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Hello and welcome to beyond the Desk, the podcast where I take a deep dive into the careers of some of the most influential and inspiring leaders in the technology transformation and operations space within global insurance and insurtech.

I'm your host, Mark Thomas, and every week I'll be sitting down with industry trailblazers who are driving innovation and modernization with within the insurance sector.

We'll explore their personal journeys, from their early backgrounds and the pivotal moments that shape their careers to the challenges they've had to overcome, the lessons they've learned along the way, and of course, the big wins that have defined their professional journey so far.

But it's not just about their successes.

It's about what you and I can take away from their experiences and the advice they have.

For anyone wanting to follow in similar footsteps.

Whether you're just starting out or looking.

To level up your career in the insurance or insurtech world, this podcast is packed with valuable insights and inspiration.

So grab your headphones, get comfortable and let's jump into beyond the Desk.

Christian, welcome to the podcast.

How you doing?

Good, thank you.

Great to be here.

Thanks for coming.

Well, as always in the podcast, we're going to go right back to the start of your kind of career and go through all that kind of stuff.

But do you want to give everyone just a quick intro on who you are and what you're doing right now and then we'll go back to the start and go from there.

So I'm Chief Digital and AI Officer at Mosaic Insurance, Mosaic's five year old startup.

We started in January 21st.

Specialty lines.

We're a hybrid insurer, have a London market syndicate and service companies globally.

We write seven lines specialty business.

So as part of the founding team was a chief operating officer till last year and then moved to this role to really focus on value creation.

So Nice.

Okay, well we'll definitely get into that in a bit more detail, but let's go right back to the start.

We were just talking off, off camera about that.

Obviously the, and the accent probably gives it away.

It's not, it's not a British accent.

So talk to me a little bit about what the kind of early, early life looked like.

Were you into kind of technologies?

You go straight into operations and insurance.

What did that, what did the days look like?

Well, it's, it's been a pretty interesting and, and sort of jagged career.

I grew up in India, just a middle class household, very supportive parents.

Just focus on, do everything you want to do, explore things, fail, learn from IT and move on.

So I went to a Catholic school and then eventually to engineering and electrical engineering in India.

My undergrad halfway through in my second or third year, got the bug to go go to grad school in the US so that was in the late 80s.

So was sort of intrigued by computer engineering and some of the things.

Primarily initially it was focused a lot more on computer architecture.

It was sort of interesting at that point.

Early exposure to computers in the 80s was somewhat limited.

So I was just sort of thinking, reflecting on where it was.

My first exposure was just those punch cards because India was probably at least five, six years behind at that time.

It was still a closed economy.

And then my undergrad engineering obviously exposed me to a little bit of programming and computer science basics.

My interest in going to the US was really to focus on more computer engineering, the traditional computer engineering architecture stuff.

I was fortunate enough for some reason or the other University of Houston said, well, will fast track you into a PhD program.

So I said, okay, let me take that with a pretty decent amount of scholarship.

So worked out well from that perspective.

So landed in the US I hadn't taken a plane since then.

First trip out of the country.

I was 18 at that time.

18 or 19.

Is that on your own as well?

No parents.

So interesting sidebar on this was I.

I was just wandering the corridors with a bunch of professors and there was a young professor who had.

Who had just started in Houston about two years prior to that.

Very interesting, interesting sort of research area.

And I was just reading his background and his research interests.

It was sort of, I'll tie it back to why it sort of intrigued me.

But he, he and I chatted and he said, well, do you want to work for me?

And I said, absolutely.

And he had a pretty significant grant from NASA and National Institute of Health in the US and his area of focus was on neural nets.

So at that time was artificial neural nets.

So everything from physiological basis for understanding the human brain to really modeling it on computers.

So that was sort of, we call ourselves Algorithms and Visual Perception Lab.

So I worked there for about two years.

Interesting enough as I was doing my master's research, the focus was initially on what I would call artificial neural nets and all tied back to where we are today in the whole world of AI.

It sort of was pretty apparent to me early in that was we were dabbling with understanding at that time sort of the foundations of what AI is today in the 80s.

I mean, it's been a topic that's been enforced from Geoffrey hinton in the 70s and we were using neural nets to do everything from horse race betting to sort of figuring out sort of hedge fund like analysis.

A lot of it was based on not necessarily understanding how the human brain works and how we understand things and how to model it, but mostly just trying to do parameter estimation.

So it was sort of very rudimentary ways of looking at artificial intelligence.

So quickly I sort of had a pretty interesting conversation.

So we actually created a cross disciplinary program within the electrical engineering department, psychology and neurosciences departments of University of Houston.

So I was one of the first couple of students to get our PhD there which focused on really understanding the human basis for cognition and brain plasticity and then taking some of that data and then trying to model it on supercomputers.

So eventually the funding for my research came from NASA when they were trying to explore building what I would call a cognition based robot to go around the Mars slander.

Yeah, so Mars was sort of unknown at that time, obviously what the terrain looked like.

So you had to navigate things which you were unknown.

So how do you really understand that was to understand how a human navigates unknown circumstances.

So really understanding perception, cognition, psychology, some level of intelligence, and then eventually using the human basis for vision to actually model it and build it on computers.

So did a lot of work on that, did a lot of work on actually primates understanding how the brain works eventually from birth to the plasticity.

So it's interesting because the human brain is pretty plastic.

Right.

It's not wired upon birth.

So you have a lot of visual experiences, visions, of course, almost third or two thirds of the brain.

