow. But the result is that you give underwriters a tool that stops their workflow. You're asking them to do something different, sometimes log into something different, something that they're skeptical about, and then pull the result into their workflow—and hope everyone's doing it the same way. You have to make sure tools are part of the flow, not an afterthought." Our conversation was one of the meatiest I've done in some time. I hope you'll give it a read. Cheers, Paul   In this month's FOCUS on Artificial Intelligence (AI), Megan Pilcher, SVP and Insurance Go to Market Leader at IntellectAI, delves into the significant implications of AI in the insurance sector. She elaborates on how AI is revolutionizing the industry by streamlining processes, enhancing decision-making, and ultimately paving the way for a more efficient and competitive landscape. Read the Full Interview "AI is not a new hammer, and we’re not just walking around looking for nails, but AI can be another tool in the toolbox. Carriers need to start thinking about where within their workflow they're going to take advantage of it. " — Megan Roche Pilcher Read the Full Interview READ MORE Taking Generative AI for a Spin Tools like ChatGPT and Bard offer endless applications for auto insurers, but adoption of generative AI isn’t a linear path. Read More Do You Have FOMO on Gen AI? Tech leaders are feeling pressure to integrate generative artificial intelligence into their programs, but some caution is in order. Read More AI: Beyond Cost-Cutting, to Top-Line Growth Call centers will no longer be viewed as cost centers. AI and actionbots will turn these hubs of customer interaction into revenue generators. Read More Using AI to Prevent Insurance Fraud AI-powered fraud detection and human expertise combine to fortify insurers and minimize risks for all stakeholders. Read More How to Think About AI in P&C AI applications have been used for discrete tasks but will soon drive end-to-end decisions across the entire claims management experience.   Read More 5 Ways Generative AI Will Transform Claims Generative AI will revolutionize how carriers, third-party administrators and medical management firms operate and the results they can deliver. Read More FEATURED THOUGHT LEADERS Henry Kowal Alex Sun Muddu Sudhakar Isaac Smith Yury Pensky Jeff Gurtcheff

Megan is an experienced insurance professional with over 25 years in the industry. A large part of her career was spent working for insurance carriers in roles ranging from sales, distribution management, product, and digital transformation for underwriters, agents and brokers. She has hands-on experience across several lines including personal, small commercial, mid and large commercial, specialty and reinsurance. In her role at IntellectAI, Megan leads product development, sales, and solutions. Megan is passionate about helping carriers and MGAs improve their underwriting experience resulting in new business growth, improved retention and better underwriting outcomes. Megan has a BA from James Madison University and an MBA from the University of Hartford.

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Paul Carroll

I think this is one of those moments when a technology just pops and grabs everybody’s attention. As big a deal as the iPhone introduction was in 2007, I don’t think I’ve seen anything like this fascination with AI since the first commercial browser was introduced in 1995.

IntellectAI has been working with AI for a long time. How would you frame what we’re experiencing?

Megan Pilcher

AI is not a new hammer, and we’re not just walking around looking for nails, but AI can be another tool in the toolbox. Carriers need to start thinking about where within their workflow they're going to take advantage of it.

I don't see a scenario any time soon where people will look to automate underwriters. Carriers believe that “no one can do underwriting like my underwriters can,” and I agree. But there are parts of the process that we can really start taking a look at.

It’s interesting to go back and look at the end-to-end underwriting workflow and to reevaluate all the places where there’s friction. To this point, we’ve accepted a lot of friction, as if to say, "That's just how insurance works." We didn't have a tool to resolve that friction, but now AI brings something else to the table. We get to revisit the process.

That's why I think the industry is really at the cusp of being transformed.

For instance, we use our embedded AI to do data extraction from a submission. We take everything the agent or broker said about the risk, then we take the carrier's guidelines about what they’d like to write. You put the two together, and you can start looking at any risk. Here are the positive attributes, and here are the negatives ones. Here are the ones in the middle. When you bring the underwriter in, everything is ready for them. They're not having to go look for information.

Underwriting rules can be great, especially in small business, where a lot of the decisions are binary. You don't need AI if there was a claim in the last three years and the guidelines say a submission has to be claim-free. You’re not going to write it.

