Sensors and data-driven intelligence platforms are foundational technologies that can identify hidden dangers and help prevent losses. In this episode, Pete Miller talks with Bob Marshall, CEO and Co-Founder of Whisker Labs, and with Dave Tobias, COO and Co-Founder of Betterview, about these two different approaches.
Segment 1 (00:03:08): Bob Marshall is the co-inventor of Ting, an innovative plug-in sensor that is deployed in hundreds of thousands of U.S. homes and helps to detect electrical issues that could cause fires. Pete and Bob discuss how tragedy led to invention, high risk of silent electrical hazards, AI-driven continuous learning, fire prevention as driver of customer loyalty, simplicity is key to adoption, and transforming insurance customer relationship.
Segment 2 (00:21:49): Betterview is a property intelligence platform used by underwriters for scoring various property risks. Pete and Dave discuss Dave’s lifetime in insurance, the hunt for deep and meaningful roof data, his passion for predict and prevent, scaling with technology, adapting to insurance workflows, changing the value drivers of insurance, and finding the next risk prevention opportunity.
Peter Miller [00:00:03] Hi, I'm Peter Miller, President and CEO of The Institutes. We're a global not-for-profit, whose mission is to educate, elevate and connect the people-focused on risk management and insurance. You're listening to Predict & Prevent, a podcast that explores how technology and resiliency can prevent losses before they occur. In each episode, we learn about innovative solutions and hear from leading experts on how they are making these approaches a reality today. It goes without saying that technology has become all pervasive in our society. Every industry, from health care to finance, has been forced to adapt to the ever-evolving world of tech, lest they get left behind in the dust. And insurance is no exception. Over the last ten years, insurtech has expanded into all aspects of the insurance ecosystem, from underwriting and distribution to claims and customer experience. Without a doubt, many of these new tools are significantly improving the traditional process of risk transfer, detect and repair. But when it comes to predict and prevent, the technology landscape is still evolving. In this episode of the podcast, we'll be discussing two different approaches for leveraging technology to predict and prevent losses. First, I'll speak with Bob Marshall, the CEO and co-founder of Whisker Labs, a company that develops advanced technologies to prevent home fires and monitor the electric utility grid. Bob is also the co-inventor of Ting, an innovative device being deployed to millions of U.S. homes that's proven to prevent 80 percent of home electrical fires. Next, I'll sit down with Dave Tobias, the co-founder and COO of Betterview, a property intelligence platform that is mostly used by underwriters for scoring various property risks. Dave is passionate about the predict and prevent mission and shares great insights into how a platform like Betterview can also be used to avoid losses entirely. Along with my guests, we'll take a closer look at the landscape of technological innovations being developed and applied to some of the biggest risk challenges we face today, along with the goal of improving how we predict and prevent losses in the future. I hope you'll enjoy the episode and learn something new from the discussion. So, Bob, we're talking about predict and prevent in the various ways that the industry and technology providers can help make people's lives better and safer. And I know you as the CEO of Whisker Labs have a really good, interesting, I think, fascinating sort of take on that and product that you've developed to help to predict and prevent house fires. So, Bob, could you just give us a little bit of background on Ting, how it works, how it was developed and sort of the rationale and behind it?
Bob Marshall [00:03:08] Yeah. So, you know the start-up of Ting came about, you know, unfortunately through an event that occurred with my sister in law's house. So her house experienced an electrical fire several years ago, and it was a terrible fire. They lost literally everything in the house. They lost a pet. They dealt with the aftermath of that for, well, over a year, you know, outside of their home. I didn't know much about electrical fires at the time, but I am an engineer, I'm a data guy, a technologist, and we've produced some pretty cool products over the years. I challenged our engineers to say, you know, why can't we take some of the expertise that we've had and produce a product that would help prevent electrical fires. And we learned about it and then set out to try to do this, and, you know, lo and behold, it turned out to be much harder than we thought. There was about a two-year period that it looked like we were on the wrong side of the impossible line. We just couldn't, you know, we couldn't find the signal in the noise to detect it's, you know, it's loose connections and damage to wires that are little tiny sparks and arcs. You know, they can be inside of your wall. It took a couple of years before we came up with the data science and the signal processing to be able to detect those tiny signals. Ultimately ended up with a, you know, a very simple product. Right, it's just plug, smart plug, and we call it Ting, and you plug it in, you connect it to Wi-Fi and it monitors your house. And you know, we can prevent 75 to 80 percent of electrical fires today.
Peter Miller [00:04:41] That's amazing. You think about all the pain and losses and, you know, all the suffering that people have as a result of a house fire, loss of life, loss of pets in your sister's case. So I understand this develops over time generally. Is that correct? Like, in other words, there's small micro fractures and variations that the device says, hey, listen, stuff's starting to happen and maybe, you know, you're headed down a path of some risk. Is that kind of accurate?
