Diya Sawhny, Head Comml Strategy & Head Customer Engagement, Moody’s Insurance Solutions: Beyond the slip: how connected intelligence is changing underwriting (421)

0:08Diya, great to have you joining us as we'll talk about shortly. Really interested to know what's happening at Moody's. It impacts my life a little bit as well in the past. You're a managing director. You're overseeing strategy and customer success for insurance solutions at Moody's. And you've got a whole bunch of initiatives you look after around insurance, including life, casualty, property underwriting, and catastrophe modeling solutions. and gives you something of a distinctive, unique even perspective on insurtech. And you're also looking after both organic and inorganic growth strategies.

0:38And of course, at the same time, you need to stay close to what your clients are doing and insurers generally as they look at new technologies and modernize their core systems. Dear, welcome. Did I miss anything in that? No, thanks, Matthew. Thanks for having me. I often joke that my job keeps me very grounded in the realities of our customers and what they're experiencing. So a little bit of vision and growth, but the realities of the day-to-day with our customers keeps life very interesting. As it should do, customers have to take priority. Since we started lining up this discussion, you've acquired two companies in the meantime, just showing how busy you are in both aspects of your job.

1:16But Moody's themselves, now talk a little bit about that, familiar to probably a lot of people through the financial services side. But when you are offering solutions to insurers, how should people think about what Moody's is doing today and how you're looking after those customers you just referenced? When people think about Moody's, obviously, it's a brand really well known in financial services through our ratings franchise. But on the Moody's analytics side, we also have a pretty remarkable business that's grown both organically and through acquisition. We started out with a lot of products, ratings, and so on that were being consumed by insurers, but then grew into a life business.

1:55We have products that address capital and scenario modeling, as well as on the lifestyle actuarial modeling. And then almost six years ago, we acquired RMS. So that really put us on the map as far as property and casualty. But we didn't stop there. We acquired in the casualty space a company called Predacat. And then to round out our property offerings, going deeper on the underwriting side, a company called Cape Analytics. So these are all kind of now part of the Moody's insurance solutions business, along with a number of other products that we offer to us insurance customers. If you want to go deeper on any of that.

2:37Well, both of those companies, Predacare and Cape, excellent companies, we had them as members for a while and got to know them very well. I mentioned in the beginning, your role is around organic growth and inorganic growth, but actually, there's really three categories, aren't there? So you're looking at who you're acquiring, how you're growing internally, but there's this sort of middle area, which is partnering. So two questions really in here. Have you seen the insurtech market move since you acquired RRS six years ago? And how are you thinking about that kind of middle ground of partnering when it's not right to acquire and you don't want to build yourself?

3:10We have a very kind of robust DNA at Moody's in terms of leading with inorganic growth, whether it's acquisitions or partnerships. We have a corporate development group that we partner with very closely from a business standpoint. And so the financial discipline of doing anything alongside the industrial logic for any of this is pretty important, right? And so those are kind of the bookends, right? Completely organic strategy, completely inorganic, and then partnerships at somewhere in the middle. We really are thinking about where are our customers at and meeting our customers and where do we want to directly have a presence and where does it make more sense for us to partner?

3:51We have put out a number of partnerships at the Moody's level with the likes of Amazon, Microsoft, Anthropic, just Google yesterday. So that's all about meeting our customers where they are. That's about distribution, where our customers are consuming our products and so on. But then we also have our intelligent risk platform, right, where the cat modeling community and the underwriting community. So they're coming to us. I wanted to double click just a little bit on Credit Cat and just building on the success with RMS, right? With Credit Cat, they were independent and continue to serve the market from a casualty cat standpoint, bringing sort of the analytical rigor that the casualty market is seeking, which from an overall tool standpoint, we think there's a ton more to do there, right?

4:43You think about the size of the casualty market in terms of just written premium, even GWP of over 300 billion, larger than property even. So we really thought that this would be a unique sort of foothold for us to keep building. And then you combine that with where Moody's across the board were investing, right, in terms of whether it's credit analytics, 600 million entities that we cover from an overall financial ownership, adverse media, et cetera, et cetera. It seemed to make a lot of sense there, bringing that together, but then making it very specific for the insurance industry, right? Well, how do we make use of all of this data and the assets that Moody's has, but focus it in on where insurers would find it useful?

5:32So that was the story with Casualty. With CAPE, it was, okay, with RMS CAT modeling, really strong reputation in the market. But then CAPE helps us extend our capabilities even deeper on the property underwriting side. The aerial imagery that is then turned into risk insights, right? That's where we really thought that was bringing a unique capability to us. And then combining it together has that force multiplier effect. And on the other side, enhancing our CAP modeling with some of the attributes that CAPE brings, things like roof condition or tree overhang, yard debris, secondary modifiers that can make our models even better.