But when you get to those meatier accounts, there's almost always going to be positive and negative, and the AI can bring to an underwriter’s attention those things of which they should be aware.

The underwriter can then figure out, "Is there a way for us to make money on this risk?" How do we craft terms and conditions and pricing? The underwriter gets to spend the majority of their day on the parts of the process that require a real underwriting skill set and not on mundane tasks like gathering information.

AI can also provide guardrails that help newer underwriters make sure they’re looking at all the relevant aspects of a risk. AI can help seasoned underwriters, too. They know the rules, so they aren’t looking for changes, but you can alter a guideline online and have it hit everybody simultaneously.

Paul Carroll

As you take this newly possible, end-to-end look at underwriting, are you seeing other friction points that you can address?

Megan Pilcher

One point of friction for brokers and wholesalers is to ensure that the carrier did what they proposed. What was bound? What was issued?

We can compare what was issued versus what the carrier said they were going to issue. Is there anything extra in there? Is there anything missing?

There are organizations that will do this work manually, but our AI can do it faster, better, and cheaper.

Paul Carroll

That's interesting. Both my brothers were professional poker players. And they would keep track of that sort of thing. How many hands did they play [as opposed to folding immediately]? How many times did they get into the final round of betting on a hand? How many times did they win? They would go back afterward and review their play. Was I too aggressive today? Was I not aggressive enough?

It sounds like the work of an underwriter becomes much more interesting when you take a lot of the mundane work off their plates.

Megan Pilcher

You can start upskilling your underwriting assistant staff, to put them on the journey to underwriting. You really create a career path by getting rid of some of the work that is more clerical. We think the change will make jobs more satisfying and ultimately attract more people to insurance, especially young people.

Paul Carroll

ChatGPT and the other large language models are letting you communicate more easily with AI than you could before. How does generative AI change things?

Megan Pilcher

As an insurance carrier, you'd love to have documentation on every risk that comes through the door because you are going to see that risk again. But when an underwriter prioritizes their work, documenting the accounts they did not write is a less than desirable task. We can start using AI to do that documentation and provide a summary. When the risk comes back the following year and a different underwriter picks it up, they can get a rundown.

What is the risk? Why did we not write it? The documentation helps determine what to do with it this year.

A lot of information gets lost today, and that's where embedded AI can help.

Paul Carroll

What are some other opportunities?

Megan Pilcher

I was just on the phone with a prospect. We're going to do loss run extraction for them using embedded AI. Where in the process do they want those loss runs extracted? With today’s manual processes, someone only pulls that information if a decision has been made that at least they want to quote the risk. But would there be value in doing it at the beginning of the process, extracting loss information on risks that you would have weeded out? What could your actuaries do with that data? Could their loss models be different? Could their predictive modeling be different if we were able to provide them loss data on every submission that comes to the door?

When all the data is being manually keyed in, you're not going to get all that information; not for a quick decline. But maybe it's a year later, and you start thinking about getting into a particular class of business, or a particular line of business, and you wonder, how many submissions would you get? What would the losses be? How would you need to price it? Now you have historical data to use for evaluation.

I think this part of the extracting is what you would go to a vendor for, but the carriers then can create their own special sauce. What do I think about all that lost data you extracted? What do I think about my appetite for risk? What do I think about my underwriting guidelines? What triage models should I apply to determine what risks I want to absorb or avoid?

That's where you can say to the technology folks at the carrier that there is super-high-value work to be done. These decisions are literally what make the carrier tick.

Paul Carroll

What is holding up adoption of AI?

Megan Pilcher

AI tools are going to pop up everywhere. I was just reading an article discussing an assortment of random AI tools, and one picture showed a street sign in New York City in front of a parking spot. It was really a number of signs about rules around when it's a bus stop versus when you can park here and when it's a towing zone and when there is alternate day parking or street sweeping, etc. Someone built an app where you can screenshot the signs, and it can tell you when you can park there.

I got a parking ticket this summer in New York. My husband and I just stood and looked at all the signs, and we thought, "Can we park here?" We decided we could. And we were wrong. That app would have been great to have.

But how would I have even known that app existed?