Bob Marshall [00:05:13] Yeah, it is. And that's why predict and prevent is possible here. Electrical fire hazards typically develop literally over weeks, months. It can even be years. I mean, so it you know, your house could be ten years old. And ten years ago, an electrician was hammering a staple into the stud to hold a wire and he hit it too tight and it crimped the wire ten years ago. And over the course of those years, you know, the insulation deteriorates and you start to have these little tiny micro arcs that develop. And once you have a hazard like this, it can't fix itself. So it only gets worse. Same thing, you take an out where there's got a loose connection. So these things develop over very long periods of time typically, and we detect it very early. So we detect the arcing the problem connection, and then, you know, we're able to develop a pattern. Our data science allows us to know that it's an outlet versus the panel versus a heating pad versus a laptop power supply. It's amazing what the data is today. We actually provide a service to the customers of our insurance partners. So, you know, if you have Ting in your home and we detect a problem, we actually cover the cost of an electrician also. So if we need to send an electrician to come in and, you know, find the outlet in the wall that has the problem and fix it, that is actually all part of the service that we provide to the homeowner to make sure that we mitigate and prevent the fire.
Peter Miller [00:06:43] That's very fascinating to me that you can be that specific. Through an entire house, you can get actually to the plug, the wall outlet where that might have happened. That's fascinating to me.
Bob Marshall [00:06:54] Yeah. And that's developed. I mean, the thing that's really cool about IoT and data science these days, I mean, you're hearing about with ChatGPT and the amazing things the machine learning can do, and it's a very similar thing for us. I mean, the kind of data we get, the system learns very, very fast, you know. So, you know, the first time we found a problem with an outlet and verified that, you know, it was a problem with the outlet and we got the picture, you know, there was the charred outlet. We have high resolution data. Then the machine can be trained to look for that specific signal in the future. And it turns out that a lot of the hazards that develop in homes have unique signatures associated with them. So we can actually now predict that you have a problem in different parts or different devices and appliances in your home.
Peter Miller [00:07:44] That must be just personally enormously satisfying because it sounds to me like it's a business with a purpose. It sounds like you're saving people's lives. Is that the way you look at that, Bob?
Bob Marshall [00:07:57] We totally do. I get to tell you, Pete. I mean, our team is fantastic and we take it, you know, our responsibility is to protect the customer, their family and their home. It is every day there's nearly ten families that we save. And every one of them, you know, the quote from the homeowner that you just potentially saved my family and home from a devastating fire, it is hugely rewarding. There would have been fatalities in Ting homes statistically as well, and there hasn't been, you know, but unfortunately, we can't prevent 100 percent either. So, you know, it's a, you know, every once in a while we hear about an electrical fire in a Ting home. And that's I hate those days. There's not many. It's only 20 percent statistically of kind of what should happen. But, you know, we're very passionate, and we hate to hear that. We turn those opportunities into learning opportunities, too. Right. If we didn't prevent something, we dig into the data, we do the forensic analysis, we look at it, you know, can we train our models and our systems and the machines to detect this, to prevent it in the future? That's why generally, over time, we're able to predict and prevent even more fires.
Peter Miller [00:09:04] Because it's a learning system, right?
Bob Marshall [00:09:06] It's a learning system. That's a beauty. Yeah. Yeah. I mean, it is a constantly learning system.
Peter Miller [00:09:11] That's fascinating as well. So the system learns over time, it gets new data, creates new relationships, understands new potential issues, and then I imagine, please correct me if I'm wrong, everybody who has a Ting gets the benefit of that instantaneously.
Bob Marshall [00:09:27] Yeah, that's absolutely right. I mean, again, the beauty of IoT and networks of sensors is that, you know, when we, you know, every month say, you know, we push out new updates to our sensors and then our cloud machine learning is constantly updating.
Peter Miller [00:09:44] You know, we hear about predict and prevent and what can it do, and it's in saving insurance companies money. Sure it is. But it's also saving people's lives, maybe, frankly, more importantly. But I'd just like to explore for a minute the business of this. Right. Because it seems, like, you have data, do you have other uses for that data or other ways that you can utilize that.