So that's the major brain function that sort of coordinates every other human activity you have.

Understanding the most complex piece sort of allows you to unlock a lot of the other things which are sort of supplementary to vision because it provides the basic input and signals.

Right.

At the end of the day, vision is based on electrical impulses that you see from lights coming in.

It just goes into your brain and then you recreate the image in your brain.

So a good example I used to tell eventually some of my students was you see a car just driving at 60 miles an hour past you and you give it a glance and you know it's a Mercedes or a BMW because you're sampling key images from the car.

Then you coordinate it and apply it to things which you're already stored in the brain.

Yeah.

So you don't actually see the full picture, but what you see in your mind is the picture you've seen Before.

Yeah, yeah.

So I mean, that's very fundamental in vision.

Right.

And the more complex pieces are how does your brain recreate visual illusions?

So it sort of compensates for lack of information around you and then recreates it in your brain.

So anyway, the basis of my research was really understanding these sort of data sets and really applying it to human perception and cognition.

So part of my PhD was inspired eventually.

Well, part of it was driven by my sort of inquisitiveness.

My mother had a brain tumor early when I was growing up.

When I was 3 5, she lost her hearing and eventually sort of recovered.

I mean, lost her hearing, but eventually recovered and became successful in her career.

Went to get her master's.

And so it was very inspirational for me.

But more importantly was sort of a trigger to understand more about how the human brain works.

Yeah, okay.

So at least it was sort of a full circle for me.

So it was a nice pathway into my graduate research post that I was doing research and teaching at the university.

And it was sort of an interesting circumstance.

My mom passed away right around the time I finished my PhD.

So I was like, I'm not sure if I want to continue to do this, so I want to do something totally different.

So I ended up working for an agriculture and tractor company called John Deere.

Yeah, so they were doing shop floor robotics using sort of rudimentary sort of robotics for welding and so on.

So I said, I just want to do something totally different.

Nothing related to what I study, but somewhat periphery related.

So it was out of the blue.

We just sort of saw some job posting online at that time.

On was a very rudimentary news groups at that time in the 80s or 90s.

So I went to work for John Deere.

Sort of enjoyed working with the blue collar workers.

Just leaving the shop floor at 4 o' clock, having a beer, just enjoying my time for about six months.

Ran into some of the senior execs at Deere and they said, what are you doing on the floor?

I'm like, oh, I'm enjoying my time.

I was 24, 25.

And he said, why don't you come in and there's some interesting projects that want you to be involved.

So I ended up doing some work that eventually started looking at weather patterns and data to actually sort of introduce agriculture insurance that Deere had introduced.

Really looking at large data sets and seeing, well, is this going to be profitable?

So the first foray into insurance was there.

Interesting.

So did that about two years, then got it up and running, sort of all through my career has always been sort of a more startup, sort of find your way around kind of career.

Interesting enough, I got a call from one of my co investigators in.

In.

In Texas.

Phenomenal chap.

He was a physician, oncologist and also a computer scientist.

So he.

He was working on the intersection of using large data sets to accelerate drug discovery for cancer and cardiovascular disease.

So I had the sort of the large computing experience and we started a company that was accelerating drug discovery.

So at that time, most of the traditional ways of identifying drug candidates were based on sort of what's called combinatorial chemistry.

So you go in, you identify target candidates in the lab.

It takes seven years to get to a point where you have three or four candidates and you go through clinical trials and get it out to market.

Pretty lengthy process.

And the first phase of getting to a target candidate, we were trying to accelerate it to 18 months.

We were highly successful because what we did is at that time, and this sounds like fairy tale and I think about it, the US had just declassified a lot of what was being used at that time for nuclear simulations post the Russian Cold War.

Right.

So they were trying to commercialize a lot of those technologies.

And so we did a lot of work with the defense labs, our company and a couple of the large defense supercomputing vendors.

I don't know if you remember Cray research at that time.

Yeah.

So the company had twofold.

One was to commercialize a lot of those technologies, eventually sell it to companies like IBM and Sun Microsystems at that time.

And the other was to really get these drug candidates out.

So did that for about four or five years.

We sold the company.

I exited when I was 26.

It was sort of an interesting point.

Again at that time, what do I do, Take the money and retire?

Yeah.

I decided to do something else.

Would it have been enough money to retire at that point?

Yeah.

At 26?

Yeah.

That's amazing.

So I ended up working for Allstate Insurance.

Allstate had an interesting problem statement.

They had this university research program to look at large data.

It was at the University of Illinois, where I'm sure you may recall or maybe way past your time, but they had this place called national center for Supercomputing Applications, which is where the browser was developed, the first Mosaic browser.

So they had large supercomputing facilities and we had invested in the facility.

So we had access to some of their technology as well as some of the.

Some of the data scientists there or grad students.

So I was responsible for this program where we were looking at claims data sets and looking at fraud detection.

Right.

I mean very sort of rudimentary start to what we're doing now for algorithm development and things that we're talking about as sort of matter of fact now.

So that was for a couple of years.

Then I moved to Gartner.

Gartner was an interesting switch.

That was my consulting foray.

Gartner said.

I mean for me the switch was really an interesting one where they were trying to write about commercialization of sort of high performance computing in the commercial world, business usage.

So this was around the time when the entire corporate world was looking at data warehouses and large transactional systems like SAP, like 96, 97.

So I did that and then eventually started building that into a consulting business.

So I spent about 10, 11 years at Gartner Consulting and ran the Central US Consulting in Asia PAC.