When I think about all these tools that are coming up in insurance, the other part that's super critical is, how do you tie them into your underwriting workbench, so they’re part of your underwriters’ flow? Non-underwriters love to invent tools for underwriters that we think will make their life easier. Especially when tools are newer, they're almost always piloted outside the underwriting workflow. But the result is that you give underwriters a tool that stops their workflow. You're asking them to do something different, sometimes log into something different, something that they're skeptical about, and then pull the result into their workflow—and hope everyone's doing it the same way. You have to make sure tools are part of the flow, not an afterthought.

When the time comes to take a look at the risk, the app is already there, and it's ready for you and it's part of your process.

Paul Carroll

I love that app that answers the question, "Can we park here?" I lived in New York for 14 years, off and on, and parking is really confusing. If you're an underwriter and something pops up and says, don't write this risk, or think about this, and it’s part of the process, not a separate tool, well, I can see that being really valuable.

Megan Pilcher

We're not at the point where AI should be making decisions. But what if something is just emerging as a risk, and the AI notes for the underwriter, "We should keep an eye on this. We've had some claims in this area"? It's not a process. It's not a guideline. The AI isn’t telling you what to do. But it's asking you to notice something you might want to consider in your decision making.

Paul Carroll

This has been a fascinating conversation. Thank you for your time, Megan.

Gaurav Garg, partner and global head of property & casualty insurance at Oliver Wyman, recently sat down with ITL Editor-in-Chief Paul Carroll to talk about the state of digital transformation, now that the insurance industry has been tackling the issue for years. He says many businesses have moved through the first stage, in which paper-based processes are digitized. They are now in the second phase, where they are working to use data more effectively to drive decisions—the rush toward using generative AI is part of this phase. Garg says the third phase will come as businesses emulate social media engines that “allow almost instantaneous dissemination of data, instant consumption of data and instant access to data.” Get ready for your smart assistant.

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Insurance Thought Leadership:

You’ve been involved in the digitization and transformation of insurance for a long time. Let me start by asking you for an overview of where things have gone over the last few years and where you think we can get in the next few. Then, we’ll dig deeper.

Gaurav Garg:

Digital technology disruption has been discussed within the insurance industry for over a decade. In the last two to three years, events have made it a business necessity, not just something that is good to have.

COVID forced companies to think of ways to connect with all their stakeholders — employees, distribution partners and clients. The supply chain has been disrupted, first because of COVID and now because of the war in Ukraine. There has been a kind of trade war, or at least significant friction, developing among some bigger economies. In addition, in recent years we’ve seen a surge in catastrophe-related losses (CAT-losses). And the talent shortage is a big issue, especially in insurance.

Companies are focused not just on digital connectivity but on efficiency.

Insurance Thought Leadership:

That’s a great start. Would you say a bit more about what you mean by digital disruption?

Gaurav Garg:

Over the last several years, companies have focused on what I call CI and CX — customer interface and customer experience. The front-end seems to be getting the most attention in terms of creating a digital experience. Not many companies are disrupting the way they work internally.

A truly digital insurance company has digitized all aspects of its business, entwined across the value chain.

Insurance Thought Leadership:

You talk about the three stages of innovation. Would you explain that a bit?

Gaurav Garg:

The first is what I call the foundation phase. You can’t build a truly digital company if you have a lot of manual processes. So, companies need to institute things like robotic process automation and the digitization of payments and cash management. Get rid of paper. Maybe start on machine learning. These efforts won’t be fully integrated with the enterprise, but they’re a start.

In the second phase, you start adding data-driven outcomes, data-driven decision-making, data-driven underwriting. You go toward straight-through processing [STP] on underwriting, on claims and on payments.

In retail insurance, let’s say you run 80% of everything through what we call the black box STP, and only 20% spits out as exceptions. Even there, you’re more efficient because that 20% takes advantage of the foundation pieces of digitization from the first phase.

This second phase is what most companies are building out now. One piece that is being developed rapidly is generative AI, which is transforming business. Everyone, including people who are not too conversant with computers, including people in my family, are using generative AI.

In the third phase, companies put themselves in a position similar to what you see in the social media world. These social media engines allow almost instantaneous dissemination of data, instant consumption of data and instant access to data.