Bob Marshall [00:10:07] Yeah. Look, I mean, the service that we provide our insurance partners, I mean, the business case I think is pretty straightforward. And there's a number of components to it, right? I mean, as with any kind of claim, I mean, the insurance companies know that, you know, the electrical fires, there's a certain frequency, you know, how often they typically happen in their book of business. And then there's a severity. Right. And when you do have one of those fires, what is the average loss? And as I learned at conference in New York, you all, well, know that the cost of the claims is skyrocketing, right? So when you do have a fire in a customer's home, the cost of that today is substantially higher than it was a year or two or three ago because of inflation and supply chain issues and all that kind of stuff. So, you know, so preventing it becomes even more important, right, economically. So if you just look at the frequency of the loss, the severity of the loss, and then if we can prevent 80 percent of those, then you can determine on a per customer basis how much are we saving. And it turns out that we can provide Ting service to a customer, and it will more than be made up by the savings from the loss, you know, prevented losses. So that's obviously the number one thing. And from a business model perspective is just prevent the losses. And that's been hard to document historically with, you know, some of the efforts that have been done with IoT, but here we have the data. You know, and then, you know, beyond the loss prevention, which is the lead, always the lead. Take care of the customer. Right. Make it better for everybody, you know, and then the customers love it. You know, the quotes that we get every day from customers are incredible. And, you know, they become very, it's a different relationship that the insurance carrier has with the family when, you know, they're not just paying the claim after some devastating event, they're actually helping prevent the event, the customer is very loyal. So we see increases in retention. You know, agents that are, you know, obviously selling new policies to customers, you know, when they can tell a prospective customer that, Hey, not only are we going to prevent you if there is a problem and protect you and take care of you, if there is a problem, we're going to help prevent the loss. We invest in technology to help prevent the loss. So there's a number of different economic benefits to the carrier, and that's all part of what we do, you know, with our partners. And on the insurance side is share the data, work together, focus on prevention, monitor the data and demonstrate that it's working for everybody involved.
Peter Miller [00:12:38] Yeah, it's an interesting analogy. If I might just for a second, I knew a friend of mine who had a limo business and he advertised himself as a chief safety officer. And instead, you know, most people would say, Well, I'm going to get you from point A to point B, well he got you from point A to point B, but he was very safe and he got me on safely. When he started doing that, it's now about caring. And sure, it's a service, but, you know, it's beyond that, and his retention in his business went way up. And it sounds like that's an opportunity for insurance companies, from what I hear you say, Bob, is to go, look, we're helping you and we're saving you before bad things happen. We'll take care of you for bad things happen. But that seems like a real value add that insurance companies can use and predict and prevents sort of way.
Bob Marshall [00:13:29] Yeah, 100 percent. I mean, I think, look, if you look back over IoT, you know, all the different devices over the last 10, 15 years, I think one of the things that has been demonstrated – and the security companies know this, right, the Simply Safe, the ADTs -- and you know what customers care about and what resonates what compels them to take action is safety. There's a lot of different things that can save you energy. They can make it convenient. And a lot of those things are great too, right? Nothing against all that. That's good. But when it comes to safety, people really do care. They don't want, you know, their families to be unsafe. So if you're taking steps, you know, to protect them, that really does resonate with the customer. And that's you know, that's obviously important. And in the long run, if you just take a, you know, a look strategically, if you can establish that kind of relationship to your point about the chief safety officer on the limo business, I mean, if that's the relationship that you have is that you're working on their behalf to protect them, it's a great relationship to have.
Peter Miller [00:14:33] Certainly is. And it sounds like you're well-placed to help insurance companies certainly do that. Bob, just one other question. I'm interested in your, you know, we're doing a lot on predict and prevent. You're ahead of the game. And certainly with the work you've done with various insurance companies, you're making it real. I applaud you for being, you know, have the vision to do that. But if you could just take a step back, can you just talk about predict and prevent maybe more holistically, and where you think the insurance companies can go with that and the value add that can happen, from your perspective as somebody who's doing it successfully.
Bob Marshall [00:15:15] Yeah, you know, look predict and prevent I believe is the future. And that applies not just to electrical fire hazards, which is where we focus, but it's fire and water and theft. Right. All the perils that impact customers of insurance companies. Technology and data can be applied to help predict and prevent losses in those regards. So, I mean, I think, you know, everybody should be looking at it across all categories of loss. First and foremost, I think it's got to be a decision and a strategy that is adopted at the highest levels of the organizations and the insurance companies. You know, obviously, all the carriers are super busy. They've got a lot going on, a lot of challenges to deal with. You know, so I think to be successful, it's got to be something where leadership in the company says, you know what, we do want to transform the relationship with the customer, we want to invest on behalf of our customers for their safety. And we think that's going to be good for our business, good for our customers. So you got to start that it's going to be something because it's not something you can just deploy a piece of hardware, whether it's something for fire or water or theft, and it immediately pays for itself in the first month or the first year. It does not. I mean, you know, even our product, right? If you had to pay to put Ting in a home and you were only measuring the economic benefits for one year, unfortunately, the math just won't work. You have to take a strategic view that you're going to take care of your customers. So I think that applies to fire, water, theft, whatever you're going to do. Right. You know, then I think the key is, you know, to make sure that whatever you're offering to the customer is simple and easy to understand. Some of the things that have failed historically have been complicated for the customer. I mean, if you expect the customer to do a lot and have to work very hard, then it just doesn't work. I mean, homeowners are busy and families are busy. They got their own life challenges and everything else. So if you're going to make it difficult for them to benefit from the predict and prevent technology, then that won't work, right? So, you know, so it's got to be simple. Then, you know, the system, the marketing, so we've learned a ton that I think applies whether it's fire or water or theft. I mean what we've learned along our journey is incredible from, you know, how to market to customers, you know, how do you engage the customer, get them to say, yes, I want this technology, that's not straightforward either, because that's not what insurance companies sell, right? They don't they don't sell these kinds of things. So but we've learned how they can talk to their customers in a way that is efficient and easy. And the customers say, yes, I want that. You know, then, but the systems, the partner with us, you know, when your customer, you know, when, you know, an insurance company customer says, I want Ting, we've got to have APIs and data handshakes back and forth so we can ship that product immediately. And then we have to know that it's installed. Right. And we have to let the insurance company know that it's installed and it's online and it stays online and all that stuff, all those things. And the telematics data that develops over time from there, not only individual sensors, but the network of sensors is great, but those handshakes back and forth with the, you know, a lot of added value is in the data that the insurance company can get. But having those systems in place is not something that's generally in place today in that insurance companies. Right. So all of those the things that we have learned, I think, apply, no matter what carrier wants to do. And, you know, again, that's why it's got to be a strategic decision, too, right, because it is new, but it is good for your customer. It is a great long term business opportunity, but you've got to commit to it and work with partners that are going to be transparent with you, share data and learn along the way.
