Ep. 180: The Future of Cyber Insurance and Parametric Risk

Speaker A: On this episode of the InsurTech Geek podcast, we explore parametric insurance, cyber risk, and the evolving MGA market with Rick Wong, head of insurance at Parametrics Insurance.

Speaker B: The InsurTech Geek Podcast is all about technology that's transforming and disrupting the insurance world. We'll be interviewing guests and doing deep dives into specific tech we see changing the industry. We're taking you on a journey through insurance tech, so enjoy the ride and geek out. M. Uh, Terra, this week's episode sponsor, is revolutionizing the world of workers compensation claims and policy management. Terra's cutting edge platform empowers insurers with comprehensive benchmarking tools, robust management systems, and an integrated ancillary services marketplace. Uh, discover how Terra is driving innovation and efficiency and workers comp at Terra Insurer.

Speaker A: And we are back with another great episode of the InsurTech Geek Podcast. I am here live at REMS 2026 here in beautiful Philadelphia, Pennsylvania, with me, head of insurance at Parametrics, Rick Wong. Rick, how are you?

Speaker C: Good. How are you doing?

Speaker A: Awesome. Great to have you on the show. We're in a little. Great to be here for context for our listeners. We're in a little sound booth, uh, here hanging out and so, uh, on the conference floor. Exactly. So the exciting thing is we've got a bunch of, bunch of stuff going on in the whole conference floor. And, uh, thanks to rems, we've got a little setup here for us to record in. Uh, super excited to be here. Uh, super excited to have you on the show. Of course, we talk about all things insurance, all things insurance tech. But first, I always like talking about the people behind insurance, because, as you know, this is a trust industry.

Speaker C: Yeah.

Speaker A: Like, at the very definition of it, it's a trust industry. And so it's always interesting hearing how different people got to be where they're at. So let's talk about you. Uh, where were you born and raised? Uh, New York.

Speaker C: New York. Grew up in Chinatown in Manhattan. Uh, born there. My parents grew up there. So I actually didn't leave the New York area, uh, until I was, like, 28. I went to NYU. Um, yeah, I was real city kid for a long time, and then when I joined hiscox, they moved me to San Francisco.

Speaker A: Where'd you go to high school?

Speaker C: Oh, so that was interesting. Uh, my parents moved us to Jersey because they were really worried about the high schools in New York and, like, how dangerous they could be. So for high school, we went out to Jersey. Grew up there, uh, did like four years at Old Bridge High School and then right back to NYU.

Speaker A: So you didn't go to like PS 34 or PS 128 or.

Speaker C: I went to PS 124 for elementary school.

Speaker A: Yeah.

Speaker C: In the building that, like, it's basically attached to the apartment building I grew up in.

Speaker A: Wow.

Speaker C: Yeah, so like, I didn't even really leave the courtyard like it was.

Speaker A: That's wild to me, like how, how urban the schools are too.

Speaker C: Yeah.

Speaker A: Yeah. My daughter's in NYU right now.

Speaker C: Oh, awesome.

Speaker A: So I go every month and visit her and hang out in West Village. So what'd, uh, you, what'd you study at nyu?

Speaker C: So I did a double, uh, degree. I got an associate in arts degree because I didn't really know what I wanted to do. And then I transferred over to Stern and then got, ah, double major. Finance and Information Systems. Yeah.

Speaker A: Wonderful. It's similar kind of combination of what I did. I went to Texas A and M. I got a degree in accounting. A master's in mis. Yeah, it was a five year undergrad master's program.

Speaker C: Oh, nice.

Speaker A: Stern's a phenomenal school. My daughter's in Tish, so she's. She, she's, uh, she's um, an acting major there. Wants to be on Broadway.

Speaker C: So Stern's an interesting place because it's not like a normal college experience. Like, I think my first day I went to school, like class in like, uh, a hoodie and sweatpants. And there are kids in like full suits and ties going to internships at like. Yeah, Goldman Sachs right after class. And I was like, oh, this is not what I thought it was going to be.

Speaker A: It's nothing. Nothing like other college campuses?

Speaker C: No, it's like right in the center of the city. I mean, it's a lot of fun. Uh, and you get a different but more expensive college experience.

Speaker A: Yeah. Ah, yeah. Like frat parties are in an apartment building somewhere.

Speaker C: The nicest apartment I've ever lived in was my sophomore year dorm. Yeah, it's like, it was, it's like right on Water Street. Two walls of windows overlooking the Brooklyn Bridge. It was amazing.

Speaker A: Yeah, yeah, yeah. She, her dorm was great. Overlooked Washington Square Park. Yeah, she's no longer there. But, uh, I mean, last day of classes today, so. Nice. Yeah. So what did you dream of doing when you were a kid or in college? Did you have a, did you have a plan or a goal?