A lot of great exposure, focused a lot on sort of the learnings on client facing, work, relationship building, a lot of things that served me well in my career.

Was that focused again still on insurance.

Then or was it just a real.

I was focused a lot on financial services, so banking financial services, but did a lot of work for pharma, oil and gas as well.

But interesting projects that a lot of the economies in Asia were opening up.

So I ran the Asia PAC in addition to my central US responsibilities.

So I was on a plane probably a couple of times a month to go to go to Hong Kong and Singapore and India and Australia was, was interesting.

I was young so it was fine.

Yeah, so it opened up a lot of learnings around deregulation and banking transformation and financial services deregulation.

Things that were interesting in the, in the Asian economies as they were opening up.

So did that for like I said, 10 years and I left Gartner, went to PwC in the insurance strategy and ops practice.

So that was purely insurance focused.

Well, I mean initially, I mean so again PwC had just were rethinking building consulting business in the US after their consulting sale to IBM in 2000, I guess.

So there was a five year non compete.

So we were thinking of do we want to start building things that were sort of complementary to the traditional audit and tax practices.

So sort of an early sort of focus on rebuilding the strategy consulting business at PwC.

And I picked up a lot of insurance clients both in Chicago and across the US so spent again another 10, 12 years.

My, my last engagement at PwC was a client of mine, IronSure Insurance.

Yeah, Iron sure was was a post Hurricane Katrina specialty insurer based in Bermuda.

Okay.

And was growing significantly in the US and in London as well.

So it was about three, four years into the journey.

Private equity was really trying to understand sort of what do we do in the next three or four years.

So there was a.

There was a long focus on expense reduction, target operating model strategy and sort of exposed me to a lot of the execs within the company and the board.

The end of the engagement had an opportunity to take over and be the CEO of the company.

So it was like you told us what we should do, so come in and run this.

So I said, okay, we'll eat our own cooking.

So that was a consulting nightmare or a dream, whatever you want to call it.

So went to Iron Shore.

So Iron show grew pretty significantly over the next seven or eight years.

We had a syndicate and a managing agency here pembroke.

And in 2017 we sold the company to Liberty Mutual.

Yep.

So spent two years post that integrated the Liberty's US business into Iron Shore as part of a transformation team and then carved out our syndicate and then we sold that to Hamilton, which is all public.

Yeah.

And then at the end of 2019, me and a couple of my colleagues and actually my.

My boss and current.

Current CEO of Mosaic, a few of us left and apply for us it was a lot of learnings from our iron.

Short and unfinished business.

Yeah.

So we.

We raised capital during COVID and launched Mosaic.

So got the syndicate approval during.

During the COVID times.

Did everything remotely raise capital.

Launched mosaic in 21 and here we are.

That's some career.

So I want to.

The way.

So I'm breaking that up into kind of four.

Four parts of my head.

There's the kind of the academic bit where.

So I'd just like to get into that.

Like the.

Do you think like obviously the thing with your mum.

And that was obviously a kind of a fairly big turning point, which it would be for the vast majority of people, especially as you were still quite young.

Do you think had that not have happened, you would have stayed in academia or like what.

What's that?

Because I think it was because.

Were you teaching?

Did I.

You said.

You said research and teaching.

Right.

So is that typically.

I mean, I'm.

I've not done a PhD, but the.

Is that typically how it works?

You teach and do your PhD at the same time?

It was post.

Yeah.

Right.

I mean you do some level of teaching, mostly supporting research, supporting undergraduate.

Yeah.

Courses and so on.

And do some of those students help you with your PhD and some of the stuff that goes.

No, no, not those.

But then eventually when you get into sort of a teaching role, then you're obviously doing both.

You're.

Yeah.

You're doing the same thing.

Yeah.

You're.

You're advising students who are going through their Master's or PhD and then of course teaching undergrad classes or graduate classes.

Doing the research.

Yeah, yeah.

Or writing grant applications.

Yeah.

But your question, I mean it was, it was interesting enough for me to, to continue to be in academia.

It's one that I fondly sort of recall and enjoy.

It's one I've considered maybe what's life after Mosaic in the next five to ten years or whatever.

I would probably go back to teaching.

So I think, I think it's sort of gratifying to see a lot of the sort of foundational work that I was sort of had some part to play, sort of come to sort of full scale fruition and then.

Yeah.

Trying to go back the other kind.

Of not the bookend.

But you know, I don't mean like the other end.

I think, I think there's still a fundamental gap in terms of understanding the intersection of, of human intelligence.

Sort of the, the, I would say the more call it the spiritual angle of human intelligence and the artificial intelligence and sort of what does it evolve into is an interesting topic just given the intersection of how at least the way I appreciated the evolution of human intelligence from birth to how your brain develops and how visual experiences and learning experience sort of evolve and impact your actions.

And then as we've sort of progressed into a point where we have algorithms and robots, eventually that sort of are morphing that there's that missing piece where I think it would be sort of more philosophical angle which I think I'll probably enjoy doing in my future.

I mean again, like, I mean, so the interesting thing for that for me is, is so like I did, I did a business degree and that one of the, the things that I think back on now, the, the biggest downfall of that, of that, of that degree was that the vast majority of people that were teaching entrepreneurship and this kind of thing had never been an entrepreneur.

So like, I mean, and I mean I kind of thought that was weird at the time but, but at 18:19, you don't necessarily challenge that quite, quite as much as I possibly would now at kind of nearly 40.