Peter Miller [00:19:13] Well, that's great advice, Bob. I mean, it sounds like it's a community, right? A lot of moving pieces, but a lot of people and what I hear you say is it needs to be executive level commitment.
Bob Marshall [00:19:25] I think it does need to be executive level commitment because, you know, look, I mean, when we work with a carrier, you know, we, you know, at the beginning, yeah, we work with marketing, then we work with the operations team, we work with the data scientists, you know, we work with the, you know, the actuaries, and we share all this data back and forth to demonstrate the economics of what's going on. But obviously, first and foremost, demonstrate that the customers love it and they appreciate the insurance company and share that data back and forth. But there's a bunch of different departments and parts of the organization that will be touched, even subrogation. So we're dealing with, you know, we work with subrogation because our data actually supports the fact that some of these losses, you know, should not be actually be paid for by the carrier because it was somebody else's fault. So, you know, it touches a lot, but we've learned a lot over the years. And hopefully we can contribute the success of predict and prevent not only for what we do, but and water theft, everything.
Peter Miller [00:20:26] Bob, thank you so much. I appreciate your time today, and thank you for your efforts. You know, I think at saving people's lives every day is an awesome thing. So appreciate it very much and I appreciate your time today.
Bob Marshall [00:20:38] Yeah. Pete, thank you very much. And obviously, The Institutes and what you all are doing is supporting all your customers and having the vision to lead, you know, that the whole industry to, you know, to the future of predict and prevent is super. And you know, we wish you all the success.
Peter Miller [00:20:56] Bob story is fascinating and I was astounded to learn that Ting can prevent 75 to 80 percent of electrical fires. The fact that the device is saving about ten families a day is truly inspiring. These results demonstrate how real time sensors combined with machine learning are making predict and prevent a reality today. And Whisker Labs approach to partnering with the insurance industry provides a roadmap for other technologies to be adopted and deployed at scale. Next, let's hear from David Tobias, the co-founder and CEO of Betterview. Dave brings a different approach and perspective to how technology can enable predict and prevent. And he shares important insights on what it takes for technology companies to successfully partner with insurers. Dave, great to meet you. Thanks for coming in to talk to us today.
David Tobias [00:21:49] Thanks for having us, Pete. Excited to have this discussion.
Peter Miller [00:21:52] If we could just start out with just a brief overview of your background, how you got to be a co-founder of Betterview.
David Tobias [00:22:00] Yeah, happy, happy, happy to do that. So my journey is kind of a unique one. I really grew up in the insurance industry. So my father started the insurance inspection company before I was born, so boots on the ground, lost control of work, commercial and residential. I really grew up around that, traveling with him, going on inspections, you know, into businesses, into high value homes and, you know, got to travel across California and sort of the western United States with him doing that. And I went off to college, got the itch to kind of be an entrepreneur and be involved with business in general, and had the opportunity to eventually take over that business. I started selling for that business and literally going door to door, like, going showing up at insurance companies, cold calling, back when you could still do that. You know, the business started to grow quite a bit, and we ultimately had about 500 inspectors across the country. And in that business, I had insurance companies telling us they needed more and better roof data. So, you know, we tried to solve that within the inspection company by deploying roof poles, which are essentially painter's poles with camera, camera attachments on top. We tried to get inspectors to go up on roofs. None of these things really worked. And that was sort of the impetus to starting Betterview. But ultimately, you know, the business was great and had the opportunity to sell it a few years ago and served some great background for me and learning the ins and outs of insurance.