Speaker C: No, like, it was insurance. Uh, is something I fell into. My parents actually do title insurance. Um, or they did before they retired. But, uh, so I grew up Working in my Fidelity national title. Like, in the summers, I would intern there. Gopher, uh, typist. All these, like, random small roles. Uh, but I never really wanted to get into insurance. Kind of just fell into it. Uh, I didn't really, like, you know, it's like this general business. Like, I didn't want to be a doctor. I knew it was too much.

Speaker A: Lawyers.

Speaker C: Too much. And those are the classic Asian tropes. Like, but, like, my parents did, like, just, like, operational work, and I was like, oh, yeah. You know, I learned early on that I could make money no matter what. Like, pretty successful as an intern with those companies. So, uh, yeah, I went to school. I thought I was going to go into banking. Graduated in 03. Banking market wasn't great.

Speaker A: No, it was not. Yeah.

Speaker C: Yeah. Wound, uh, up at BNP Paribas. Hated every minute of it. Um, and then I had a friend who had a friend that was hiring at aig into the ops team. So that's how I got into insurance. Yeah.

Speaker A: And it seems like AIG is the

Speaker C: entry point for a lot of people, especially back then. Yeah, this is pre. Pre, uh, financial crisis. I was there from 2003 to 2008. And then 2008 was a real interesting time. Yeah.

Speaker A: You don't say. Did they have any problems around there?

Speaker C: Yeah, yeah. We were in the buildings that they were like, all the. The cameras were stalking, and, like, we were just. They were just like, don't say anything. Just keep walking. Keep walking. Yeah. Wild.

Speaker A: Did you leave during the global financial crisis?

Speaker C: I did. I left it. Well, I left in November of that year. So the team I was on was basically the private, nonprofit, middle market D and O team. And, uh, I was in operations at the time. I wanted to transition into underwriting. But, you know, the financial crisis happened that kind of put a hold on everything.

Speaker A: Ah.

Speaker C: So part of the executive team went to Allied World. Part of the executive team went to Hiscox. The Hiscox team was like, we'll make you an underwriter. And so did that. Yeah.

Speaker A: So what was your. What were your jobs at AIG then, if you did underwriting at Hiscox?

Speaker C: Uh, uh, so I was the Div 39, operations manager. I did all budgeting, financial reporting, reconciliation, uh, stuff like that.

Speaker A: So what was it like going from opposite finance and underwriting?

Speaker C: It's. So it was a good transition, I think mostly because of what I was doing. I went into DNO underwriting, which is like a lot of financial analysis, and it kind of just like, transferred over really well, um, but a lot of like insurance like we were talking about before is like relationship based and you know, trust base and think I do that really well, build trust. So. And get along with a lot of people. So it was easy transition. Yeah. Plus it gave me a lot of like skills that you don't really get just in underwriting. Like I can manipulate, ah, an Excel sheet really well. I can go through financials really well. Like, um, going through all the budgeting. I remember I did the first cut of the budget that year. It's like in March it was like 900 million. And then financial crisis hit. Did another cut of budget in September for like 400 million.

Speaker A: Wow. Yeah. Wow.

Speaker C: Yeah.

Speaker A: So it was uh, that's a dramatic change. Yeah. It's called extreme budgeting. Right.

Speaker C: It was, it was a interesting time. Like global calls every day, you know, just like no one really knew what was going to happen. You would get like these emails that like Zurich hired 100 people out of the New York office in a single day. Like it was even Hiscox when I went, I think they started 13 new product lines that in that August, November window, they like, they were always big in the UK, right. 100 year old company there, but jumped into the US market pretty heavy right around then. And like they went like 13 product lines right away. And like most of them started from AIG people.

Speaker A: Yeah. So you know, from pain comes opportunity, right?

Speaker C: Yeah, yeah. So it was a lot of fun.

Speaker A: So from Hiscox and being an underwriter, what was the path to Parametrics?

Speaker C: Uh, so started underwriting DNO at, um, Hiscox. It was a brand new team. I was the only, I was the first underwriter, only underwriter. And then as that grew, we hired like three or four people in New York and then Hiscox had this like push to go to these like regional offices and they sent everyone from the New York office out. So got the opportunity to move to San Francisco and ran the DNO team there for a while for the west coast. And then, yeah, just continued to grow. Went there, went back to New York, ran the east coast team for a while, then started the broker relations group,

Speaker A: then went back to San Francisco and

Speaker C: then went back to San Francisco and ran all lines for the Pacific Northwest. Then the pandemic hit. They were just wanted to shrink their, their footprint and wanted me to move m to Atlanta and my wife was

Speaker A: like, absolutely not for San Francisco.

Speaker C: No, no. Yeah, yeah. She's like, there's no way. All our friends are here. Like, find a New job. And I was like, okay.

Speaker A: Are y' all in the town? Are y'. All.