So for you having taught that in the, in the early phases of when that was just becoming a thing to then have him had a kind of 30 year career in and then go back as, as AI and technology is, is really kind of skyrocketing with that lived experience of done.

It would be like incredibly valuable.

It would be so fulfilling to really tie it back to sort of the philosophical roots of sort of how do you, how do you.

Where's the intersection between the human intelligence and sort of the generative artificial intelligence or, or general AI?

And really applying some of that sort of philosophical angle is something that I think is important.

I mean for a couple of reasons.

Right.

I think, I mean you hear a lot about sort of the progress of AI and sort of where human intelligence is going to be sort of overtaken by artificial intelligence in terms of everyday activities and so on.

I think there's a point where I can't remember the exact quote, but the key differentiator.

I mean it was a quote from one of the books I've been reading by Henry Kissinger.

Kissinger.

The.

And, and, and there's a quote there that said it's, it's less about evolution.

You can't sort of, you can't supersede evolution by sort of building something that's more artificial and the human species is sort of much more sort of, what do you call it?

Much more flexible and adaptable.

You're not gonna really.

Yeah.

So, so that was the end of the, that, that kind of first period and then you, you obviously went into the John Deere thing which actually got you.

So that's kind of almost like, feels to me like the, the second phase.

And then you had the, you had the, the kind of the landmark sale of the business.

But what, talk me through what that, because that wasn't John Deere.

That was the role after that, wasn't it?

So talk me through the, the thought process then around kind of.

You've obviously earned a significant amount of money.

You could potentially kind of sail off into the sunset, go live on a beach or whatever.

Like what, what, what, what, what was that decision making process and what, what kind of went on in your mind?

I mean I was quite a unique situation for a 26 year old.

Right?

Yeah, I was 26.

I didn't know what to do with.

I think again, not to get too much into the details, but for me I think it was much more gratifying.

One of the drug candidates we were able to engineer and get through clinical trials and eventually get it out in the market by somebody else was one that was targeting brain tumor.

Right.

And for me it was, that was sort of the ultimate sort of gift to what I could, I could sort of, it was sort of, it gave me full closure around sort of my, my, my childhood and sort of what I was very inquisitive about and contributing something to, to at least address something that perhaps alleviated some other people's suffering.

So to your question, in terms of.

I didn't really think of it as well.

It was a huge monetary exit and I could just sort of whatever, invest and move on life.

So it was just life as usual.

I mean, it's one thing that I think it served me well in terms of not necessarily sort of, sort of resting on your laurels because I just didn't want to feel like I was entitled to a life.

So I, I, I just had to scrape from, start again and get into something new that I didn't understand being uncomfortable situations.

Yeah.

So that's one that's always served me well, both from a career as well as in life.

I mean, as, as I mentioned, I, I think my career sort of spawned 20 different things or the last 30 years.

It's really being in a spot where you don't know what you're doing.

Yeah.

And really trying to find your way around it is the one that is so exciting to me.

Yeah.

I think early in my career, somebody, one of my managers said to me, if you know everything you know in the job, just go find something else to do.

Yeah.

You shouldn't be doing this job.

Yeah.

So for me, that's the most important life lesson.

Yeah.

So.

So that's why I ended up doing something totally different.

There was some intersection to what I studied and what I was capable of.

But going to personal line insurance was not something I was, I was doing.

Right.

So it sort of helped me learn new skills.

And then consulting was the same way.

I mean, I didn't get an mba.

I was the sort of academia kind of guy that had no relationship to strategy consulting.

So here was, I was 30 when I had a P and L and ran the consulting business.

And I had sort of more tenured experience, partners and consultants working for me.

And so getting the people skills.

And then you learn a lot.

Right.

You make mistakes, you learn a lot.

And that's one thing that sort of served me well.

So, so you're not, you're not sort of entrenched in your views and, and you're sort of constantly evolving.

So that's why I, I just didn't want to sort of sit back and take it easy and do something else.

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Now let's get back to today's episode.

I get I guess as well the, the element of like, you mean, look, obviously you're very young so it gives you, I mean what, like what would you have done for 50, 60, 70 years or whatever long.

So, so, but, but also I guess that gives you a little bit more freedom to, to make some kind of.

To do.

Do things that you don't know necessarily about because.

And, and because actually there's, there's a little bit of financial kind of back.

You've, you've got something to, to protect it.

I, I suppose it's interesting you say that.

Right.

I, I think for me it was, it was done.

I didn't, I didn't really count on it.

No.

Okay.

Interesting.

It's just, just almost kind of.

It was all.

Yeah.

Went out to sort of good causes and yeah.

You just start from scratch.

Yeah.

So let's talk a little bit about the consult because obviously that's so obviously went to the Allstate people.

I'm interested on the consultant people because actually that's, I think that's, I mean I've done 40 or 50 of these now and there's, there's definitely a common theme that there's people going from consulting, moving into industry and stuff like that.

You mentioned just then that you, you learned lots of.

It sounds like you did a lot of the learning in those, in those roles, certainly the people side things.

And it sounds like that was the first movement into kind of proper management.

Owning a Piano, that kind of stuff.

So talk to, talk to me a little bit about what that, what that looked like and, and what the key learnings from, from that.

I think any young person that wants to sort of move up in career has to have a stint in professional services.

Yeah, that's sort of, I tell my kids that.

And whether it's, whether it's a law firm or consulting or accounting, whatever it is, yeah, I think it gives you some really good career lessons, life lessons.

