InsTech - insurance & innovation
00:11:49 · Diya Sawhny
[00:11:49] S1: 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? That asset could be a company, it could be a property, it could be commercial property, it could be residential even in the future. Knowledge graphs have been around for a while, 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, 90,000,000 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? So 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, right, 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.
00:13:48 · Matthew Grant
[00:13:48] S0: Yeah. That point about regulation is so important, but also that point about AI as well. As 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 really good valid data behind that. Just going back to the knowledge graph. So is there any way you can sort of bring it to life?
00:14:04 · Diya Sawhny
[00:14:04] S1: Think about a scenario where every quote that begins with a slip, and you might get some information. So 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 600,000,000 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 is actually suppliers that the underwriter suddenly finds himself, you know, looking at on that knowledge graph. And 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 wanna take? What's the appropriate level of pricing? And so on. And I think it could also create opportunity. Right? Things like supply chain risk, BI risk, etcetera. 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.
00:16:15 · Diya Sawhny, Matthew Grant
What 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. [00:16:39] S0: You've referenced AI agents, of course, everyone is these days. What does that mean for Moody's?
00:16:44 · Diya Sawhny
[00:16:44] S1: I mentioned earlier the partnerships with the AWSs, Microsoft, Microsoft, Anthropic, etcetera. So a combination of bringing users to Moody's platforms as well as embedding Moody's directly into theirs. 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 to 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 GenAI native applications. We've just released 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 endpoint. So this exposes the full portfolio of data and analytics through MCP servers, right? 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 life side or on the casualty side. We have a number of navigators and we'll be coming out with agents, especially on the casualty side, would 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 to worry about everything that's going on and our strategy sort of both organic and inorganic, bringing it together.
00:19:02 · Matthew Grant, Diya Sawhny
[00:19:02] S0: It's fantastic. As you know, it was twenty years at RMS for the acquisition, left there ten years ago. 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. So it's just tremendous to see all this has come together and, yeah, not surprising with the technology and the AI, energetic is making that so much easier to do. Diya, before you go, do you have a recommendation for a podcast or a book that you like to share, work or personal? [00:19:28] S1: 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. And 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. [00:19:55] S0: Fascinating. Diya, well, listen. Thank you very much for your time. That was tremendous. You got a lot going on, and that's great to be able spend some time with you catching up. And, again, thank you for your support for InsTech. Moody's has been a really important part of what we're doing, and, hopefully, we'll continue to do so going forward. [00:20:09] S1: Great to be here. Thanks again for the opportunity. We're... Was super excited about what we have going on. There's a lot going on here.