Peter Miller [00:23:38] That's pretty unique. I mean, so you started out in this industry or sort of attached to this industry from the get go. So you have a long experience then with the types of issues that Betterview that you are trying to solve. I mean that's pretty neat. What exact problem you're trying to solve? I think every business has to solve a problem. Right. And I'm just curious, when you think of your business, what problem are you trying to solve?
David Tobias [00:24:04] Yeah, it's a you know, it's a great fundamental question. I think our product is helping insurance carriers underwrite risk better. And that can mean a lot of different things to a lot of different people. Right. You know, pricing and effectively servicing their customers, you know, customer experience element of it and predicting and preventing loss, which is core, you know, core belief of our company that we can actually effect change with that predict and prevent versus just repair and replace. And so for us, ultimately how we're doing that is it really started originally with using aerial imagery. So manned aerial being one of the course and using computer vision machine learning to extract data from that, to really get to the condition of a roof in a property and do it near instantaneously, and now, you know, basically instantly. We're trying to fill this void around conditional information around a property as well as just data in general that's been historically hard to get. Maybe the only way to get it would be to send an inspector, you know, out, send boots on the ground. Or a lot of companies were just relying on what was coming in the application. Right. And that data is flawed for a variety of reasons, whether it be from the insurer or the agent, because some of the data points that are asked on these application forms are hard to get. Right. So, you know, it's fundamentally trying to fill in the information gap with up to date cost-effective information about the condition and elements of a property but doing that at massive scale and doing that at the beginning of the insurance process. So all the way through the quote rather than post buy or send out an inspector and so on and so on. What if you could bring that information all the way to the front of the process and know the condition, price it effectively, know what needs to be predicted and hopefully prevent it at second one of that policy, not, you know, in the worst case scenario, when the loss comes in, when the claim comes in.
Peter Miller [00:25:57] So you sell to insurance companies, you produce, like customers would, their policyholders would see the product through an insurance company. Is that correct?
David Tobias [00:26:05] Correct. Yeah. So insurance carriers are our main, you know, go to market.
Peter Miller [00:26:10] You are using drones originally, is that correct?
David Tobias [00:26:13] So when we first started the business, drone technology was really taking off. No, no pun intended. You know, we were seeing how we could leverage that to get this roof data, because of the other things that I mentioned, like we tried to get inspectors to go up on the roof, we tried, you know, painter's poles with cameras on. None of those things really worked. And so, you know, drones were really just autonomous flight and the technology was really advancing. And so our original idea was we're going to use drones to go capture this imagery. We develop software on machine learning to take the drone imagery and turn it into reports that insurance carriers can use. The thing now that was really interesting, a lot of learnings, of course, from that piece of our business history, but the drones really solve the problem. They could get you very accurate, very detailed analyzes of roofs and properties. But you were still sending a person to the site with the drone. It was costly. It was slow. It didn't, it wasn't a quantum leap really. And the experience other than it was a new data point that people didn't have before. So drones, you know, we did tens of thousands of drone inspections for carriers. And, you know, we saw that, you know, could be a viable business, but it wasn't going to scale in the way that our customers really wanted. So that's when we were able to use some of the technology we'd already created on pre-captured imagery. And, you know, we sold off the drone piece of the business to DroneBase that was a partner of ours so that we could give our customers a place to still be at those drone inspections that they wanted. You know, it was a really good learning for our company and all the people within our companies. It's a, I like to say that, you know, we fell in love with the problem, not the solution. So just because we had drones and we created tech, I think we don't actually really care what the technology we apply to it to solve the problem, but if you really deeply understand the underwriting workflow, the problem of the information asymmetry that was out there, whether it's satellite, whether it's drone imagery, whether it's manned aerial, who cares, right? It's can we solve the problem? Can we do it in a scalable cost-effective way that fits into the workflows that already exist? And so, yeah you know, that was our start in drones. It's you know, really gone well beyond drones now today.
Peter Miller [00:28:36] What you're saying is it's not a solution kind of run around looking for a problem. You're actually solving a problem. And that's kind of the focus. Right. It's not, you're not enamored with the technology in terms of it. That's what I hear you say, Dave.
David Tobias [00:28:51] Yeah, the technology is a means to an end, right? And I think you see this sometimes sort of people come with some new technology and they think, you know, they can come into insurance and totally upend it. It's not to say they can't try it. You know, I think people come into industries they don't know all the time and they tried to make an impact there. And it's possible. But you have to remember, we're in a regulated industry that has a lot of processes that have existed for many years. A lot of our team comes from insurance, and we think that's really important. We're one of the only ones, I think maybe the only one in the space that has anybody on the founding team from insurance. It's kind of striking when you look at that, right. So, understanding the problems in really have, I mean, I literally lived with the problem from for many years. You know, we think that's important. It gives us an advantage.