It's building relationships, working with sort of a group of people that you may have not worked on an everyday basis.

You're trying to understand and solve a client problem.

It's thinking on your feet, working on deadlines, working under pressure.

And it's all extremely important and it gives you a huge amount of lift in terms of corporate success.

And I even say to people, I mean, if you feel bored in your current corporate life, just go in and do some consulting work.

At least you'll sharpen your skills.

And I think it is important.

I mean, as much as you have this healthy skepticism, a consultant is going to tell you what you already told them in a nice PowerPoint or an Excel spreadsheet.

But I think the ability of synthesizing information that people glean from conversations, sort of presenting thoughts, ideas in a way that people, if you're sitting in a corporate world and you're just sort of, you have your entrenched views and really getting that out of people and driving consensus are such great skills that would serve, I mean, anybody well in life.

Yeah.

So what would you say that the kind of, the, the two or three kind of biggest things that you learned in your consulting career that, that, that held you in really good stead.

Because, because it seems like that was the, that there was, there was a kind of foundation early on of the, the academic stuff and then, then doing some, some early roles.

But it sounds like the, the consulting piece was the real big foundation to moving you on, to take on an executive level role after that.

So what, what, what, what do you think the key things that you learned from that?

I think there's a couple of things, right?

One is listening.

It's absolutely important to understand and hear diverse points of view and try to bring ideas together and drive consensus.

And I said that before, that's one that was a great learning experience.

The second is, is really working around deadlines and working with people you had never worked with.

In another, I mean, you come together in a project, all you're doing is and really motivating people Working towards a common goal, understanding.

And I mean, not all client engagements work out well.

Right.

I mean, you have difficult clients, you have a tough situation where, I mean, I've had clients who have said, I don't ever want to see you.

And you don't take that personally.

Right.

It's the same message delivered by you may, may resonate well with someone else.

Yeah.

So it's those kind of things where you're not taking sort of criticism and feedback personally, but taking it to evolve yourself.

Yeah.

Was, was absolutely important.

And then the last piece is really managing sort of people through a structured process of evaluating them rather than just sort of.

For me, I've said this to anybody who works for me, and I've said this to my managers in my career.

I never, ever want to know how I did at the end of a performance cycle.

If I have to wait till the end of the year to know how I've done, then I've lost the plot.

So it's really providing feedback at the point and really sort of evolving and coaching and mentoring and that's so important both for me personally as well as for people who have worked with me.

Yeah.

So, so what you're saying there is that it kind of almost shouldn't be a surprise that you've done really well or done really badly.

Yeah.

I mean, you should just kind of know.

It should be an iterative process all throughout the, the, the, the, the kind of time.

Yeah.

I mean, even if, if things aren't going your.

Well, I mean, going your way or isn't, you haven't done well this year or whatever the reasons are.

I think it shouldn't be any surprise.

I, I think.

Yeah.

I mean, I, I totally screwed up this year and it wasn't.

There was a bunch of reasons.

You talk about it and you move on.

It's not like you come to the end of your bonus cycle and figure out, oh, there's a gap.

I was expecting whatever, 200 bonus and I got only 50.

So.

Yeah.

Yeah.

So then obviously.

And then there's the kind of, the first movement into the, the, the, the kind of exec role.

Iron Shore.

What, what was, what was.

I mean, how big was the business when you, when you joined?

It was about a billion and a half.

And then.

Yeah.

And people, how many people aren't sure.

At that time had about 600.

So.

Okay, so it was a fairly sizable business.

So you, you were, you were taking your first kind of CEO role into a quite established business.

What was that like?

It was.

Well, I had a leg up in the sense that I had worked with them as a client for almost a year.

So I had relationships that existing, but we were introducing a significant amount of change into the company and, and here I am sort of moving away from being a consultant who was telling the company what could be done and the benefits and so on to saying, okay, this is how we executed.

Yeah.

So it was, it was, it was challenging.

Right.

I think people who thought this was just be a consulting exercise that would be going away eventually started seeing sort of things that, that had to be executed.

But I had very supportive management.

My, my current CEO who was, was the time as well in Bermuda, very supportive.

And I think the one thing he said to me my first week when I was an employee was just telling me what's not working well so we can work on it and fix it.

If things are going well, it's fine.

We don't need to know.

So I think that's important in the sense that you have that level of support and backing.

To say I don't know this thing is not going well and then, and then work through how do we solve for it is an important reassurance that, that I had sort of moving into sort of a corporate world into somewhat of a, of a challenging.

Because you were challenging people's jobs, you were restructuring, you're outsourcing, you're going through massive amount of reduction in headcount and re engineering jobs and so on.

So after a year or two, I mean, I think the benefits were apparent a lot of sort of people that sort of brought into it and to Mitch's credit, I think we proved what we said we would do and so it was a great success.

But it was collaborative.

Right.

I mean, and that's one thing that I'm very, I, I emphasize a lot in terms of not necessarily sort of gloating on and sort of personal success, but sort of team success.

It's, it's just everybody else carries you forward, so it's not.

Well, I did this and I got to be in the spotlight.

Yeah.

What was that?

What did you find?

The, the kind of.

Because obviously at that point you'd been in consulting for, for 20 years or so.

What was the, what was it like?

Kind of flipping.

I know that there was relationships there and stuff which made it, would have made it a softer landing.

But, but, but what did you, what did it.

What were the kind of the, the big differences of going to work in, in a more of a corporate role and, and, and the kind of challenges to overcome.