Three things are holding insurance companies back. One is obviously regulation. Number two is privacy laws. And number three is the enabling technology. But I think all those issues will be solved. The laggard will be regulation, but there are already some “regulatory sandboxes,” including in Bermuda, where companies can start to experiment and innovate.

Insurance Thought Leadership:

I'm interested in exploring generative AI a bit. As someone who’s followed the technology world for 35 years now, I’m not sure I’ve ever seen anything catch on quite so fast, not even the internet. Would you please say a bit about what the actual uses are that are happening now and lay out a road map for what’s coming?

Gaurav Garg:

From what I’ve seen, working with several companies on this and with our own generative AI at Oliver Wyman, companies will start with a closed loop. They’ll use internal data to train the AI. Then they’ll move to an open loop, bringing in data from outside.

Many are starting with claims, where generative AI can assess what’s happening with policy conditions, deductibles and so forth and can measure and report on the outcomes. The same thing is starting with underwriting, where you consume a lot of external data and have to turn it into useful information.

Insurers, like all enterprises, will also use generative AI for other, general activities, such as research. If you look manually at earnings for companies in a sector where you might want to invest or want to know the state of a line of business in insurance, like D&O [Directors and Officers Insurance], that takes a long time. But generative AI can gather all that information within seconds. It’s not perfect yet, but you can add another layer of AI to screen for errors.

Finally, you have your day-to-day uses: Smart assistants, email management and other internal tools are popping up. All kinds of work will be taken over by generative AI.

Insurance Thought Leadership:

Lots of people talk about how generative AI lets people do things faster and more efficiently, but I was talking to someone the other day who said large language models like ChatGPT will also let companies do more things. For example, a state of the business report to a partner might take too long to justify if done manually, but ChatGPT could spit out a report in no time. Do you agree with her take on doing more things?

Gaurav Garg:

Yes, output will increase tremendously, because in a short time you can do much more. But this is going to throw out another new challenge: There's a limit to how much information humans can consume.

People will have to learn to use generative AI to provide precise answers to precise questions. Maybe you start out wondering what the rate environment is in commercial insurance, but then you have to be able to zero in on cyber insurance, say, and then to figure out how much of the rate increases relates to ransomware.

You also have to deal with the “hallucinations” that generative AI can produce. How do you create a reliable AI that doesn't need a second level of scrutiny?

I think we will get there very fast.

Insurance Thought Leadership:

I find it interesting when you talk about external versus internal data. At this point, generative AI basically consumes everything that’s out there, but I can imagine an insurance company, or any company, saying, “Okay, we're going to have an internal ChatGPT, and it's only going to have access to our policies, our data and so forth. And on that basis, we'll be much more comfortable with the accuracy than we would be if it just grabbed everything that was out there.” It sounds like you're seeing that possibility, as well.

Gaurav Garg:

Yes, certainly. One has to be extremely careful with the information about policyholders, but the technology will address that issue, as well.

In financial services, where I’ve worked for over 30 years, there is the concept of the maker and the checker. We might see that with generative AI: One layer of AI will be the maker and another the checker.

Insurance Thought Leadership:

What about other technologies that hold real potential for insurers?

Gaurav Garg:

IoT [the Internet of Things] will allow for connected homes, connected cars and so on, but is also a challenge, because all those connected devices present so many opportunities to hackers.

I see some companies now coming up with personal cyber. How do you make homes more secure? It could be disastrous if someone hacks into any of these home systems, with all the cameras and all the devices that control practically everything in my house.

A related area is sensors. A lot of things that were actually done on a manual basis, such as inspections, are now being done by sensor technology. Sensors also allow for more parametric insurance, not just by measuring rainfall or wind in natural catastrophes, but by measuring something like the water level in factories.

Drones are playing a big role, too, with things like roof inspections.

Another big one relates to the future of mobility, where Oliver Wyman has done significant work in our insurance and automotive practices. Two things are happening here:

As safety features keep getting better and as autonomous vehicles move toward full self-driving, the liability shifts from the driver to the technology, and what had previously been considered personal insurance becomes commercial insurance. Liability also shifts to the infrastructure, because the technology is reading the road signs. If the infrastructure gets messed up, who’s responsible in an accident?