Speaker C: Yeah, we're in San Francisco proper in Knob Hill. Um, so it's at the top of the hill's great. Yeah. Till you have to walk home and then it's like, like I. My walk to work is like 15 minutes. My walk home is like 40. Because it's downhill versus uphill. Yeah.

Speaker A: It's a big difference.

Speaker C: Yeah. Yeah. Especially in San Francisco hills.

Speaker A: No trolleys going up Nav Hill. There are.

Speaker C: But like get the exercise and like the trolley's like nine bucks. Like so.

Speaker A: Really?

Speaker C: Yeah.

Speaker A: It's that much?

Speaker C: Yeah. Unless you buy it like a monthly and I don't take it enough to buy a monthly. So. Yeah.

Speaker A: Yeah. You know, I, I grew up in Baton Rouge, Louisiana. My family all lives in New Orleans and they have a, a great trolley.

Speaker C: Yeah.

Speaker A: No hills and New Orleans up. So. So it's kind of a, a crapshoot. It's uh, a. It's. It's not $9 though. It's a lot less.

Speaker C: That's.

Speaker A: Yeah.

Speaker C: The bus is like two something. If it was two something, I might take the trolley. Cuz it's.

Speaker A: That's Cal inflation for you right there.

Speaker C: It runs right past our house. Yeah. So it's not.

Speaker A: That's cool. So tell me like what, what led you to, to join Parametrics and, and what does it do?

Speaker C: Yeah, um, I get that question a lot. Especially like when we're interviewing people because we are startup. Right. We're insurtech startup. Um, I always give the example or like the reasoning as like you're. I worked at 200 year old companies, AIG and Hiscox. Right. When you're at a large company, if you want to do something new, it could take a lot of time. And if you go to a new company, you try to. You generally do something old. Very rarely do you get to go to a new company and do something new and like have that kind of impact on the culture and the, the hiring and like how the product looks and how the product acts. So that's what attracted me to Parametrics. Um, at the point I joined, I think it was like a two and a half, three year old company. Right. It started off with a cloud downtime product and the whole ethos was basically like, we think that there's a better way to insure technology companies. And to do that it was like we needed some technology to apply on our own. So you know, we basically built a tech stack that monitored the cloud. And so we were basically running cloud services and gathering data and built this cloud downtime product. It was basically a way to bring parametric insurance from the property market into the commercial insurance space. And we did it by quantifying downtime.

Speaker A: So if I have all my infrastructure on Azure. You'd monitor the uptime of Azure?

Speaker C: Yeah, Azure aws, Google, pay me out

Speaker A: the second that goes down.

Speaker C: Uh, we have a waiting period, but yeah.

Speaker A: Uh, and it's so it's like a disability policy. Like you have to wait for. You get paid.

Speaker C: Yeah. So like, I mean our waiting period is like two, three hours, um, on the cloud product. But yeah, basically we quantify what you stand to lose up front, uh, on an hourly basis. And then uh, when an outage happens, we're monitoring the system anyway. You let us know when the outage is over. You were down for eight hours. You had a three hour waiting period. We give you the hourly compensation for five hours.

Speaker A: Yeah, no, that was product number one.

Speaker C: That was product number one, yeah.

Speaker A: And did you primarily sell it to technology companies like mine that are hosting in the cloud?

Speaker C: Yeah, so we had a couple of programs that did like small startup tech companies that uh, really, you know, didn't have the balance sheet that large companies have. So like any kind of business interruption really helped them, uh, really hurt them. And this product helped. And then we worked with a lot of brokers to get into like the larger companies and started getting into like retail SaaS companies, um, you know, online gambling, online gaming, stuff like that.

Speaker A: Really? You'd write that?

Speaker C: Yeah, yeah.

Speaker A: Cool.

Speaker C: Like almost everything at the time it was like, you know, the pandemic had just was going on. Right. I joined um, basically over a little over four years ago. And so Pandemic was kind of still in the middle of it. People were moving to the cloud, cloud growth was accelerating, but you know, everything was new. So like technology still fails. And so we were able to like find, provide solutions for that.

Speaker A: Yeah. All right, so what lines do you offer now?

Speaker C: So we basically have like three verticals. Right. We have uh, what we call our critical system outage policy, which is incorporates the cloud downtime product, but it can be any first or third party name system. And then we have uh, our Tech Cyber product which incorporates parametric bi, um, and then we have our Data Center SLA product. So we basically become the financial backstop for data center SLAs. Yeah.

Speaker A: So the Data center then uses you.

Speaker C: Yeah. Uh, so the data center buys the product.

Speaker A: Yes. They're laying their risk off on their SLAs.

Speaker C: Yep, exactly.

Speaker A: They literally just hand you the contracts or the SLAs and then we match it. And then you match and cover it.

Speaker C: Exactly. Yeah.

Speaker A: Yeah. I've used a lot of data centers in the last 25 years.