Peter Miller [00:29:41] Nothing like experience. Right. So, tell me how does a typical engagement look? So, let's say I have a home and I want to take advantage of this, how does that look like? How do you get data and how does your and high level the process work so that you can predict and prevent?
David Tobias [00:29:59] Yeah. So you know, the carrier so, you know, coming to us, and there are many times saying either we have a condition problem, so like we know we have a lot of roof claims or we know that we don't have a full picture of the property or in this case like a home. Right. We know that we have properties on our books that have pools that we don't know about. We know that they have, you know, roofs that are worse than we think they are. And we write in Florida, and we know this is an issue. Right. So, you know, typically, you know, it's approached from that side. They're coming to us, you know, and they're, if it's during the quoting process, we're getting an address, we're instantly providing data back, saying this is the condition of the roof, here's what's on the property, we have proprietary scorers that roll up this information because we dissect it. We use this manned aerial imagery. We're going to pull the most recent image, process it with the computer vision. It's going to tell us if there's staining, ponding, missing shingles, all these roll up into scores. And over the last six months, we've also been filing rates in the various states using our scores. So, we have a wildfire score, a hurricane score and many others coming now. And we've been filed across 10, 11 states so far. So, people can use this all the way at the forefront of the quoting process. And so we're going to run that. We're going to extract this information. We're going to be able to give you conditional things like the condition of the roof, the score of the roof. We're also going to be able to give you predictive score. So, what's the probability in a while if it's in a wildfire zone, this building is going to burn? What's the probability if it's in a hurricane zone, that it's going to be impacted? And then we also have just elements. Is there solar panels? Is there swimming pools? Is there debris in the yard? So it's a combination of these conditional elements and, you know, physical property elements that go into quoting, you know, and go into monitoring renewals as time goes on.
Peter Miller [00:31:51] That's really cool. So in other words, if I'm a carrier's underwriter. I'm an underwriter and a carrier, I guess I should say, then this would become immediately available to me. I would look at some addresses and, if I'm a client of yours, would that be part of my quoting mechanisms, quoting screens or built in?
David Tobias [00:32:12] Yeah. So it just depends on where they implement us. But many carriers are using us in the quote, they're using us post buying and many are using us in their renewal process. So when a carrier comes to work with us, they may, many times they're putting their whole path, all their policies in force into our system, and we're monitoring them for change, right, so that they can be a little more proactive. And sometimes they'll just monitor for change right before a renewal, decide what they want to do with it. So it really depends on where we're getting plugged into the process, but we're also integrated. We have out of the box integrations with Guidewire and Duck Creek and OneShield so that we can make it easy. I mean, I think as easy as we can, again, understanding that there's many processes within insurance that already exist. How can we fit into those processes, you know, we think that's important versus trying to just upend the process altogether.
Peter Miller [00:33:06] Yeah. I think your point is very well taken. Right. The idea that, as you said earlier, it's a heavily regulated industry, and, you know, if you try to go in, I'm hearing you say, if you try to go in and change your workflow, that's a big deal, at an insurance company so you can integrate.
David Tobias [00:33:25] Yeah, it's a big deal. And, you know, I think, there's opportunities to do that sometimes and change your whole workflow and those should not be ignored. But we're trying to take something, you know, in predict and prevent, which is effectively what those you know in my background those lost control inspections did. We would go out to a property. We would look for problems. We would issue recommendations that would then get, you know, shared with the agents and the insurance. We're trying to do pieces of that but digitally. You know, that process is slow and expensive. So the fact that we can really democratize predict and prevent using technology, I think is really impactful. And to get people to start really viewing everything through this predict and prevent lens, you have to make it as easy as possible. So coming in and trying to say you need to change your whole workflow, you know, ABC insurance, that's a that's a more problematic and a hard discussion to have versus let me tell you how we can augment the processes you already have, make them more efficient while also reducing, you know, improving your loss ratio, your expense ratio and your customer experience. You know, we talk a lot about predict and prevent, but there's a third P, which is price, right. And how can you help price effectively to make sure people can get insurance, but it's priced appropriately to cover that risk.
Peter Miller [00:34:50] You have a lot of experience and very successful experience working with insurance companies, as you say. So what have you found workflow. I hear you saying integrate into a workflow, but what other success factors are there for an organization like yours when trying to work with a carrier, like a particularly technology based? What have you learned?
