Guidewire Software, Inc. (GWRE) Earnings Call Transcript & Summary

Hello, and welcome to today's webinar, The Future of Underwriting in the Small Business Market. Our presenter today is Jesse Lou, Principal Product Manager for Guidewire Analytics and Data Services. [Operator Instructions] And with that, I will turn it over to Jesse to get started.

Thanks so much, Lizette. And hey, everyone. Thanks so much for taking the time today. My name is Jesse Lou, and I'm the Product Manager for Cyence for Small Business, with the products within our Analytics and Data Services business unit at Guidewire. So what we'll do today is to share a little bit about our views on kind of small business market, what we see as some of the opportunities and challenges and why we decided to further explore this space and provide more value to our customers by creating this analytics tool to basically differentiate small business risks. Risks that we feel might look similar on paper. So I'll talk through a little bit of that, the data behind it all and then talk through some of the validation and improving the value that we've gone through with our partners and our customers. And at the end, we'll open it up for Q&A. And so in the meantime, I think if you look on your console, there's a Q&A section tab. So feel free to punch in any questions at any given -- at any point during the presentation. And at the end, we'll go through those and talk through any of the questions that the group has. So I wanted to start off with kind of getting the lay of the land here and talk through what we see as some of the market opportunities and challenges. So in thinking through the small business market, I think it's immediately obvious to a lot of carriers and players in the space, it's a definite opportunity, it's a growing opportunity. More businesses are started every year. I think a noticeable amount of employees actually work at small businesses. But as you think about the changing evolution of these small businesses, the business centers are behaving more and more like consumers. As we think about all these other technologies and these other services that exist to us today as consumers, and so when you think about and you look at some of these polls that are out there about what these small business centers are looking for, a lot of these small business centers aren't that familiar with insurance. And so they definitely need some guidance and some handholding in that process. But at the same time, they're also asking for a very streamlined, easy-to-use, self-service kind of approach when they are buying insurance and they're looking to make that process as easy as possible and as a little bit of a headache as possible. And so I think that really leads to growing opportunities in the small business space. I think metrics and figures will vary. But I think, by some estimates, there's over $100 billion of DWP in the U.S. written for small businesses across lines. And so I think that's something that we saw as really, really exciting an opportunity to explore. But at the same time when we talk to our partner carriers in our customer base, we quickly realize that across the board, the way that small business insurance is being underwritten right now is just not efficient, especially from a cost efficiency perspective when you think about how little small businesses pay in terms of premium and the amount of time and effort it takes to actually go and do the due diligence and process it, et cetera. Not to mention the fact that these business owners are looking for a faster turnaround, low friction, and then compounded by the fact that if you actually wanted to automate this process or structure, some kind of approach to streamline this overall underwriting process, it really comes down to having the underlying capabilities to process as quickly en masse in kind of this volume or high throughput approach. But what you really need is actually the data underneath to be able to make faster decisions and to prepare your underwriters to either triage risks that might require more attention and time and then to also service the ones that don't require any further attention and time that can get passed through. So when we look at the market, we saw this opportunity, and we wanted to be able to bring value by building some kind of tool that can really quickly determine and assess the risk of a small business, and then either triage it and serve it to an underwriter so that you can then further explore or enable carriers to take a more low-touch or no-touch underwriting approach. And so I think that's what really takes us to this maybe not just a view of how this low-touch underwriting world can look like. Because on one hand, you can automatically [indiscernible] a lot of risks more straightforward. That's the time that triage and review other risks that essentially get floated up to the top using risk assessment tools that could make these decisions very, very quickly, and then overlaid on top of that with business rules and decisions within your policy framework. And so when we think about really the [ potential ] value that we want to bring to the market, it is how do we help accelerate the adoption of technology and data across the P&C industry -- insurance industry. And this is kind of where we're starting off with small business as -- where we feel like a really interesting opportunity to do that. And so if we can then double-click into one of those accounts that you want to triage, then this is where we can then augment the ability for underwriters to really dig a little deeper and to be more focused in their exploration if you were to have some of these different risk factors surfaced to say, "Hey, maybe you should take a look at these 2 things on 