Speaker C: Yeah.

Speaker A: I had one in Louisiana, then I won Dallas and I had one in Brian. And now we're 100% on Azure.

Speaker C: Yeah.

Speaker A: And Azure goes down. You know, Azure's got a big problem right now. They're out of space in the east data center. Did you know that?

Speaker C: Yeah, yeah. And they're, they're adding like everyone's. The investment is going to add.

Speaker A: Not fast enough.

Speaker C: Yeah, that's the problem.

Speaker A: Like you really can't get capacity in the east region.

Speaker C: That's. But they have East 2.

Speaker A: They have, uh, East 2. They have Central. The Midwest. Yeah. So they have a bunch of. It's just wild to me that like, you don't think about that like that you would literally not be able to provision a resource.

Speaker C: Yeah, yeah. And especially the cloud with how fast it's growing and the expansion of data centers. But like, data centers take a couple years to build. Right. And so like everyone is building a data center now and it's like, are you going to be colocation hyperscaler? Like there's a lot of growth happening in the space, but with growth and fast construction comes issue.

Speaker A: Yeah. Comes risk.

Speaker C: Yeah.

Speaker A: So best loss ratios, can you say?

Speaker C: Uh, I mean our cloud product is probably running, uh, around 20. Like all of our other products are Fairly new, last 18 months. So we don't really have too much of claims history. Uh, like our tech cyber product launch in October, our data center product launch in like, uh, August. If we had like really bad loss ratios or any loss ratios would be really bad for us. Right.

Speaker A: Yeah, it would have been. You would have gotten.

Speaker C: Yeah.

Speaker A: Catastrophically bad losses immediately.

Speaker C: Yeah. Especially like on the data center side. We're putting up anywhere from like 20 to 150 million. Yeah. Per risk. So. Yeah. It could be pretty bad.

Speaker A: Yeah, it could be pretty bad. Okay. Do you have, do you ever go, out of curiosity, do you ever go and inspect the risk yourself?

Speaker C: We, we have done data center tours. Um, you know, like we have a team that focuses on data centers and they've gone and looked at a couple. We're really good at like reviewing what's on paper, um, and taking the submission information just like any other risk. But we do occasionally go and see more because they're just interesting. Right.

Speaker A: I love data centers.

Speaker C: Yeah.

Speaker A: I'VE been in them a long time.

Speaker C: Yeah. And like all these new ones, right. Like meta's building one the size of Manhattan in Louisiana. Like that's got to be interesting to see. Like, how do you get around that? Like, how big will that be?

Speaker A: Exactly. How many robots does it take to run the facility? Yeah, there is some crazy stuff with robotics to run these facilities too, so.

Speaker C: Exactly.

Speaker A: Pretty neat.

Speaker C: Yeah. So we have a team that goes out and sees it's not part of our underwriting, but like it's definitely something we do. Yeah.

Speaker A: So explain parametric insurance to the uninitiated.

Speaker C: Yeah. So parametric insurance started in the property space, Right. Weather related risks. So it's easy to think about in that space because it's like if it rains, you know, if the wind blows X miles an hour, right. You get Y. If, uh, if you get X amount of feet of flood, you get Z. Right. So it's very quantifiable risk. Everything's done ahead of time. So like you quantify the exposure, you, you name the trigger and you like name the payout and the terms happen that, that fast. So we're just trying to take that.

Speaker A: It's essentially betting. Yeah, it's a little bit less because it behaves much more like a betting book. Right?

Speaker C: Yeah, I mean we, we use our information to model it. But like insurance is, is betting, right? Like you're.

Speaker A: Yeah, but parametric is really like betting because in, in betting there doesn't have to be a loss of it, just that event has to be triggered, Right? Correct. And in parametric insurance, like you're not even going to spend time calculating what their damages are. You're just going to pay out what you owe.

Speaker C: Uh, you still have to certify that there is a loss, a financial loss. Right.

Speaker A: But they don't have to quantify it.

Speaker C: They don't have to quantify. And that's the, the beauty of the product. Right. Like in some instances, like if we're saying you're going to get $100,000 an hour, you might have lost 50, but you might have lost 5 million. Right. But like you're still green. Yeah. You're getting that agreed amount.

Speaker A: Uh, those agreed values are upfront.

Speaker C: Yeah. And so you get the certainty and you don't have to go through forensics.

Speaker A: Right.

Speaker C: Like which, you know, on a cyber claim, if you're a SaaS company and you're revenue subscription based, how do you prove an actual loss? It's very difficult.

Speaker A: Yeah.

Speaker C: And so what you're trying to do Is like eliminate all that confusion and, um, you know, difficulty.

Speaker A: Yeah. So what tech have you enabled? What tech have you implemented or built? That's been a game changer for you?