Well, one of the, one of the attractions maybe if I had picked a different company, I would have probably not had the same level of success.

Iron Shore as a company and as a culture was extremely entrepreneurial, fast moving, a lot of issues, almost like a consulting engagement.

That's sort of how I treated my whatever 12, 13 years we were there.

It almost felt like you're just sort of learning as you're going along so that you keep that perspective in your job.

And it made it much more easier.

It was never like I knew what I was doing.

Right.

It was some fire somewhere or something that was changing over.

Writing a new business line of business or we're going somewhere else.

How do you address it?

So and, and, and, and to, to the credit of the management team there, I think I was parachuted and doing a number of things which I had no clue what was going on.

We were looking at a company in Australia to, to acquire.

So went into the due diligence.

We had some regulatory issues with the US Federal government.

I had to go support our CEO in making representation on, on sort of national security issues.

So it was sort of interesting.

So you're always on, on your toes.

So it was fast moving.

Yeah.

So very much like consulting.

Whereas if you'd have gone in to kind of do a, a truly operational COO role in a kind of steady state business, it might not have been quite the same.

Same concept.

Yeah.

That, that said, I can understand it was the, the fast moving, fast paced element of it meant that it was still, still quite interesting and I guess obviously then it moves to the kind of current state.

So talk through the kind of thought process and that thinking period around kind of how Mosaic became a thing.

I mean for me, I think our integration into Liberty was great.

But I mean it was a larger corporate entity.

Much more sort of structured.

Lots of people less fast moving.

Yeah.

Culturally very different.

Yeah.

So, so did you work in that business for a, for a period?

Three years.

Yeah.

Okay, so it wasn't.

You were there for.

Yeah, yeah, yeah.

So we did a lot of the first, first couple of years around the integration, really taking the acquisition synergies and working as a part of a transformation team.

Yeah, but I think, I think it was getting to a point where to, to your earlier question.

I think it was more like business as usual.

It wasn't my cup of tea.

So the opportunity to really do something else with colleagues and management that I enjoyed working with.

Yeah.

Was, was sort of.

Well, we're all going to start looking to do something else.

Okay, tell me when.

Yeah, yeah, yeah, yeah, yeah.

And I guess, you know, you work well with them, you like them, you've.

You've been successful with them before.

So you've got a kind of a decent recipe.

And, and dare I say it, finding the right people is kind of 70, 80% of the battle.

It sort of manifested not just in me, my personal story in working for Mosaic, I think I would say 50% of our colleagues have worked with us in some other capacity.

I mean.

When we launched Mosaic, we had a couple of people that used to work in our old shop and talked about, well, we're doing something else, and they used to work for me and they said, yeah, tell me when.

So again, it's that level of trust, it's a level of sort of interest and the potential to grow and sort of work together as a team was so important for me and for other people that now work for Mosaic.

So as a culture, that's one we were very proud of in terms of just building something that everybody enjoys.

Make mistakes, learn from it, move on.

Yeah.

What was the idea behind the business and where did that come from?

So Mitch Blazer, who's my boss, who's a co CEO, and Mark Wheeler, we had talked about having an interesting sort of model that addressed a significant amount of frailties in the current sort of structure.

Whether you operate as a pure MGA or you're a balance sheet company, that's sort of the two sort of models.

We wanted to do something that was sort of unique in hybrid, where we were taking a piece of the risk on our balance sheet and then eventually writing on behalf of other people's balance sheet.

So for us, the key differentiator was what we call ourselves, the underwriter's underwriter.

So we wanted to really focus on key specialty lines that really was built on unique and profitable risk that other people, other companies are confident enough to lend us the pen.

But we also wanted to make sure that we're not a pure play mga.

We're just riding on other people's and just collecting fee income.

So we want to eat our own cooking.

So we have a syndicate that takes a portion of our risk.

So the business model is unique and more complex in that sense.

Yeah, we're sort of have a balance sheet play in the London market, but also have probably about 30 different partners on behalf of whom we write.

So ultimately, our value is created by the fee income we generate, as well as obviously the underwriting profit of the.

Syndicate and how many people Are you up to now?

We're about close to 200.

And the split between US and UK London's the biggest.

We have close to 100 people here.

Right.

Or less than 100.

Then the rest are spread in the US and Bermuda and, and five other offices in, in globally, so Germany, Singapore, Dubai and.

And your role when you started there was of.

It was coo, I think evolved slightly.

Yeah.

So a startup.

I had technology and operations focused on all the things.

Startup.

Yeah.

And we had outsourced a large portion of our operations.

Yeah.

So the first couple of years were really sort of focused on building the underwriting talent.

And then as, as is with any startup, you're sort of really focused on the top line and building the underwriting and then the, and then the operational footprint was catching up and building it.

So we got to a two year point and three years actually last year and the focus was really, I mean it made sense for us to separate out sort of keeping the lights on, running the operations which are much more stable and really needed to focus more on an operational sort of discipline.

And then the technology and the data and the AI assets.

As we start looking for how does the next three to five years look?

How do we start building differentiation in addition to underwriting expertise?

What is accretive to value creation for us?

And that's all I do now.

So what does the current role, what does that entail and what are the kind of big things on your agenda right now?

So we have sort of an interesting way of looking at.

So I have technology, data and AI assets as a part of my remit.

Obviously there's a foundational keep the lights on technology, assets and data and reporting.

And we look at differentiation.

We look at it in three pillars.

One is how do we use AI and what I would call more commoditized tools for operational efficiency.

