AIG CEO Peter Zaffino on using Palantir to rebuild underwriting and cut portfolio analysis from months to days

Always fun first.

Oh, they want me to stay for two minutes or what?

Oh, I'm only going to stay. Look, but just a minute. He's got to be the star.

The other headset.

Put it Put him Put you go in here.

Yeah. And I'm just going to I'm going to take off after a minute. We're going to put here here and why don't you here put that headset on

car. Why don't you introduce our guest

microphone on the left?

Well, I you know let he's he's

one of the smarter people in in business um has developed um unique ways to underwrite that did not involve firing people

and someone and someone I admire.

Thanks, Alex.

With that, I'm going to let you guys go. Make sure to tell them that the anttology powers are

it's everything.

Always selling. Hey,

fantastic.

Thanks for coming on the show. It's great to meet you. Pleasure.

Uh yeah, please uh kick us off with like a bit of a more formal introduction.

Yes. So, I'm Peter Zapino. Um I'm the executive chairman as effective on Monday of of AIG. I used to be the

chairman and CEO. Um and have you know worked with the company for nine years to help transform it. It was in a place where underwriting profitability was challenging, operations were challenging, data was challenging. Um, capital was challenging. Uh, so you know had a great team of people

with me to transform the company.

So give us the give us the shape of the business in terms of the different business lines, the different products, the international footprint, the workforce like what give us the scope and the scale here.

Global company with a little bit of a unique footprint. We're 50% international, 50% North America, but our second largest country after US is Japan.

Oh.

Um, we have a big business in India.

Okay.

Um, and then we have a very big business in in the UK. Uh, we do complicated risks.

So, you could think about what's happening uh in the Middle East now with shipping, marine, energy. We're heavily involved in that.

So, something where there's not an existing futures contract that a company can just go and hedge. It's not, oh, I'm going to buy some oil futures because I I I fly planes around and I know I'm going to need diesel fuel in a couple months and so I'm going to hedge that out. This is for more complex risks.

It's for more complex risks and you know you think about the largest you know sort of customers uh in the world and you know big oil companies um you know Fortune 500 companies but we also have a

personal insurance business which will cover things like accident health

uh that are distribution to consumers. Uh so we have a real balance. Part of that feels like if you're talking about insuring a Fortune 500 company against a geopolitical risk, uh that feels like a meeting that takes place in a boardroom. It feels like there's a lot of folks with a lot of trust built up over years to understand each other's businesses. Uh but then there's probably a lot of other underwriting happening and teams uh putting together comps and spreadsheets and data. And I want to know about the the intersection there. It feels like the business is and I don't know if it ever will be just oneclick checkout for for insurance products for Fortune 500 companies. Uh but what what is the interface between the quantitative the qualitative the relationship and the data and then how is that changing?

So the quantitative you have to start at the portfolio level. Okay.

Um and you want as much data as you possibly can to look at deterministic y modeling probabilistic and then stochastic. I think once you understand like your mean and you understand the standard deviation around that then you have to apply it to you know sort of the widgets which is each policy

throughout you know the globe as well as um ways in which you structure yeah insurance. So for us

you can't look at you can't look at an individual policy uh in in isolation you you're you're managing portfolio risk risk to the entire firm and and that's something that's happening probably 247 I imagine

it's hard and that's what led me to Alex Karp. Um, you know, it's hard to get the aggregation done in anything that looks like real time. It's usually static. It can be 30, 60, 90 days.

And your portfolio could change. I mean, it's not going to change dramatically,

but having the ability to, you know, sort of assess risk and use the quantitative data to make better decisions on a daily basis is the aspiration of the way the company's going.

Yeah.

Take us back to your first meeting with Karp. Curious uh what the experience was like.

It's a unique individual.

Call you.

Yeah. No. Um I was actually introduced by a board member many years ago and it was really in this pursuit of um not necessarily foundry or AIP or ontology that's where it led us but it was more on sort of the quantitative ways in which I was looking at the portfolio and could he help me think through computing

and could he help me think through sort of portfolio optimization and I just got more and more uh intrigued. I mean, you see the brain. I mean, he just thinks about things. Um, yeah,

he doesn't hold back. I mean, so he's so I always knew where he stood with uh with me and with AIG, but just developed a very strong trusting relationship

and there's such a tremendous partner that we're able to iterate with them almost like no other company because we do things in 90-day increments

because going out like a year or two years is is too static. And so, we actually build

our relationship on 90-day goals. Okay. And that's been incredibly effective.

What is uh you know a lot of the AI companies talking about scaling laws, exponential growth in token production or even revenue in many cases, but what's growing exponentially in your business? Are you bringing exponentially more data into the platform every year? Exponentially more compute resources, teams, number of policies like what what is the what is the thing that's experiencing a boom right now? most important part I believe in terms of business is that you have to have a business solution you're trying to solve for. So for us it was

more data.

Yeah.

Um better data.

Yeah.

And then reduce cycle time. So in other words like when we get the data that comes in from our distribution partners, how fast can we get it with higher quality data and more data to the underwriter to make decisions?

Got it.

Um and then how do we actually make

what's an example of distribution partner in this context? So it would be like a insurance broker or insurance agent um or you know someone who has

their client is the product effectively. Exactly. Okay. Yes. Yeah, that makes sense. Um what else? Jordy, do you have something?