Speaker C: So for us, it's really our ability. How we started was building a tech stack to monitor the cloud. Right. So we have. Our original technology stack was monitoring aws, Google and Microsoft Azure. We basically run every service in every Availability zone in every region for those three cloud providers all day, every day. So.

Speaker A: Oh, you do on your own, in your own account.

Speaker C: Yeah. And so we have accounts with them. If, if we're spinning up a virtual machine. We're spinning up a virtual machine all day, every day on every one of these availability zones and data centers.

Speaker A: Just so you're pre validating if it's actually down or not.

Speaker C: Yeah. So then we'll know, like, if we can't spin up a virtual machine, then we'll try to spin up 10. If we can't spin up 10, then we'll try to spin up 100. And that gives us the ability to see when downtime is happening, uh, before it may be announced. Right. And so our monitoring system is very strong. Like that was our original tech stack.

Speaker A: And then that's amazing. And you know, there's a lot of those type of events that go on.

Speaker C: Yeah. And we have our cloud outage risk report that comes out every year, and we analyze the trends and let everyone know, like, what, what everything's looking like. And it's like we've done it for three years now. Um, but our monitoring capabilities have expanded a lot. We're now monitoring like 7,000 other companies. Um, we either pay for the service and do it like we do with, uh, the cloud, where we're running the service. We might use the service, like, so

Speaker A: we're using pay for it. You pay for it.

Speaker C: Yeah. Uh, but we're already using like Salesforce. Right. So we like, use Salesforce. And so we have insight into like, how that's running for us. Uh, we get public data and we buy data as well. Right. So it all feeds our monetary. And like when we have like, now that we're in the tech cyber space, we see more of what, like the digital supply chain looks like for certain companies. So we'll see like, oh, certain companies are, uh, relying on Salesforce and we'll monitor, we'll. We'll note that in our, our system. And so then maybe like all, uh, right. Maybe it's time we built a monitoring system for like Stripe or Salesforce or Square. And so we can see what that looks like and add to that data so we can really look at the full digital supply chain.

Speaker A: That's pretty amazing.

Speaker C: It's really cool. Like, and that's how we model and build and price our products. Yeah.

Speaker A: So do you also do security scans and security sweeps and configuration scans and like if someone, if you know they have, they've misconfigured their system, are you telling them, hey, you have to remediate this, this is a condition of our policy coverage.

Speaker C: No. So like now that we have like a tech cyber policy, we're starting to get into using vendors for a lot of that. Like the standard scans. Like you know, like Coalition does scan, APA does a scan like the do. We don't want to build that technology in house. We want to focus on our, our monitoring technology.

Speaker A: Okay.

Speaker C: Um, but we'll, we'll add that to it and continue to grow our services in that regard. But we want to monitor more companies, more services. We want to start doing more monitoring in like the AI space. Like you know, there's four main large language models and like how many companies are built off the backs of those companies and so you know, new ones every day. Yeah. And so for us it's not like how good are and accurate those com, uh, those models are, it's how available are they if they fail, how many companies are going to go down from that?

Speaker A: Yeah, true. Yeah. Because each one of them could be 500 companies.

Speaker C: Yeah, exactly.

Speaker A: You can have knock on big, big knock on effects.

Speaker C: Yeah.

Speaker A: One data center can take out 500 providers and then those 500 products would solve, conserve 500 companies each. Now you're edging towards a quarter million effective people for an outage. It's a pretty big deal. So are you, are you doing a full cyber, you know like, are you like straight up head to head against coalition?

Speaker C: Yeah, we're doing a full cyber, you know, policy. Um, we were writing on the Markel360 form, we wrote our own form um, to incorporate our parametric bi. So basically instead of like a standard liability BI policy where like you have to file a claim, you'll go through forensics, you have to justify the loss. We'll do a pre agreed daily BI amount. And so if there's a BI event then it meets our four hour waiting period. They get access to that daily BI amount, no forensics. We pay our claims in 25 days once the outage is over.

Speaker A: Wow.

Speaker C: So you know, it's much quicker, much cleaner process and think like when things happen Like Change Health or CrowdStrike or, uh, the CDK outage. Some of these companies don't have the balance sheet to, you know, weather those, those outages and, and make most payroll.

Speaker A: Yeah.

Speaker C: And so it becomes important.

Speaker A: Yeah. So I'm a tech company on coalition, um, now. Now in a different carrier. We actually just moved, but. But, you know, am I the ideal customer profile? A SaaS tech company and insurance that has, uh, you know, a bunch of cloud providers and.

Speaker C: Yeah, yeah. For us, it's like SaaS companies are uniquely, um, will uniquely benefit from our policy because, you know, we don't.

Speaker A: You never call ourselves SaaS companies anymore. Yeah, we're just all a company, everyone's AI.