10 or 20 different potential areas." We want to be able to support the underwriters. And that's making efficiency of their time, again, through the use of analytics. So what that looks like kind of one layer behind the scenes, if you imagine, looking at the far left-hand side of the screen there, a business owner might be using an iPad to buy or purchase, and when they do, they're surveying the information there, and what gets ported over to the Cyence data listening engine or the Cyence risk assessment tool, at a minimum, is really just the name and address. And then we can take that information and run a lot of these different data collection and these sophisticated risk assessments behind the scenes, serve that information. That's the carrier in the form of a risk rating and a set of risk factors that I'll go into later, and have that flow through the underwriting guidelines to then, ultimately, to the eyes of that policyholder, that person applying for insurance, they can see a really streamlined end-to-end process that really all it takes with us. Now when we think about these kind of future state views of what low-touch or no-touch underwriting could look like, especially when enabled with data, I think it's not so controversial that there is a place for where idea can help augment and streamline a lot of opportunities and avoid having underwriters really peanut butter their time across all these different accounts that -- where only a small subset really deserve their time and attention and focus. And so I think the idea of having data be an integral part of that underwriting process really across the full insurance value chain, I don't think it's controversial. But I think it's also nontrivial to actually take that idea of integrating an intimate data and how to come to life. And so that's what I want to call out here and talk through this kind of thought process and these steps that we've seen in our conversations with carriers that we've had to go through. And so first, there is this idea of capturing data that -- all this data that's out there. And when we think about all this data that's out there through a lot of these different platforms and technologies, there's a lot of digital exhaust, so to speak, that's being created and generated on a daily basis. And so as all this data is created out there, I think a lot of carriers recognize that while the data is there, it's actually hard to systematize it, capture it, distill the value of that data and have it flow through to the underwriting process. And so the first step of that is really to have some sort of tool to go out and to know where to look and to collect that data in some kind of automated productionized way so that you can then set up the processes to distill the value and extract those insights from that data. And so you might want to take an AI or a machine learning approach, apply natural language processing to look at the words that are being said, look at historical analysis and look at how things are trending, look for outliers, et cetera, and to really figure out what are the insights that you can glean from any given set of data and then to productionize that and turn that into a pipeline, so they can collect similar types of data on a continuous basis. And then comes insurance tooling, where you want to take that data and you want to bring it into these different tools that actually drive decision-making in these different use cases. And so up to this point, we've been mainly talking about underwriting, but it's really what component of that overall value chain. And so whether it's underwriting, pricing, prospecting or claims, we want to be able to have insurance tools that take all this data, distill the insights, service that data in some kind of readable or ingestible format, whether it's human or machine as the IS, and then ultimately, how that flow into the workflow. And so you have all this data, you have the tooling behind it, and then ultimately, I think where rubber really meets the road and where there's truly value that can be appreciated is if you're looking at the screen that you normally would as an underwriter and now you have more information, where now you move to different workflows because of information that's gathered by these tools. I think that's where the value creation really happens. And that's where you really can start seeing either higher throughput because the different folks along that value chain are more efficient, they could focus their time better, and I think that's where you really see the cost savings impact to the bottom line. And so this is where I think, I'd like to call out, that Guidewire really brings that full stack solution from the very beginning of the data ingestion phase through to the data synthesis, tooling and ultimately the workflow. So what I'd like to do right now is really quickly open it up to the audience here and to the group and learn a little bit more about what some of your roadblocks are when it comes to taking in third-party data to streamline underwriting and broadly other processes. I have only a couple of buckets here just to see if we can get some interesting insights, whether it's more internally driven or whether some of these roadblocks are more externally driven. So we'll take a minute or 2 for you guys to punch in your response here. All right. Before we go there, 10 seconds. All right. Yes. So I think this is really interesting. Looking at where existing solutions are not being meet as being the biggest one, followed by a close second around internal prioritization. And I think that echoes some of the conversations that we've had with our partners, where either it's hard to really understand it and dig underneath like a service level of explanation of what