InsTech - insurance & innovation
00:07:06 · Diya Sawhny
[00:07:06] S1: The data estate, as we call it, sits across Moody's Analytics. Right? So includes information on, I mentioned earlier, 600,000,000,000 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 2,000,000,000 ownership links sitting over there, tens of millions of risk profiles, 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. So 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 and 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 is 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 Estates, 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. Since it's not a generic solution, it's very fit for that particular use case.
00:08:46 · Matthew Grant
[00:08:46] S0: This area of financial lines is relatively new in terms of the application of modeling, certainly relative to what's been around for about thirty or forty years in in property. Are there any examples you've got just to help understand what that might mean in in practice?
00:08:59 · Diya Sawhny
[00:08:59] S1: 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 size. So you combine that with the financial data that we were talking about earlier, the ownership data, litigation, regulatory, 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 US 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. We looked at their North American book and we created sort of a test, if you will, against Moody's lost predictive risk signals, so combining that credit data, the formographic 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%, looking again using the claims experience that this particular company had, it actually reduced their loss ratio by over 50%. 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.
00:11:04 · S2, Matthew Grant
[00:11:04] S2: Exponential risk in London returns on twenty third and 03/24/2027, bringing together the catastrophe modeling, 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 November 6 to take advantage of early bird savings. Find out more at insect.co. [00:11:34] S0: And so now you've all these these incredible assets, different parts of the business, different parts of the client's business. How do you connect them all together? And I guess the clue is in the the concept you're talking about now, which is connected intelligence. But so how does that all work in practice?
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?