6:18But we're not stopping there, right? We want to continue sort of growing and investing and really take our cues from our customers is how we look at it. And those secondary modifiers you mentioned can actually be responsible for quite a significant amount of damage, such as masonry chimneys toppling over in earthquakes and landing on roofs. I've just seen that myself when I was out in the Northridge earthquake, but it'd be a very important part and definitely something I could see why Cape can add some value on that. But I want to come back to you. You mentioned 600 million companies that you've got data on and you kind of alluded to this, but it'd be really helpful to understand a little bit more about what that means in practice, because there's a lot of interest amongst insurers, as we know, in augmentation of data and finding third-party data solutions, as well as unlocking data they've got themselves.

6:59But how do you sort of extract value from the data that you've already gathered and bring that across into the insurance side of the business? The data estate, as we call it, sits across Rudy's analytics, right? So includes information on, I mentioned earlier, 600 million entities, all the ownership linkages. So you want to investigate something that looks like a small or medium business, but maybe it's part of a larger ownership chain. You got 2 billion ownership links sitting over there, tens of millions of risk profiles and 35 ,000 rated entities, a billion individuals that we follow and track and lots of deep financial and research content, things like adverse news and so on.

7:42So when you think about the use case for insurance, we thought really kind of connects very well with the world of casualty, whether that's financial lines, things like D &O underwriting, surety, trade credit and so forth. But then also things like onboarding and compliance checks, various ownership analysis that could be pretty useful at the front end of any sort of onboarding process. So casualty, one big area, and then onboarding and compliance. And then the third area we're starting to look at is could there be any application to claims and fraud? When you think about all of these, right, Moody's data estate, some of the investments that we're making at Moody's generally could have a pretty significant impact to how insurers are doing business and making it very specific for their context, for our customers in insurance solutions.

8:40So it's not a generic solution. It's very fit for that particular use case. this area of financial lines is relatively new in terms of the application of modeling certainly relative to what's been around for about 30 or 40 years in in property are there any examples you've got just to help understand what that might mean in practice one example is surety right and we think about credit risk and again we've been doing this for years we have lots of information and expertise on large rated entities, but then also really deep expertise honed over years working with clients across many different financial services sectors and looking at small and medium-sized businesses, even models that we know are really good at assessing credit risk for whole swaths of not just the largest, but small and medium-sized.

9:34So you combine that with the financial data that we were talking about earlier, the ownership data, litigation, regulatory, it paints that full picture. So if you think about that surety line of business, we're starting to see some initial sort of really good uptake there. We had an opportunity with a U.S. specialty insurer that was wanting to write more profitably without pulling back on growth. The book was made up of small and medium-sized companies, and the credit data there tends to be a little bit thinner, and there's not always a public company type of signals that you're looking for. So we worked very closely with them.

10:15We looked at their North American book, and we created sort of a test, if you will, against Moody's lost predictive risk signal. So combining that credit data, the firmographic data and company data into a single claim risk score that was calibrated against the insurer's own historic claims experience. And what we found was that removing the riskiest 10 percent, looking again using the claims experience that this particular company had, it actually reduced their loss ratio by over 50 percent. So that's the kind of use case where we're really excited about bringing these broader capabilities that Moody's has, but then making it very specific to the insurance industry.

11:04Exponential Risk London returns on 23rd and 24th March 2027, bringing together the catastrophe modelling, exposure management and wider insurance communities at The Mermaid in London. Across two days, we'll explore changing and interconnected risks, advances in AI, exposure intelligence, and how to turn better data and modeling into better decisions. We're delighted to have Moody's joining us as platinum sponsors of next year's conference, booked by 6th November to take advantage of early bird savings. Find out more at Insect.co. And so now you've got all these incredible assets, different parts of the business, different parts of the client's business.

11:41How do you connect them all together? And I guess the clue is in the concept you're talking about now, which is connected intelligence. But how does that all work in practice? This is something where we're thinking about and talking about a lot here at Moody's. There's a lot of data out there, but how do you make that more consumable, more relevant, more fit for purpose? That's what we're calling Moody's Connected Intelligence. So at the heart of it, you have a knowledge graph, which is a way to organize all this information. It's not just a data store. It's connecting the entity with all of the attributes around it, right?

12:17That asset could be a company, it could be a property, it could be a commercial property, it could be residential even in the future. Knowledge graphs have been around for a while, but I think it's now these use cases are coming to life because we have the technology and AI enablement there, which is allowing us to do this at scale. And we have tons of data on commercial properties, 90 million commercial properties, things like valuation and tenancy and so on. So think about all of that data, the corporate data, the property data, physical risk, but then connecting it up. It's robust, it's curated, it's governed, right?

13:01So you know you can trust that information when you're getting it from Moody's. I know there's lots of value propositions for lots of AI firms out there, but this is where we think we really win, right? That data to be decision useful needs to be auditable. It needs to be verified. It needs to be up to date. So it needs to be governed so that you can make use of it. So imagine where you wanted to use that data in an insurance context for Lloyd's regulatory purposes or for solvency too, right? And the domain expertise, I think, is the other aspect of it, right? underwriting in an insurance context is related but different than underwriting for a bank and that's where we think we have something unique to bring yeah that point about regulation is so important but also that point about ai as well you said it's not enough just to be able to surface information you've really got to be able to give confidence to the person that's using it and making different decisions there's very good valid data behind that just come back to the knowledge graph so is there any way you can sort of bring it to life but think about a scenario where every quote that begins with a slip, and you might get some information.