the capabilities are, or it's hard to see kind of how these different tools provide business value without either going through some kind of more involved pilot process or seeing how this data, while it might seem really interesting or valuable to other carriers, ultimately, I think some of the challenges that we've heard are, how does this actually impact me? And how is it going to be relevant for me? So I think that's interesting to see here. And then for the others, we don't have an option here that can have you type in the answers here, but I think, later, we'll have -- I'll share some contact information to our team, and we'll really appreciate it, be interested in for the folks who said other to share more about their views. So hopefully I'll talk more like a high-level overview of kind of the why and a little bit of the what around what we're doing with Cyence. So we'll dive into this next layer of detail around what is the data, what are some of the approach that we take and some of the thinking behind the scenes. So it really comes down to this underlying approach of what we call data listening. And it's this approach to collect data of what we call Internet scale, where we're going to look at data that's somewhat obvious to collect. A lot of this information comes in on applications, but also data that is less obvious. And a lot of this is also a legacy of the work that we did within cyber insurance when we first started out a few years ago and now expanding to all lines of business. We're finding that a lot of characteristics around looking at a company's digital footprint actually translates well to us understanding a little bit more behind the scenes around what the behavior looks like, what the management looks like, et cetera. We then curate that data, synthesize it with our AI and machine learning techniques. And then that's where you can then distill those insights into something that's more usable for these different use cases, whether it's underwriting, pricing or marketing. And I think the actual output of this data also differs -- is modulated based on whether it's a human or a machine audience on the other side and what kind of needs they have in terms of doing their job. So as you think about these different use cases, we talked a little bit about underwriting pricing to an extent. But I think what's also really interesting is around this marketing and prospecting use case, where I think the ability to actually make a decision and learn more about a business before they even come to your door is this ability to collect data from the Internet and from the wallets out there on demand and get it back in a very few seconds using very little pieces of information, just like your business' name and address. And so with that, it unlocks -- it opens a lot of doors for marketing, for example, where now you can start to prioritize how you go about that process of getting customers and you can really fine-tune that segmentation to really target the set of audiences that you want to go to. And that can really help cut down and focus your acquisition spend, as an example. So I also have another quick poll to learn a little bit more about -- for each of you -- to learn a little bit more about how are you guys thinking about using data and analytics. And of these, I'm sure you're focusing on many of these. But if there is the biggest focus for data and analytics across these different use cases, really appreciate your insight here. All right. I'll give it another 10 seconds. All right. So it's really cool to see everyone's feedback here. I know that this webinar is set up as more of an underwriting -- future underwriting webinar. And so I think it makes sense that the bulk of the responses are towards streamlining underwriting processes, but also interesting to see that our -- where actual -- I think it's also very tied into the underwriting process but also claims and marketing as less of a priority. So it could be just based on the audience that are out here. But very helpful. Thank you. So by then, now it's kind of this peeling back one more layer as we go behind the scenes in terms of how we think about idea collection. So we'll start off with a little bit of the approach. I'll go to some of the examples of the data that we collect and then we'll go into looking at the validation of that afterwards. So when you think about the data that we want to collect, we definitely combined all the different resources that we have in-house as well as leverage the expertise that our partners have. And so it's very much a collaborative process, where we're actually going out and building out these sets of data and our products and our tools. And starting off, really, as you can see on the left-hand side there, with these data-driven hypothesis. What I'm showing here is just an example of some of the causes of injury tied to different OSHA. And when we looked at it, we thought that, hey, maybe this might be a good place to start to think about how we're measuring workers' comp risk, for example, and what types of data we should really collect to try to and quantify that and build that to a risk factor. And so we've been working with our partner carriers in coming up with what some of the types of causes are more top of line. We took one that's really -- we took a couple that are related to motor vehicles, machinery and boats. And how we then turn that into insight is we go through all these sort of websites. And for businesses with websites, we'll basically crawl through every single line of pages, look at all the words, click out the links, look at the pictures and distill that into, as an example, you can see here in our work cloud. And we're going to be doing that for millions and millions of different