Uh where was I going to go? The

Alex ontologies. We'll get there. So So there's been uh we we primarily I mean we at least started covering early stage startups. There's been a debate uh in our kind of little subindustry right now uh around a bunch of new uh insurance focused startups that are growing incredibly quickly.

Uh and there's a debate going on is one uh maybe AI makes it more possible to underwrite risk and if you can do that well grow very quickly. Uh the other side, you know, says uh hey, you know, if you're hypers scaling an insurance company, uh maybe that's not maybe you don't want to work with a company that is, you know, going through that hyper

the iron law of the universe goes up fast.

But yeah, talk talk about um talk about what AI has actually enabled, where you're excited about it, where it's failing broadly, maybe where it's overhyped, and you can I guess tie that into uh everything you built with Palunteer. There's never been a time in my opinion whether it was you know introduction to fintech and sh how to use algorithms how to build data lakes and repositories for data there's never been a time in in my professional career so it's 35 years in big companies yeah

that I've seen the ability to change how an an organization actually runs itself and that can come from big companies like Palunteer or Google or it could come from uh you know companies that are being funded by venture and have a very specific niche that can be you know additive to the organization. And what what I think is happening we talked about the sort of data ingestion portion getting that into a digital workflow using large language models to extract more data from what comes in but also uh helping underwriters make decisions that are you know more comprehensive. You also have the ability in the way in which you service customers to be much better through the use of AI. I think companies generally uh my observations are struggling with the orchestration of how you actually drive agents, people and data into an organization and once that is solved and it's certainly on it on its way capabilities are there um then you start to think about the entire endtoend chain being very different.

Yeah. What I think about Palunteer while they've been such a critical partner is one as we evolved together but in that data ingestion to be able to take structured unstructured text all sorts of data and get into a workflow in a fraction of the time helps us on the things I try to achieve is like we have now data that we probably wouldn't have used before because it wasn't good or we couldn't translate it couldn't get it into the digital workflow. Um, and then we start to build out an ontology. And I, and I really do think it's incredibly important. If there's one thing I look at for our organization, certainly the advancements of LLM's, their ability to do things more autonomously now where we started with the binary gen AI, now we're into a Gentic AI where we can just do things autonomously for so much longer. without the ontology of actually building like what the sort of digital twin of your business looks like where you take it and how you evolve it becomes very challenging. So we've been able to do things with Palunteer. I'll use the ontology example again. We did the full ontology of AIG and then we went to look at an acquisition um called Everest which had about $2 billion of premium.

We got Palunteering to work with our team. We could build an ontology of Everest's portfolio on top of ours in 4 days. Um and quite frankly what we started to learn again about that evolution is that you always relied on data lakes or global data repositories. What we found is that we could get you know sort of foundry and start to build out this ontology with going to the admin platforms. All of a sudden these repositories and the central places of getting data and make sure it's scrubbed wasn't as relevant. So I think we continue to advance that

in in the way in which we are looking at our business. I have a la I have one last question. Um just on the actual change management, the organization like how the office feels. What how did you go about actually working with Palunteer? Do you set up your own internal Palunteer workforce who sits alongside FDES? Do you let Palunteer come in and plug in like one person per team that you have set up? Like was there a best practice? Did you go with the best practice? like what was the actual like experience of deploying the forward deployed engineers they get deployed into the organization that's got to be uh a unique situation

first is making sure Alex and then you know two of the senior executives Ryan and Ted that everybody knows what we're trying to do together so we start there

then we wanted to embed the engineers with our team so if we had a business leader that was trying to drive the underwriting output you'd have you know technology from AIG you would have some of the change management but you'd have the engineers sitting there with our teams throughout the entire process because

the iteration is really important in terms of translating what you're trying to achieve from the business side and the engineers actually helping us think through the application of some of the LLMs or ways in which we could circumvent some of the things that we were doing.

Yeah, that makes a ton of sense. Jordan, anything else?

Uh no. Well, insurance has to be the most important topic.

No, the last if we do have a second I I was uh uh

not sure on timing. what how are you how are you thinking about you know workforce planning uh asked uh Karp about this and he said to ask

token budgets um you know we we've stayed uh you know as as you've had this wave of AI layoffs we've been uh over and over and over reminded people that uh if you have a an individual and you give them more capability you make them more productive you make them more efficient a a thriving business will want to hire more people right because you get more individual and so we've tried to remind people of that over and over and over as you know companies that often times are you know underperforming or bloated for whatever reason but what's your kind of philosophy around uh hiring headcount planning uh riffs all that stuff in this kind of uh new era we've been focusing on I heard Alex at the tail end and I agree with him so we're focusing on growth uh we're focusing on reskilling and actually training um our employees to be in different part of the workflow. Now

you would do this I believe in all of this you have to still have great endto-end process and so things that have been the humans been an LLM trained how to do things like outside of the normal workflow has to you have to get rid of that. I mean so I think that's just normal business. Yeah.

Um but you know our aspiration is not to implement you know AI or anything that we're doing with our partners to eliminate jobs. I mean, it's about growth, reskilling, and finding ways in different markets to have exponential growth and opportunity and having a lot more insight in the business that we run.

That's a great optimistic vision. I love it. Thank you so much for taking time to