The other part is the emergence of EVs [electric vehicles]. Safety features are making drivers safer, but if something happens, there is a much bigger loss because there are so many sensors to replace. If a pebble hits your windshield, you can’t just call a service and have it replaced at home. There are cameras and sensors in the windshield, the system has to be recalibrated… and the price of the repair doubles.

EVs also complicate things because the batteries have a life, whether that’s 10, 12 or 15 years, and the battery is the main component of the car. How do you insure the car as it is aging? In the financing world, some companies are leasing batteries separately. And you may wind up with a different battery in the car, because some companies are trying to save time for drivers — especially for truckers — by swapping in a fully charged battery rather than make people wait while their batteries recharge.

The future of mobility will have a huge impact on P&C insurance. And remember, auto insurance is the major chunk of P&C. How are companies preparing?

Insurance Thought Leadership:

That’s great. Any parting words?

Gaurav Garg:

I am personally of the opinion that companies that are fully committed to technological changes and that become fully digital will survive and thrive.

There is no other option but to meet the client and the market where they are, not where the company is. There are companies still on the periphery and still kind of wedded to a very traditionalist model. They will find life extremely difficult, because their customers will leave, their distribution will leave and their employees will leave.

But for anyone that can get past that traditionalist approach, this is a very exciting time.

Insurance Thought Leadership:

This was great. Thank you for your time, Gaurav.

We are entering a profound age of acceleration. AI is a co-pilot and brings huge opportunities for economies of the future, for the future of work, and for the future of workers. In Part 1 of our series, Reinventing Insurance with Generative AI, Oliver Wyman explores the opportunity for insurers and the impact on operations, strategy and ways-of-working.

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Sponsored by ITL Partner: Oliver Wyman

As the insurance industry is starting to evolve, the small and medium business (SMB) market becomes an interesting growth opportunity. Customer needs and wants are beginning to shift, opening new value pools. To win, thrive and gain market share in the SMB market, Oliver Wyman presents 10 considerations that should be at the top of every insurance carrier’s list.

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Sponsored by ITL Partner: Oliver Wyman

When I first read about an accident involving a Cruise robotaxi that happened in San Francisco in early October, it sounded like a bizarre one-off. In fact, now that more details have emerged, it's clear that the accident represents a real challenge to the near future of all autonomous vehicles.

As originally reported, this was the story:

The accident happened in the central business district at about 9:30 pm on Oct. 2 when a driver hit a woman who was crossing Fifth Street, not in a crosswalk. She was thrown into the path of a robotaxi operated by General Motors' Cruise unit, which was in full autonomous mode. The car hit her and then quickly stopped, with the woman trapped under the car. The robotaxi waited for EMTs to show up and take her to a hospital, where she was treated for multiple trauma injuries. (The driver who hit her first never stopped.)

But that turned out not to be the whole story. When a robotaxi encounters a situation it doesn't know how to handle, it generally pulls off to the side of the road and waits for instructions or assistance. That's what this Cruise AV did -- dragging the injured woman 20 feet in the process, while she was trapped under the left rear axle.

Compounding the problem, the California Department of Motor Vehicles says Cruise didn't initially show authorities the full video from the incident. The DMV says Cruise only showed the seconds leading up to the impact and then the robotaxi stopping -- leaving out the video of the car pulling off to the side of the road while the woman was trapped underneath. Cruise says it showed authorities the full video multiple times.

In any case, the investigation into the accident has ramped up tensions between Cruise and city and state authorities, which were already sky high, and could drag in Google's Waymo and other AV companies.

What happens now? 

For the moment, Cruise has not only halted its operations in San Francisco, as ordered, but has stopped nationwide -- it was also operating a commercial service in Phoenix and Austin, Texas, and a free form of its service in Dallas, Houston and Miami. Cruise had also announced plans to test its vehicles in Nashville and Seattle, on the way to a broader rollout.

The company has also committed to "examine our processes, systems, and tools and reflect on how we can better operate in a way that will earn public trust.” That will be key. 