companies. We then go back and assess for the companies that turn out to be related or worked with both single vehicles, what are the words that show up more on their websites related to water services, related to their customers' feedback, et cetera. And that bar chart right there on the top right-hand corner was kind of completely algorithmically driven chart where words like boat, trade, marine actually surfaced up to the top and automatically telling us, "Hey, for these types of businesses that work for vehicles, the words when they show up on the website such as these, actually indicate that these businesses work at boats more than vehicles. And this is one example, again. But if you imagine, you can take this set of one [indiscernible] approach and attach that to every single one of those causes of injury on the left-hand side bar chart, in addition to really any questions that you might have when it comes to does a company do delivery. And if so, is it fulfilled through like a Grubhub or there are actual employees that go and do the delivery, among many other questions. So that, I think, is one of the pillars of how our technology works to go out and try to prove or disprove hypotheses and then turn that into insight. And so one of those applications could be things like industry classification, where we feel like there's a lot of attention on this, both from a application intake process where the industry is either not filled out correctly, is not comprehensive enough. And so if there's a way that we can actually really pick out what those different class codes are, what those industries are, we feel like that would be a really interesting way to interpret value. And so if you take a company like this example, it's Privatsea, and you were to go to some company database and look it up, it might provide pretty high-level [indiscernible]-level categories. If you were to go to the website, then, it's kind of another click down, you might see boats on that front page. So it kind of looks little Mediterranean, pretty fancy, [ you have ] passing boats or cruises. But then at the very low level, if you were to read every single word on their website as the robot there is, you actually start to identify words like boating, marine and support. And through not just this website and your knowledge of websites and companies, but across a blend of different companies, we can then start to build models that really help refine these classifications down to the NAICS, a 6 level, for example, around other support activities for water transportation. And so it turns out that this company does kind of support and maintenance for boats. And I think this is an example where you can then enumerate multiple ones so that an underwriter can still leverage their expertise to say, "Hey, this NAICS, it's that line of work, I didn't realize that this company provided the services. So I'll also click on this other industry code or this other class code on top of that." So that's one example of how these data points can be applied to different use cases. But broadly, we want to expand that to kind of all things data, both data that at the surface level is more straightforward but also expanding to things that are more non-obvious. And so this is where some of the other data points come into play like web sophistication or consumer sentiment, that we'll go into a little bit more later, where it's really interesting to think about. And that's where our minds really go to when we talk about, oh, there's so much [ digital lost ] or there's so much data that's out there. Now if you can only go and enumerate through all of the company's Yelp reviews, that would be a great way to be able to get a sense of how people feel about the company to, again, learn a little bit more about the management, et cetera. Now it's not enough to have this data that's collected from the outside-in. We also want to be able to look at data that really tells a story around whether or not these data points have relevance. So that's where the proprietary data comes in, where we'll partner with certified data providers, who are our carriers, to look at this report. And this -- of having claims, I think, makes this whole process that much more credible, especially when you layer on the historical data, which is not only do we have all this data at some point in time, if you were able to pull this data now back historically, you can then start to do back-testing and validation, especially against that proprietary data like claims to say, "Hey, for these [ 2 ] types of data points, if you now have them 10 years ago through today, how would you have priced these policies differently? Or how would you have made different decisions? I think that's what really enables our ability to bring value. As a double click, like I mentioned, some data might be more obvious or top of mind, but a lot of this data is actually really hard to parameterize. And what I mean by that is it's easy for each of us to say, and sometimes not even, just to look at a Yelp rating or a Yelp review and say, "Okay, this person is pretty negative and here to pick out some of those challenges." But then for a machine, especially when you have double negatives or the Yelp ratings data, this food tastes really bad but I think the service is great or the opposite, I think a lot of those different components of a review, for example, can actually throw off more traditional machine learning approaches or automated approaches looking at this. And so that's where we have then taken on some of these different approaches to categorize the data, think about what these high-level keywords are, apply a lot of the different open source kind of approaches to synthesizing and distilling this data to then try to look at