Speaker C: You just got to get the right buzzword. But like, I'm assuming you run on a subscription revenue. Right. Like, so proving a business interruption loss for you would be very difficult because, like, you're still making the money, uh, over time, but, like, it's going to cost these something.

Speaker A: Right. Like, oh, it's eight months later on renewal or it's going to cost you.

Speaker C: And so how do you quantify that? With a standard forensic process where in our policy you don't have to. Yeah.

Speaker A: Is that. And that's the fundamental difference with your policy docs, right?

Speaker C: Yeah.

Speaker A: Your policy documents specify a trigger event and a trigger payout.

Speaker C: Yep.

Speaker A: Is that accurate?

Speaker C: Yeah, yeah, exactly. We, we really tout the, like, the ease of using our claims process.

Speaker A: So what they have noticed is that, um, insurance carriers and brokers and others are putting higher and higher and higher limit requirements on their tech vendors, you know, policies. So how are you dealing with that?

Speaker C: How are we dealing with what's the biggest policy? All right, I mean, on, on a tech, you know, policy, we have the pen for up to 15 million. Right. So that's, uh. But on our business interruption policy or our data center policy, we're putting up anywhere from, you know, 20 to 150 million. And we have one data center account where we're trying to build a $500 million tower. Yeah. That's a lot.

Speaker A: Yeah. Yeah, you got to think about how many hundreds of thousands of companies, and slash, millions of people are affected by a single data center outage.

Speaker C: Exactly right. And so what we do is like, we match. Our data center policy will match the terms of the SLA that they provide to their renter, whether it's a hyperscale scaler or colocation. And so, you know, that's what they stand to lose. So we're basically protecting their revenue, making it just Like a better asset to either invest in or just own. Right. Like, because you streamline the, uh, revenue.

Speaker A: Yeah, it's really neat. I mean, as a regular buyer of tech. So you're, you're embedding the errors and emission policy with the cyber policy.

Speaker C: Yeah, yeah. Tech ENO policy and cyber come as one.

Speaker A: It's all, it's all combination packages. Everybody else. Yeah, except you're different. Is there. Are there any other parametric players in the, in the, uh, tech Cyber, you know, package space?

Speaker C: I think there's one out of like France Descartes, but like in the US I think we are the only one right now.

Speaker A: Yes.

Speaker C: Yeah.

Speaker A: Is business good?

Speaker C: Business good? Yeah, we finished Q1 60 of what we did in all 2025. We're projecting to be.

Speaker A: Yeah.

Speaker C: Over 200 growth this year.

Speaker A: So your distribution partners are catching on to this?

Speaker C: Yeah, it took some time. Right. Like, you know, parametric as a concept was very popular in like property and construction. Like, and um, very well known. But like, getting it into like, you know, the commercial side is, is took some time. And not only that, the brokers have to understand, like the clients have to understand too. Right. Like it's.

Speaker A: Yeah. That they have a risk that they can cover it.

Speaker C: Yeah.

Speaker A: What it means.

Speaker C: Exactly. Yeah.

Speaker A: What a combined, you know, and cyber policy does for you.

Speaker C: And like our standalone BI policy sits next to the tech cyber policy. Yeah.

Speaker A: So have you implemented a lot of proprietary tech? Like, do you port scan all of your clients every day? Are you going. Doing automated pen testing and vulnerability assessments? Like, how far are you going with this?

Speaker C: So like, again, like, for, for what we do on like the tech side, it's really investing in our monitoring capabilities and our. And modeling capabilities. So it's really just like, what can we, how can we expand that to improve our data set, to add new products, to add, um, more coverage? Right. Yeah.

Speaker A: What code have you written versus licensed? Like where. Where have you really had to. You don't have to get. You don't have to get super granular with me, but you just speak in generalities.

Speaker C: I think all of our code is written in house.

Speaker A: So you've built everything.

Speaker C: Yeah.

Speaker A: Your policy system, your claim system, your underwriting billing, all built in house. All built in house.

Speaker C: Our CEO is very, uh, very much in the. The frame that like, if it builds it, if we build it in house, it adds to the value of the company. We just built our policy administration system this year. Right. We were using Salesforce as a kind of a policy administration system, and it Wasn't really working for us in that regard. And we shopped around to different vendors and our, our engineers. Like we can do this in eight weeks, uh, from the, for the first cut and then we have like a two year window of continuing adding uh, different features.

Speaker A: Yeah.

Speaker C: And growing the product. But yeah, everything is built in house. Monitoring systems, policy, administration claims Y.

Speaker A: Yeah, it's, I mean I've built a bunch of ground up systems. Yeah. I mean a bunch 25 years of it.

Speaker C: Yeah.

Speaker A: I've, I've, I, I've been, been around. You, you and I are almost the exact same age. You're 43. 44.

Speaker C: 45.

Speaker A: 45. Okay. Oh, I'm 46, but 47. So we're two years apart from graduate college. No one so you know, you and I have both been around the block long enough to have done this a few times.