Cruise had faced opposition from the San Francisco Fire Department, in particular, when it applied to operate a commercial service throughout the city. Robotaxis sometimes have trouble figuring out what to do when facing emergency vehicles and all their flashing lights, and the SFFD said Cruise had interfered with its vehicles many times. 

While Cruise won approval from the California Public Utilities Commission in early August, the DMV, just a week later, ordered Cruise to cut by half the number of AVs it was using. The DMV action followed an accident in which a Cruise robotaxi pulled into an intersection after the light turned green and was hit by a fire engine that, with its sirens on, was running the red light. Cruise vehicles had a number of other, well-publicized problems, including one getting stuck in a patch of wet cement after missing signs that warned cars to drive around it. (If you're interested in more details, I wrote about the initial approval here and the curtailing of that approval here.)

Tesla is adding to the PR problems for AVs, as it's in the middle of two trials blaming it for fatal crashes involving drivers using its Autopilot system. In my opinion, Elon Musk has been irresponsible in extolling the capabilities of what, as his lawyers are now arguing in court, really amounts to advanced cruise control and not anything close to full self-driving. Tesla won a major case in 2019 in which the company argued that any crashes are the fault of the drivers, who've been warned that they need to stay attentive and be prepared to take control of the car. But even if Tesla wins these two latest cases, the AV movement still takes a hit, because the company will have won by highlighting the limits of Tesla's technology.

So far, Google's Waymo seems to have the best safety record, but that only partly insulates it from Cruise's and Tesla's problems. Reilly Brennan, a partner at Trucks Venture Capital, who writes an influential newsletter on innovation in vehicles, says the AV world suffers from what he calls the WALTER syndrome -- companies Win Alone but Lose Together. In other words, Waymo only gets credit for its own success stories but gets pulled down by anyone's failures.

Waymo could take advantage of Cruise's pause to grab market share, although we're early enough in the move to autonomous vehicles that I doubt it can do permanent damage to Cruise, unless the forced hiatus drags on for many months. 

More broadly, I think (and hope) the effect of Cruise's temporary shutdown will be to drive AV companies toward more transparency and to push regulators to standardize how they measure safety. At the moment, AV companies are required to report accidents but are allowed to be rather idiosyncratic about how they do so. They should be reporting even more data than they do now and in a format that makes it possible for regulators -- and the rest of us -- to compare and see which operator is the safest.

I'll still flag down a robotaxi the next time I'm in San Francisco, even if it's a Cruise, but, then, I'm always technology-curious. Operators and regulators need to up their game if they are going to reach the broader market any time soon.

Cheers,

Paul 

 

 

 

 

KEY TAKEAWAY:

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

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

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

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

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

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

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

See also: What to Do About Climate Risks?

Economic Headwinds 

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

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

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

See also: Our New Era of 'Global Boiling'

The Road to Greater Resilience 

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

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

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

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

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

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

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

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

See also: 5 Ways Generative AI Will Transform Claims

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

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

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

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

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

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

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

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

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

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

See also: 3 Key Uses for Generative AI

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

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

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

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

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

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

Stress vs Distress

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

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

Let’s take a look at two scenarios.

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

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

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

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

Identifying the Context; Easing Distress

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

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

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

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

See also: 3 New Trends in Life and Health

LASER Focus

At Genex, we use an approach we call LASER:

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

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

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

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

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

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

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

What's Holding the Industry Back? 

Transparency & Regulatory Challenges  

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

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

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

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

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

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

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

See also: AI: The Future of Group Insurance

Lack of Available High-Quality Data 

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

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

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

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

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

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

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

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

The Fear of AI Replacing Humans 

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

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

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

See also: The Risks of AI and Machine Learning

Best Practices for Integrating and Maintaining AI Systems in Group Insurance 

Clearly Define Long-Term Objectives Before Integration 

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

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

Identifying the Problem:

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

Choosing the Right Solution:

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

Defined Business Objectives:

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

Managing Expectations:

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

Position for Success:

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

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

See also: 3 Key Uses for Generative AI

Leverage New Data Sources  

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

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

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

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

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

Use Synthetic and Internal Data 

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

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

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

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

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

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

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

Tomorrow Belongs to Those Who Embrace AI 

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