how we can featurize some of these different word phrases in overall sentiments and turn that into some quantifiable measure we can use to model. And on the flip side, there are other types of data that we think is really interesting but still requires a lot of [ information ]. And so one example is that when you look at websites, you can look at the [ normal ] technologies that a website uses and also whether or not their experience, in not just the best web standards, but also like any new standards around this ability. And so as someone who's a little color blind myself, it's actually meaningful for a website to take on some of these responsibilities to adjust different colors, for example. And so we're -- the hypothesis around whether or not websites that adhere to best practices both on the technical and [indiscernible] perspective are more reputable or more credible or better risks. And so it's through this process of having one of these hypothesis-driven approaches, distilling the data, having some kind of technology to pull down that information and then turn it into some kind of usable unit of measure. That's really interesting, and that's where I think just this volume of capacity and the ability to [indiscernible], that I think is really one of our strengths. So this last section, so I think this is where the crux of it all is like how do I know that -- you guys talk about this data, how do I know that it really works? And it's through this idea of historical back-testing ultimately to determine the kind of efficacy that we're talking about. So on one hand, you can take an approach where you can do back-testing, whether it's 10, 15, 20 years of data to either build and improve your models or one can look at like risk factors, like ADA compliance, to determine whether or not those types of data actually have relevance, and then if they do have relevance, how much relevance and how much lift. And so in a nutshell, what this is really doing is saying, if you had a time machine to take you back 10 years in the past, now armed with the knowledge that these types of data have lift, you can then append all the service historical data to those historical policies, monitor that performance year-by-year and to look at where along that curve are you doing better and doing worse and then how you can improve, how you can use each of our data points to, even if you have an in-house model today, to augment how you're thinking about evaluating and assessing risk. Again, from an underwriting approach, I think this is pretty tactical in terms of how you might make different decisions. But I think that also extends to all kinds of other different use cases, whether it's prospecting all the way to claims. And so this is a process that we take whenever we're coming up with new data points, for example, and working with the carriers or for new carriers that are interested in our products. We oftentimes take this approach where we'll go and back-test up their data and basically take a test drive or take the carriers on for a test drive to say, if you had this data now 10, 15 years back, how would that have worked? How would you have done things differently? You can pull in your model as well. We can compare what it would look like if you had your insight, if you had your application data to make your decisions versus our totally outsided approach just given, again, name and address. So I'll call out a couple of things here, which I thought were pretty cool. But basically, this is one way to look at back-testing and validation and efficacy. And so I'd just call out a couple of these different risk factors that we have within our products. And what you'll see here, and I'll just choose one of the couple at the bottom. The gray lines you see are each individual year that we were doing in the back-testing, whereas the darker line is the average of all the performance. And so -- and if you look at some of these charts, the bottom one on the front left-hand side is the number of nearby medical services. And on the X axis, if you go further to the right, you can see that as the number of medical facilities goes up around the area, we actually see that the likelihood of an event actually goes down compared to the commercial vehicle size or some of these other prior OSHA violations where, as you look to the right, going from left to right, the larger the commercial fleet size or the number of previous OSHA violations actually increases risk. You can also look at this from kind of a loss ratio perspective. And here, I'll turn your attention to the bottom right-hand corner around web sophistication. We're seeing that if you look at the websites of different businesses, that if you look at the technologies and generally try to get a sense of what the hygiene looks like on these businesses, we saw that interestingly enough, there was negative correlation around the more sophisticated website, the lower the loss. And so this is something that we thought was interesting and you can prove and test out. And so we started looking at other metrics related to web sophistication to try to tease out what it's related to. And ultimately, I think we just came to the hypothesis that it might just have to do with the hygiene and internal practices, whether or not a business runs the entire ship. And so those are some of the interesting things that we've seen coming through storytelling of the data. And ultimately, when you think about now the hundreds of different data points that we have, we want to expand this approach to have as robust as comprehensive of a way to assess these services as possible. And so in addition to sort of just looking at lift and the directionality of whether or not a data point makes sense, we also look at coverage. We