Speaker C: Yeah.

Speaker A: Got, got some scar tissue and some battle wounds, right?

Speaker C: Oh, big time. Yeah. Yeah.

Speaker A: I, I honestly look the, for, for me the joy is in the building. I, I, I try really hard to not let that be um, preconceived paradigm that I can't escape. Right. Like, oh, I have to build there. I have to build everything. Yeah. Like sometimes it does make sense to partner with but when you know how to engineer a product, you know, and

Speaker C: our engineers are great. Like we did a policy administration system at Hiscox and I, it took forever but we didn't have our own engineers. Right. We outsourced that and it was like a lot of gathering the information and then handing it off and them going to build it. Whereas like here we have a team of engineers, um, project manager, our tech cyber lead, Sandy for, She's like, this

Speaker A: is what I want.

Speaker C: They come back and she's like change this, change this, change this. They do it and you know, six, eight weeks later we had a, we had a policy administration system. I was like, that was amazing.

Speaker A: Yeah, there's always worked that well. Uh, yeah, especially when you're, when you have a, an older established organization running lots of programs, it takes a lot longer. And that's the problem. Right. When you're, when you're de novo, man. It's a, it's a, it's a heyday, right?

Speaker C: Yeah.

Speaker A: It's fun starting from a blank sheet.

Speaker C: Yeah.

Speaker A: All right. So kind of kind of closing out. Um, what are you most excited about? You're doing some cool stuff.

Speaker C: Yeah.

Speaker A: Ah, am I a little jelly sandwich? Yeah, sure. Am I going to get a quote from you? Yes. Okay. Like we're going to talk?

Speaker C: Yeah.

Speaker A: Uh, what are you most excited about? The future. Other than the fact that, like, a lot of tech guys like me really like the idea of a product like this.

Speaker C: I think for me it's really about, like, doing something different. Like, we're not, we're not trying to stay in a lane. Right. Like, we think that if we can build some technology and get the right data, that there are things we can innovate, even on our tech cyber policy. Like what we're doing in the data center space. Like, data centers are growing like crazy. But from a tech cyber standpoint, the exclusions on a standard tech E and O cyber policy don't really fit the needs of, uh, a data center. Right. Like, there's a contractual liability exclusion. You know, how do they, how does that cover SLAs, right. There's the, um, infrastructure exclusion. How does that work with all the power generation that data centers use? Right. There's a property damage exclusion, which, you know, it's absolute. But cooling systems fail, fires happen. That's part of the technology of a data center. Right. So we think that there's ways to continue to innovate even within the products that we have. And that's what's interesting to me. Like, not standing still, continuing to change, continuing trying to bring different data to improve coverage and be more focused. We're not going to be everything to everyone and like, even our products. Like, if you're meta, right, and you sit on 700 million in cash, do you really need like, uh, a. A quicker payout on your business interruption policy? Right. Probably not. But if you're a midsize company, it's great. Right? And, and you need that liquidity. Um, but if you're meta, you probably need our data center policy.

Speaker A: Yeah, yeah, yeah. That's cool. That's why, that's why I'm excited about that too.

Speaker C: Yeah.

Speaker A: Well, this was a really fun conversation. You know, you opened my mind up to, honestly, an area of parametric that I hadn't thought about putting parametric in, which is my own.

Speaker C: Yeah.

Speaker A: Which shows you how locked into our own, Our own thought process, how often we get on our own stuff. We don't think, hey, maybe I should have parametric insurance on this.

Speaker C: Yeah.

Speaker A: For me, uh, an outage is like a farmer not getting rain. That's a big deal, you know?

Speaker C: Yeah. And that's, you know, we've been, we're out there, we're trying to get people to understand what we're doing and to understand the exposure. Right. Um, but yeah, it's been a really good ride and really excited to see where we're going next.

Speaker A: Yeah man, you're worried about the, and you got to be worried about the extreme prevalence of bad actors using AI.

Speaker C: That's for any cyber exposure. It's going to be your problem.

Speaker A: Yeah, this is a really scary deal.

Speaker C: Yeah.

Speaker A: And I'll be honest, I've been writing code since I was 11, 19, uh, 91, 92, 93, 94. I started write code before the Internet and we would hack our friends computers. We would fake a login screen. One of my favorites was faking a login screen and then loading it on all the computer lab computers and then locking all your friends passwords. Yeah, like that was a fun one.

Speaker C: Yeah.

Speaker A: Hacking used to be fun and jovial and what we're dealing with now is multi billion dollar criminal organized rings that are leveraging large language models.

Speaker C: They're backed by nations.

Speaker A: They're backed by nations, they're protected by state actors and they're synthesizing voices, they're mimicking numbers and text numbers and phone tone and this is serious.

Speaker C: It is, yeah.