David Tobias [00:35:12] It's a great question. I think there's so many learnings across the way. I think finding initiatives that already exist within the carrier is important and seeing how you can plug into those initiatives. You know, trying to create a net new initiative can be difficult. I think that looking for the forward thinking champions within a company is really important. People understand, you know, the world is changing in various ways, whether it be economic or climate, and you need to react faster. Right. You know, these things are happening today. You know, I'm in California. What we've experienced over the last few months, weather wise, is unlike what I've ever seen. And I grew up here, right? So places that didn't have roof, California was never seen as a high roof risk place. Well, I could tell you we had 60 mile an hour winds in Burlingame where I grew up. You know, I grew up right around there and I live there now. That's unheard of. You know, that's not normal. Right? So now pretty much every region is a CAT region in the country. It's just a question of which CAT you might be affected, and many regions are now affected by two to three different types of catastrophe, you know, possibilities. So I think really finding folks within a carrier who are seeing these changes and they're trying to react and trying to get ahead of them is important. And, you know, I think those folks exist at every carrier. Everyone can be hard to find them, and it can be hard to find ones that actually want to make some of these changes. But I think there's more today, there's more understanding change has to happen versus before maybe even six, eight years ago, it was more of, well, let's wait and see how the market develops.
Peter Miller [00:36:57] I think you have your vision certainly for how to work with carriers is pretty compelling and pretty spot on. Are there any other barriers that you see to adoption of this technology other than kind of some of those that you just mentioned? You know, the reason that you are very attractive to us and I admire what you're doing is the predict and prevent, right? Because the idea that we can avoid losses or mitigate them in the first place so that folks don't have to go through the pain of a claim. And I'm curious, do you see organizations, culture like insurance carriers accept that or is they from a cultural point of view, have you run into any resistance there?
David Tobias [00:37:41] Yeah, a little bit. I think that's changing, though. You know, I think there is some element of out of sight, out of mind, right. Like, well, if we don't know about the problem, then we don't have to react to it. But I think given what's happening, you know, with climate especially, it's hard to ignore right at this point. And I think that there's also this understanding from the carriers that they have to, they have to interact with their insureds in different ways and more than they did historically. So we're all used to, you know, getting Uber Eats delivered to our house now when we're, you know, having constant engagement from our brands, you know, sometimes getting instant gratification, right. Whereas in insurance, a lot of the touchpoints still happens once a year when you get your bill. Right. And so I think where I see so much opportunity, I think there's starting to be a mind, you know, shift in this space, is like carriers, we have, you know, 70 plus customers. Right. And my carrier is a customer and they buy data from Betterview, but they don't share it to me yet. You know, as a carrier, why not use this data to say, you know, here's the health of your building. We're just given this to you as a value add, right, like use these positive touchpoints and the benefit of, you know, helping predict and prevent. You know, it's, I don't go up on my roof. You know, having that data for folks that aren't doing these things, because they're bad insureds, it's just these are hard. It's dangerous to go on your roof as an example. And why not use this as a touchpoint to improve that customer experience, give more value into the chain, and ultimately help people be more proactive and take these preventative measures that they probably would do if they knew. I think that's starting to get embraced in the space because again, I think that there's a recognition that the old way of doing it is not going to be sustainable for too much longer.
Peter Miller [00:39:39] Couldn't agree more, couldn't agree more, Dave. I'm curious, you have 70 customers. If I was one of those 70 customers, just in general terms, Dave, what would I see? I want my business change if I adopted, you know, your, your approach. Like what kind of results would I see?
David Tobias [00:39:58] Yeah. So I mean we have, you know, one carrier that saw a 31 percent improvement in loss ratio after instituting Betterview. Right. You know, that's an extreme example. But, you know, we see a lot of that. We see loss ratio, expense ratio improvements, we see optimization of inspection workflows, which obviously I know really well. But you know, every people run things through, asked to decide where did they need boots on the ground. Right. Where do they, you know, where is this? The place looks great. I don't need to send anybody versus the one that has a hole in the roof that I'm not going to quote or renew anyway. Why am I going to send that additional expense? So you see these expense improvements. You see these loss ratio improvements. But I think one of the ones we're most proud of is the customer experience improvements, because we do see some very, you know, progressive carriers. They're sharing the Betterview outputs with their agents in a lot of cases to say, we're making this policy change, here's why, or we're going to make this change unless you do X. And what we found in, we have a couple of carriers, we've talked about this publicly. What they have found has been that the conversation with the agent is much better. Right. Instead of just saying we're changing the premium on this policy, they're giving them the why, right. Or we're canceling this policy, here's why. There's a hole in the roof. Right. And so, you know, I think there's just a lot of benefit there. And so, you know, on day one, if you were a carrier working with Betterview, you would know where all your high risk properties are, your medium risk or low risk. You know, instantly you would know where you have some potential problems, where you might have some exposure issues in a given, you know, geographic area, and then you'd be able to monitor those over time and look at the risks, you know, in addition to your new business, of course, being able to track what's coming in and see what's in, what's good and what's bad.