want to make sure that the data point is relevant to as many businesses as possible but also correlation so that you're not double counting and having a lot of redundancy around these different types of data points. Not that redundancy is bad either because we also then want to have a way to triangulate data points to make sure that there's multiple data sources but won't tell the same story. And so, again, happy to discuss more offline, if you're interested, but there is a lot of work that goes on behind the scenes to make sure that the data is both valuable and has lift, but also is as robust as possible. So then ultimately, what we can do is actually try to provide value to carriers and uncover a lot of this business value, which is, if you had now this outside-in data to then append to your own internal models, and especially if you have an internal model, you're really only looking for a couple of other data points that add incremental marginal lift. And so from tests that we've seen with carriers, they've seen 2% to 3% improvements on loss ratio, especially when compared to their in-house models. And so that's something that we've seen where there is value that's shared across different customers and it's something that we're really excited about, especially in sharing with you. And that is -- again, in addition to these other opportunities like operational efficiencies, looking at and making a lot of these different components around employee training or model development, pricing compliance, making these processes easier when you have a more systematic approach and a more auditable approach to how you're underwriting. And so with that, I know it was a lot of material, and I think we'll be able to share a recording of this later on. But with that, I wanted to turn it over to some of the questions that you have and kind of a bit of a discussion around some topics that are more interesting to you.

Thanks, Jesse.

So sorry. Yes, so I think there's a couple of questions that have come in. And I can read them out. One of them was, at what point, based on the size of the risk does the automated risk selection and pricing begin to lose credibility? So thank you for the question. So I'd say that when we think about -- and this is kind of like how do we define a really small business. I think we take the high-level approach of -- at the point where the economics really don't make sense for me to spend all that time underwriting that account, I think that's kind of how we define small business. Internally in terms of how we've been building the model, we've been more so looking at tangled location businesses and that I think in terms of employee size or revenue somewhat does vary. But I think that's the kind of the main street, small business, single locations where we've seen our model have more relevance. So there's a question around how many data points are required to produce a quote or policy? And I'll interpret that as -- so I think there's a couple of things here. In terms of how many data points are required to produce a quote or policy, I think that's ultimately up to the carrier in terms of what their business rules are and what their appetite is. You can make as in-depth or as not in-depth as possible, depending on the appetite. But then from our end, out of the tool, we have 101 different data points with all risk factors, along with a risk rating that really synthesizes the learnings, the signal from each of those different risk factors. So hopefully, I answered that question. And if I didn't, feel free to follow-up again. This is a question to go back to the previous slide. So I'm happy to do that. And so there's another question about how our state insurance bureau is accepting the outside-in rating factor as well as determines the rate. I think that's a great question, especially from more regulated minds like a comp for workers' compensation. Something that -- and candidly, that's something that we are exploring with our partner carriers as we go along this process of building out our product. For the time being, how we've approached it is that a lot of these carriers are using these data points as pricing that's coming out of it as just price guidance. A lot it is coming from the scheduled debits and credits to help underwriters log and how they're thinking about among the different categories as part of their [indiscernible] debits and credits, how to come to a better decision around what their assessment is for each of those categories. There's a question about what sort of explanations are provided with the [indiscernible] indicators. So there's a few different -- there's few levels of explanation. We have a level documentation that basically goes into a lot of detail around how the data is collected, what it means, et cetera. But then, generally, there's a -- not to get too in the weeds here, but the [indiscernible] documentation has had an explanation of what the risk factor is, how the -- what the different risk factor buckets and value is signifying. So there's different levels of documentation as well as kind of being able to pull in big data scientists and our product team as needed to help better explain what's happening behind the scenes. And then the question about how accurate our models for risks that have no Web presence. And to answer your question, thank you for breaking that up, so what we've seen is that using our approach, there is certainly in terms of companies with a website as a starting point there are certainly companies without website, and we've seen that oftentimes, whether it's contractors, et cetera, they will have like a Facebook page or like a Google page or a Yelp page, et cetera, or one where it's industry-focused page. And so with that, we recognize that not