It could manifest itself in ingestion algorithms or helping operational efficiency.

And then there's a way to enhance that to agentic AI.

That's one of the things we're focused on.

The second is what I would call front office productivity.

How do we source and select intelligent risks?

So using external data sets, using generative AI to help the underwriter, say, is this risk better than this other risk?

Sort of a more challenge support model to challenge enhance and then eventually you start.

We're thinking about building other things as a part of just having a human in the loop rather than just a full scale AI.

But, but again it's one that I think there are tools that exist, training Sets that exist.

Our view there is we'd want to build things which have an inherent bias that we believe is sort of unique to us.

And then the third piece, which is sort of totally differentiating in the way we look at it, is what others perhaps wouldn't want to consider because it's not scalable, but it's value creation for us is what I would say call it moonshot problems.

But really starting to look at emerging trends that are global.

A good example would be six months ago there was a bunch of change in Argentina.

Does it really introduce new products?

So starting to look at sort of global macroeconomic trends with a view like a hedge fund does.

So what is a secondary tertiary and the fourth level criteria, if there's government change in Argentina, there's going to be sort of whatever monetary stability, is there going to be inflow of funds as they're going to drive M and A activity in which sectors should we identify new products?

Similarly, on a post facto basis, if you start looking at, let's say, take an example, China, Taiwan conflict, you want to scenario play this out saying if you use some of the AI models to say, I mean, this is so fast moving, it's sort of interesting to even talk about it than what we were debating, let's say six months ago with the conflict, what does it do to US Semiconductor industry.

Okay, so one hypothesis is the semiconductor industry is moving to the US So if they're building new plants there, what does it do to water resources?

Because water resources are.

You require a lot of water for semiconductor manufacturing.

Yeah.

Where are they building it?

Is there any environmental pollution?

So is there any new products we should be starting to look at so you could start seeing.

Yeah, yeah.

Way more interesting stuff.

Yeah.

So is that, is that what your, your lead in the charge on that kind of stuff now?

So your, your, your role encompasses the technology and regards to kind of the, I guess the run aspects of technology, but also the future facing stuff or all the stuff around AI.

And are you, are you predominantly building those teams and running that?

It sounds like the stuff you're doing is, is kind of, you want to build bespoke stuff to Mosaic rather than, rather than kind of outsourcing.

There's, I mean, in, in the three tranches I described, the first two, there's a lot of commodity.

Yeah.

Which I think is easy to build and integrate and sort of tweak.

The tweak part would be our unique value proposition.

Even in the risk selection piece, where you're actually sort of taking external Data using small language models and training those data sets to sort of have an underwriting bias, for example, and then see what it comes back with compared to what an underwriter does.

It's a good challenge model there.

But to your question on.

Sorry, I forget what you're around building.

Stuff in house and kind of like the kind of bespoke element of it.

Our view is you don't need a large amount of large teams to build this.

Number one, at least in the general AI space, there's a number of tools that you can actually, I mean, as long as you can spend a decent amount of money on tokens, you can start playing with it.

And so our philosophy is to build, have small teams that's just focused on the strategy and we can farm out execution.

You can always get a couple of Python developers or training engineers or whatever you want, ML engineers.

So that's how we tend to operate.

Because at the end of the day, my view on this is you got to be in a fast fail model rather than just sort of spending six months to build a strategy and then saying, okay, this is what we're going to do, and then suddenly you find yourself, you're like 20 paces behind.

What's.

I'd be interested.

I listened, actually listened to a podcast last night, which is kind of Diary of a CEO podcast, where they had three people talking about the kind of how AI is taking over the world.

And they're kind of varying different opinions of kind of the real doomsayer to the real optimist and then a guy kind of in the middle.

You mean you, you, you've obviously been in a unique position in the sense that you were kind of looking at this stuff 20, 30 years ago, 40 years ago, and, and now it's obviously the stuff that you were kind of thinking about now is, is really taking, taking effect.

And, and, and so you, you, you've obviously had the kind of fairly unique position of, of probably thinking about this kind of stuff for, for several years.

But what, what's your kind of view on, on, on the, the good, bad, indifferent of, of, of the next kind of four or five years or so.

I mean, I think I was fortunate enough to look at what I would call in somewhat an academic term of physiological basis for intelligence as opposed to the sort of the general purpose general AI.

Right.

But to your question, is it doomsday?

I think the ultimate objective is really furthering human progress.

Right.

I mean, if you think of it very sort of philosophically, I think, I mean, the progress in AI and the uses of AI is certainly going to benefit a lot of, a lot of industries, a lot of things that we consider complex tasks today.

It's going to help and I think what it's going to do in my view to humans is again is to transform ourselves to do things which are much more complex and much more imaginative than perhaps what you're training a model.

It's easier said than done.

And it's sort of cliche to say it at this point because you hear the, well, general AI models are now hitting whatever 160 IQ.

What does that mean?

Right.

I mean at the end of the day I think you're going to find sort of this general purpose AI morphing into specialized sort of again I use the term small language models because sort of tasks that are very unique.

Right.

So again, think of it as if you're an underwriter.

What do you go through to write a piece of risk while you learn about the business?

You build relationships and you can't take that piece out.

So how you evaluate a risk and what do you write?

In most cases they are relatively straightforward.

So what does a human do?

What is the role of the underwriter in the future?

So you want to evolve into things which are much more interesting in terms of, well, I find an interesting piece of risk.

Let me design a product that sort of targets that and then have the data to support me so I can make that decision.