Peter Miller [00:41:50] So it seems like predict and prevent, right, can help your underwriting make you underwrite more accurately, lower your losses, right, and give your agents some of the why, so that they can talk to customers. And it sounds like there's some opportunity to, as you're saying, for companies to share a data like Betterview's data with them so that it's maybe more of a risk consultant, perhaps kind of relationship rather than I'm going to collect the premium and indemnify you.
David Tobias [00:42:22] Yeah. And I think given the change, right, whether again, whether it be economic climate or otherwise, given the change, you have to be more of a consultant these days, just providing the coverage which is the base level that insurance is must do and needs to do and always, you know, protect people and spread that risk. That's an important tenet of insurance, you know, now and into the future, in my belief. But you have to be more of a consultant. You have to help predict and prevent because you look at what's happening with reinsurance rates right now. You know, this is not sustainable, the current state of things. And so I think you have to look for ways to work with insurers, work with agents to be more proactive, to really, you know, help solve the problem, which is good business, too. That just happens to be good business to help your expense ratio and so on and so on. But, you know, most people do not want to have this catastrophic thing happen, say, like a, you know, a roof collapse or a wildfire destroyed their home. I mean, these are the, yes, you might get a new home, but these are, you know, very challenging things. And the statistics show that, you know, it takes many, many years for people to recover financially and mentally from some of these disasters. The toll of doing some of these things and the claim, the loss, it goes on for many, many years for an individual and for a community. For us, we think it's partially our job to help prevent the preventable losses to the extent we can. Not everything can be prevented, but a lot a lot can with some simple actions.
Peter Miller [00:44:00] So, Dave, you've been in this for your whole career, right, since you work for your dad's company and then started your own company, Betterview. And you mentioned some of the things you see opportunities for carrier is going forward. But I'm curious also if you see any new technologies or if just take out your crystal ball for a minute and say, where do you think this is going? Or, you know, if we think down the road, what other things are coming kind of online or near online that you're excited about?
David Tobias [00:44:29] We do a lot of work with, you know, machine learning, AI to turn unstructured data, you know, like imagery into structure data that can be action upon. But I do think there'll be more and more opportunity to combine some of that with the IoT sensors, some of which that are already out there, some of which are still coming and really put all those things together. I think that's kind of what's lacking. There's a lot of point solutions around IoT, but there is no one sort of epicenter to pull that together. And I remember talking to one carrier that we work with that with the Texas ice storms. You know, they had the IoT sensors out there. They told the insurers this weather was coming, you know, set, you know, set your faucets to drip and so on and so on. And they could watch the data. Basically, nobody took their advice. Right. So I think there's still a lot of opportunity there. And I think ultimately those things will start to come together and drive to some actual action and change. I mean, I think I'm hopeful, you know, regulators kind of, you know, start to, you know, look at some of these new technologies, not as, you know, something that could be bad, but something that's needed to help react, you know, faster in the space. And, you know, I think there's more interesting technologies around like SAR or synthetic aperture radar. We've partnered with some folks on that to monitor floods, flood depths and wildfires. Right. So I think there's more and more data available. The key is really how does that data become consumed, how does it become actionable so you can actually get it in the hands of the insured. And I think that's where AI comes in. I mean, obviously there's a lot of talk about GPT and some of these other transformative AI technologies, but for me, it's all about how do you put that to practical use and get it in the hands of the people that actually need it. And I think the technology over the next five, ten years will really, really drive towards that in a much more proactive nature.
Peter Miller [00:46:25] Well, Dave, thank you. Certainly when we think of predict and prevent, you've been doing it for a long time and doing it very well and very successfully. The approach you've taken is certainly on the forefront, and we're really, really grateful that you would take the time today to share some of your thoughts with us, so thank you.
David Tobias [00:46:43] No, thanks. Thanks for having me and letting us talk a little bit about what we do. I mean, I think, you know, predict and prevent is, you know, something we as a company and the employees of Betterview care deeply about. And I think the technology is here to actually help people do that. I think that's what we're so excited about. And, you know, I think there's just a lot of opportunity if we can prevent a few losses, a few of those catastrophes for those insureds. Right. That's really important to us as a company and as people. And I think it's our duty to use the technology to help actually, you know, affect that change.
Peter Miller [00:47:22] Great. Dave, thank you very much. I appreciate your time. Betterview is another great example of how innovations in insurtech are improving the industry and as a result benefiting customers. After hearing stories from Bob and Dave today, it's becoming clearer than ever that it's essential we continue to drive innovation forward in insurance and risk management. We know it's not always easy to make large scale changes in an industry that is old and established as insurance. However, as Dave mentioned, the current state of affairs won't be sustainable long term, so it's crucial that we act now. As we learned last episode, there's a real sense of urgency when it comes to enacting a predict and prevent approach. And innovative technology is a big piece of the puzzle that will help us all move forward. I hope you enjoyed this episode of Predict & Prevent. A big thank you to my guests, Bob Marshall and Dave Tobias. And of course, thank you for listening. See you next time