all companies own a website. And so in those cases, the website risk factors are getting tailored exactly for that company. And so that if you see credit [indiscernible] that company. We also -- but we do have models and fairly sophisticated implication models to try to predict, well, if this company had a website, what roughly would that website's kind of risk look like? Given that, if a company does have a website, they oftentimes will still have a web presence. And I think if you look at new companies who are fairly new, they'll often have some kind of profile on Google, there's a Google Places that talks a little bit about kind of what their open hours are or what people have said about them or their customer review rating, again, on Yelp, et cetera. So we have found that a lot of companies have some kind of web presence. Now even if a company does not have a web presence, a lot of the risk factors that we do have for a product are related to very kind of physical-based things, and so as long as you provide a name and address, for example, we'll actually be able to look at certain risk factors like how far away is the closest fire department or the police station, et cetera. Now something that we have also found, kind of going back to one of those slides earlier around risk, to show that, that actually does correlate with risk, and that's something you don't a web presence for either. And so we try to take a balanced approach and try to have this wide coverage as possible. The question I think related to this page about a line here that says we have petabytes of external data. And the question is, is it already built into Cyence? So I'd say that the -- we have petabytes and petabytes of data because we've been collecting this for years now. And so a lot of that data is in Cyence across our really wide kind of data infrastructure, our databases. And in the process of collecting that petabyte of data on a monthly basis or a periodic basis is just part of our overall data collection processes. And when you think about all the different companies, millions of different companies that are out there and the digital exhaust that they're creating among other types of the cloud basically [indiscernible] a vehicle into the web, it adds up very quickly to the ton data that we're able to sort through. So if I'm not answering the question correctly, please follow up. We got a question around how much transparency there is to the under-logged data? So I think that this depends. On one hand, we don't provide the actual vendors or the data sources that we use. And so there's not so much transparency there. On some cases, that's like a kind of data licensing issue. In other cases, we do feel like in our exploration of finding, like, high-quality data sources, that it is kind of part of our IP to protect those sources. But then beyond that, if it's a question of what was the process to get that data or what are the, I guess, like the nuances and the variances of all that data, that's something that we can definitely share, depending on the data source, data point, the sets of metrics historically, et cetera. It's not something that we've done often for carriers to show them more of what's happening behind the scenes. There's a question around how do you weigh the different data points or risk factors to get to your target price for a given risk? And does that vary by geography, industry, et cetera? So I'll take this in kind of 2 parts. So the first part is, how do we weigh the different data points and risk factors to get to our target price? So 2 things here. One is the output of our product isn't actually a price, it's a risk rating, and it's an indication of price. But in terms of how we weigh these different factors, we actually run through autonomies of, like, machine learning models and then happy to pull in our teams to go into more detail here. So a lot of that is kind of first produced using our machine learning models and then we actually go and take kind of a pricing comb and magnifying glass through that to look at what actually makes sense from an intuitive perspective. And if something doesn't make intuitive sense but has a lot of signal, we'll actually take it back through and try to do more analysis on that and also surface that to partner carriers to try to figure out what happens. And so we're not manually adjusting the ways we're doing data points. That oftentimes comes out of the modeling, where -- yes, we can go into more detail there if you're interested. And then the other question is, does it vary by geography, industry, et cetera? It definitely does. And geography and industry are definitely some risk factors and considerations when it comes to thinking about the model and then model out. So we do also break out company differentiated by geography and industry. So I think we're pretty close to the time, and so there's a couple of questions around like, does it only work with Guidewire systems? The answer is no. It's actually underlying system diagnostic. And then how often we review the data sources around the modeling? We are looking at and we review the kind of the stability of this data on a monthly basis. And then we also do kind of periodic model refreshes just based on the underlying behavior of the data as needed. And so thank you so much for your time. If you have additional questions, I've also included Brett Schneider's e-mail here. He's really the lead of all this client engagement. And he can also put you in touch with our strategic advisory teams as well as product teams if you're interested on learning more. And so with that, thank you so much for spending the time with us today. And Lizette, I'll turn it back over to you.

Thank you, Jesse, so much. And thank you all for joining us.