That's sort of the better use of it.

And the other place where I think which I'm most excited given my background is the ability to really enhance quality of life.

The things that I'm more fascinated about is things like the neuralink chip.

I was just reading a couple of last week that they're doing some testing to, to basically restore vision.

Again using some of the things I can sort of.

Yeah, tie the things together.

And that's one of the things that excites me most and saying wow, that's, that's exactly.

It's come a full circle.

So the, the, the, the interested, like the medical thing for me is that there has to be some like massive improvements in the medical space.

That, that's where, where, where definitely I can, you can see just in time, in time, a lot of it, isn't it?

Because that's one of the biggest negatives about the medical.

And then the knock on effect of medical insurance is everything just takes too long.

And to see people and people talking about kind of scans taking weeks to be reviewed, it's like, well, that can be done Instantly, like probably now.

So, yeah, that's an interesting one.

Look, we're coming to the end now.

Before we, I just did find some quick fire questions at you.

I just wanted to like what, what's the, what's next for you?

Obviously you're, you're right.

Kind of in the midst of Mosaic at the moment.

What.

I guess you've, you guys have got a plan of kind of what you want to do with that business.

But what, what do you think the.

We spoke a bit about education and going back into that.

Like what, what, what's next for you?

Well, I think I, I, I, at some point in the future that would be my sort of next foray is, is really the intersection of, of, of human intelligence and artificial intelligence and really sort of challenging some of the philosophical basis of how we could one use generative AI to enhance our lives.

So to answer your question, it's partially academic, but more importantly, I would probably enjoy mentoring people and doing things that are sort of keeping me in, in sort of in front of what, what's happening outside rather than just sort of being in a, being in, in a sort of retired life.

Yeah, yeah, yeah, sounds good.

Right, I've got some quick fire questions here.

First one is which brand or company do you most admire and why?

Well, Tesla.

Yeah.

Early adopter of Tesla.

I just love everything Elon Musk does.

Interesting quote from him.

As humans or engineers, what we do is we try to make processes efficient rather than questioning if it should even be done.

I mean, you just look at sort of the simplicity of the car design, of taking everything and putting it into two motors sort of attached to the wheels is just brilliant.

And then making the car is just a huge software platform.

And even the SpaceX sort of Raptor engines that started with such level of complexity and now you look at the third generation, it's so simplified.

Yeah, I mean, I think he embodies everything that challenges status quo and a lot of business and life lessons and so probably great respect and admiration for him.

Yeah, I read his book recently.

It's a real eye opener.

The next one is what piece of advice do you wish you were given when you were first starting out your career?

Celebrate success.

Yeah, that's a great.

I, I tend to underplay because I feel like.

On to the next one.

Yeah, on to the next one.

And, and I think it's always like, well, it's part of your job.

Just move on.

We've done it, let's get to the next challenge.

Yeah, but I think it's it's.

It's human tendency or I mean I think a lot of humans, maybe I'm.

I feel different but.

But it's a learning.

I think celebrating success and sort of recognizing people for their part is important.

Whatever small contribution that they may have had, it motivates and I should do more of it.

It's one that I perhaps don't do as much.

Yeah, I don't think you're on your own now.

There's just lots of people there, don't they?

The best kind of non fictional business related book you've ever read?

Two I talked to you about Genesis.

The other one, which is my favorite book is Only the Paranoid Survive.

I'm Andy Grove.

You're the CEO of Intel.

There's tons of, I mean great business reading but also really there's inflection points in your life and business and how do you overcome challenge constantly be paranoid about how can you drive success.

It's one that I tend to live my life mostly to my own detriment.

But it's a great read definitely.

I'll have to check it out.

The next one is if you could wave a magic wand and change one thing about insurance, what would it be?

It's what I just said.

More from Elon's Code we tend to make things more efficient or think we are making things efficient, but not necessarily challenging.

Should we even do this?

I wish.

The industry certainly looks at sort of how archaic and bespoke we've operated and continue to sort of throw more money at things that just don't work and really rethinking it.

But I mean, you said magic wand, so here I am.

Yeah.

And then the final one, as always, is if you could what's the best thing about working in insurance?

I think, like I said, I think it's one where there's new things every day.

New things in the sense you learn a lot.

I mean, insurance is interesting in the sense that it cuts across a number of different topics.

Whether you're insuring a financial institution or a merger and acquisition transaction or cyber insurance.

There's a lot of underlying information which is sort of tied to world events, corporate events.

Whether it's on the claim side or on the underwriting side.

It gives you a great deal of exposure to what's happening around you.

Yeah.

So you learn a lot whether you're peripherally involved in underwriting or in operations or technology.

Just the issues that are manifesting driven by external events and how you react to it in whatever sphere you are in within insurance is so fascinating because it's such a great learning experience because you're constantly sort of exposed to new things, which, I mean if you're just, I mean if you're just sort of whatever, a computer engineer writing code, you're probably just interested only in what you're doing, right?

Yeah.

Yeah.

Amazing.

Well, look what great way to finish.

Thanks so much for making some time.

I know you're very busy.

It's been been really good.

I'm sure there'll be some people that want to connect interview like LinkedIn.

Are you happy for people to reach out and connect and stuff?

Look, same with me, plenty more episodes coming.

So if you want to connect with myself or Krishnan, do so and like comment, subscribe, all the usual stuff and we will catch you again next time.

Cheers Krishna.

Thank you very much.

And that's it for today's episode of beyond the Desk.

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