14:10So let's imagine it's ABC Tools Company on paper. You get some information. It's two entities. It's one supplier, four plants with exposure to two ports, right? That's all the underwriter knows. Now imagine if you query that against Moody's knowledge graph, right? What subsidiaries and the suppliers throw up in Moody's knowledge graph? And so the graph might come back with information. Again, you've queried those 600 million entities and you go the next level deeper. And so, you know, while the slip only included two ports, right, we might find that this company has exposure to 11 ports. And then instead of the one supplier, it's actually suppliers that the underwriter suddenly finds themselves, you know, looking at on that knowledge graph.

15:00And then where does that risk really concentrate, right? What if out of those plans, five of them shipped through a single port, right? A port with a history of hurricanes. That's where you can really hone in and query this sort of real time rather than historically, you know, that data would have been sitting in five different databases that you have to go search and you have to know what you're looking for. Whereas here you're getting that information served up to you in that initial instance and it's more dynamic. You can then decide, right? Is this the type of risk that you want to take? What's the appropriate level of pricing?

15:37And so on. And I think it could also create opportunity, right? Things like supply chain risk, BI risk, et cetera. Things have been difficult to model with this kind of data. You're starting to surface information in different ways, making it more accessible and hopefully supporting the industry in terms of creating new products there. Another example, maybe it's a commercial property in some mid-market in Florida. You have limited information on some of the tenants in the building. You're being given an opportunity to underwrite one slice of that building, right? One tenant that you know about.

16:14What if, you know, you had information on the five other tenants there, and you had some information on the characteristics of the roof condition and the roof geometry, rather than having to query different databases, you know, know exactly what you're looking for, having that information served up so that you can have more dynamic decision-making information at your fingertips. You've referenced AI agents, of course, everyone is these days. What does that mean for Moody's? So I mentioned earlier the partnerships with the AWSs, Microsoft, Anthropic, etc. So a combination of bringing users to Moody's platforms as well as embedding Moody's directly into theirs.

16:59So it makes each integration faster to deliver and more valuable over time. We recognize that our customers may choose to work on Moody's platforms in some instances, or they may prefer to consume this through model context protocols, right, that make this data more consumable, make our insights more available through our network of partners. It's all sort of the nested concept of broader strategy of Moody's analytics. And then there's the insurance specific version, which is what I've just shared with you. Putting all of this in the context of what's going on at insurance solutions beyond the data and the connected intelligence is Gen.AI native applications.

17:41applications. We've just released a product called Risk Data Refinery, which transforms unstructured risk data, such as insurance slips and SOVs, statements of value, into clean, analytics-ready exposure data models. So it reduces the time and manual effort to onboard critical data. Another example within insurance is Intelligent Risk Platform, the model context protocol endpoints. So this exposes the full portfolio of data and analytics through MCP servers, right? So enabling secure governed access to Moody's intelligence from AI agents and external AI environments. And then building a suite of agents and navigators on top of our existing products as well, whether that's on the live side or on the casualty side.

18:30We have a number of navigators and we'll be coming out with agents, especially on the casualty side, I'll be watching out for more there. We are very much investing, keeping abreast of technological advancements and then partnering with a broader ecosystem to meet our customers where they are. So hopefully that gave you a bit of a whirlwind tour about everything that's going on and our strategy, sort of both organic and inorganic, bring it together. It's fantastic. As you know, it was 20 years at RMS for the acquisition, left there 10 years ago. A lot of what you're talking about is what was kind of in the aspiration, but it was too early then to be able to do it.

19:12So it's just tremendous to see all this has come together. And not surprising with the technology and the AI and Agentec is making that so much easier to do. So, Dia, before you go, do you have a recommendation for a podcast or a book that you'd like to share, work or personal? So I've become a big podcast person. I love a combination of markets, politics, technology. I've lately been sort of a combination of Steve Eisman's Markets podcast. And in fact, I think his next one that he's coming out with in early September is going to be focused on property and casualty. So I'm looking forward to that.

19:47And then left, right and center podcast, I find pretty useful as well in terms of especially different viewpoints of what's happening in the US. Fascinating. Thank you very much for your time. That was tremendous. You've got a lot going on and it's great to be able to spend some time with you catching up. And again, thank you for your support for Instate. Moody has been a really important part of what we're doing and hopefully we'll continue to do so going forward. Great to be here. Thanks again for the opportunity. We're super excited about what we have going on. There's a lot going on here.

20:22well if you've made it this far then i'm pretty sure you found that as interesting as i did the instech podcast comes out every sunday morning where we spotlight the latest news leading voices and freshest updates across insurance that you need to know about if you would like to take part in these conversations head to www.instech.co to find out how you can join our network and be a part of the insurance intelligence for the curious.