Speaker A: You know and then I think there's. We have this really weird existential threat of, of quantum computers like if we don't upgrade hardcore our encryption techniques. I mean quantum computers can hash. They can break all of your everything. They can break everything.

Speaker C: Even with the news was it um, Anthropic didn't release their most recent. It's too good.

Speaker A: Yeah, they had, they'd found. Okay, you saw all the stuff. It found 25 year old vulnerabilities that no one's ever exposed. What the hell is this thing? I mean is it the greatest hacker that was ever created accidentally? Yeah, probably.

Speaker C: Yeah. But uh, like EDR companies will get better. Like they have to.

Speaker A: Yeah, they're gonna get better or they're gonna get hacked. I mean this serious stuff.

Speaker C: It is and it's, it's, you know why insurance exists too, right? Like if you need that protection.

Speaker A: I know, I'm just curious how you quantify that in your models.

Speaker C: I mean uh, for us you're underwriting to as much information as you have. Right. And you're taking all that like data that's out there in the marketplace and understanding that nothing's going to be perfect. But it's a portfolio. Right. And some things are going to when and some things are going to lose.

Speaker A: I'm scared. I'm um, I'm nervous sighted all the time about the future.

Speaker C: AI is scary. It is, yeah.

Speaker A: Yeah. This is coming soon.

Speaker C: But they have the, I mean, I guess foresight or, or to. To not release it.

Speaker A: This is why I like Anthropic. Yeah. Did you know that with. With the exception of one person who is a meta, all the other major founders all worked at the same live at Yule together? Really? Yes.

Speaker C: I did not know that.

Speaker A: All of them.

Speaker C: That's wild.

Speaker A: They all worked on the same project at Google X. Google X? Wow.

Speaker C: That's a question.

Speaker A: I want you to like, wrap your brain around that.

Speaker C: That's a lot of brain power that,

Speaker A: that the original large language model was, uh, written at Google. The original paper, um, that was published and the speeches that were given afterwards. They introduced the entire concept of LLMs was at Google.

Speaker C: Wow.

Speaker A: And they, they told the world about it. They didn't keep it to themselves.

Speaker C: Yeah.

Speaker A: Sam Altman, one of the major co founders of uh, Claude, who they ended up being at OpenAI. All the roads come back to Google. One lab, one lab at Google and one paper.

Speaker C: Wow, that's so impressive.

Speaker A: Like, I mean, and it was only 14 years ago.

Speaker C: Yeah. People talk about the PayPal mafia like

Speaker A: this is the other. This is the AI mafia. Yeah. This is different.

Speaker C: Is that group.

Speaker A: Wow. Yeah. This is like. And what if that doesn't happen? Like, what if that paper never gets written? Right. Like, anyway, it's just, it's wild to

Speaker C: me just how quick too. Like OpenAI just kind of hit the scene and then it was like everyone else popped up like within a year.

Speaker A: Because they were all at the same lab. They're all working together. Sam Altman didn't. He wasn't unilaterally building this.

Speaker C: Yeah.

Speaker A: Yeah. You know, and then some of them joined him. I, you know, it'd be real interesting. You know, the, the results. I have not seen them yet because I don't think they've ruled on the lawsuit. Elon against OpenAI. Oh, yeah. But that's going to be a pretty monumental. Has the potential. Yeah. Had to be monumental. Yeah. Now I personally am a clan fanatic. I um, think that the way they approach things is, is amazing.

Speaker C: Yeah. Um, we were a big, uh, OpenAI company and now I think we're using more Gemini than anything. And then. But like our engineers use everything. Like they use all of them.

Speaker A: No, we use all the above.

Speaker C: Yeah.

Speaker A: You know, for data you have to abide by customer restrictions on.

Speaker C: Yeah.

Speaker A: Well, you can do their. With their data. Well, this was a cool conversation. I appreciate you. I appreciate your background, I appreciate your passion for insurance technology.

Speaker C: Thank you.

Speaker A: Um, Parametric sounds like a great company.

Speaker C: Yeah, it's great.

Speaker A: And we're growing and. And m. I'm really excited to see what you guys do. And, uh, when you get a submission across the desk with one of your underwriters. Yeah. It says jbk. Just, uh, just take a. Just take a favorable look at it. Yeah.

Speaker C: Send me a note. I'll make sure they. They do it.

Speaker A: They got it. Yeah. So, uh, for all my listeners out there and listening, this is a live interview. We do these every so often. Um, there's a live interview with Rick Wong from Parametrics Insurance. Thank you for tuning in. Enjoy the ride and geek out.

Speaker C: See you next time.

Speaker B: The InserTech Geek Podcast is all about technology that's transforming and disrupting the insurance world. Hosted by James Benham at jamesbenham.com with co host Rob Galbraith at endofinsurance.com the show takes listeners on a journey through insurance tech. So enjoy the ride and geek out.