May 28, 2025 • 45 mins
Summary:
In this episode, Lisa Wardlaw and Samantha Chow discuss the critical issues facing the insurance industry, particularly in distribution and data management.
They explore the rising coverage gap, the importance of empowering agents, and the foundational problems that hinder effective distribution. The conversation delves into the outdated architecture of insurance companies, the complexity of data management, and the need for insurers to take responsibility for providing agents with the right tools and data. They emphasize that quick fixes like APIs and portals are not sufficient to solve the underlying issues and highlight the hidden costs of poor data management. In this conversation, Lisa Wardlaw and Samantha Chow delve into the complexities of data mastery within the insurance industry.
They discuss the importance of deep data diagnostics, the role of data scientists, and the necessity of aligning value streams to enhance data utilization. The dialogue emphasizes the need for real data hygiene and foundational changes in technology and processes to drive effective distribution transformation. The speakers also highlight the importance of honesty in assessing current capabilities and the need to attract younger agents to the industry.
Takeaways:
The insurance industry often overlooks life, annuity, and health sectors.
Empowering agents requires more than just technology; it needs a solid foundation.
Many solutions in the industry are just 'lipstick on a pig' without addressing core issues.
The architecture of insurance companies is often outdated and fragmented.
Data complexity hinders agents' ability to serve clients effectively.
Insurers must take responsibility for providing agents with the right tools and data.
APIs and portals alone cannot solve the underlying data issues.
Dynamic data exchange is crucial for effective data sharing.
Standardization of data is challenging due to its dynamic nature.
The hidden costs of poor data management can be significant. Data mastery is crucial for effective decision-making.
Superficial analysis won't yield actionable insights.
Value streams should be aligned for better data utilization.
Data scientists need to focus on operational data use.
Navigating data complexity requires collaboration across teams.
Real data hygiene involves simplifying the vendor landscape.
Foundational changes are necessary for effective technology use.
Listening to agents is key to improving experiences.
Honesty about current capabilities is essential for growth.
Attracting younger agents is vital for the industry's future.
Sound Bites:
"Let's tackle the rising coverage gap."
"What's actually broken in distribution?"
"We just added one more tool to me."
"The foundation is broken."
"How far behind is the architecture really?"
"The blindfold, total blindfold."
"The push has got to come from the insurer."
"APIs are not the solution."
Keywords:
insurance, distribution, agents, data management, technology, architecture, coverage gap, empowerment, challenges, solutions, data mastery, diagnostics, value streams, data scientists, data complexity, data hygiene, distribution transformation, insurance industry, AI, technology
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:01):
Insurance Unplugged in the hot seat where the complex world of
insurance is laid bare. Hosted by Lisa Wardfall, this
podcast promises an unfiltered glimpse into the industry like
never before. Each episode invites you to
listen in on the candid conversations that usually
happen behind closed boardroom doors.
From deep dives with industry leaders and thought leaders to
(00:23):
innovative discussions with minds shaping the future of
insurance, we bring the most genuine talks directly to your
ears. Our guests take the hot seat
alongside me to explore the inner workings, challenges and
triumphs of the insurance world.If you've ever.
Wondered what goes on in the shadows of the insurance
industry? From the boardroom banter to the
(00:44):
behind the scenes strategies, this is your chance for a front
row seat. Prepare for unguarded,
enlightening and engaging discussions that cover every
angle of insurance presented in a way that's both insightful and
accessible. Welcome to the conversation.
Welcome to Insurance Unplugged in the hot Seat with Lisa
Wardbaugh. Welcome to today's episode of
(01:07):
Insurance Unplugged, proudly sponsored by Iris and Suretech,
your gateway to the future of insurance distribution.
At Iris, we harness the power ofgenerative AI to revolutionize
data processing and decision making across the distribution
spectrum. Our platform integrates Gen.
AI to provide not just insights,but actionable intelligence,
(01:28):
configurable work flows, and dynamic form generation, all
underpinned by continuous data quality management.
Discover how Iris is pioneering smarter, more efficient
operations in the insurance industry, paving the way for a
new era of distribution excellence.
Let's dive into how Jen AI is transforming the landscape of
insurance distribution today on Insurance Unplugged.
(01:52):
Welcome to another episode of Insurance Unplugged.
I'm your host Lisa Ward Law and joining me in the hot seat
today. Slight repeat guest, but we're
going to take it in a different way.
I I will tell her like you can'tget away from me and it's one of
my dearest and just you know, most mentored idol leaders in
(02:13):
our industry. Samantha Chow Samantha, welcome
to the hot show. Thank you for agreeing like
sorry, Samantha, welcome to the hot seat.
Thank you for agreeing to do it twice after you already
experienced it once. I appreciate your your
fortitude. If you don't mind, you are the
global insurance leader at Captain and I.
If you don't mind giving everyone an introduction to
(02:34):
yourself in case they missed ourfirst episode.
Because today we're going to go.Really detailed into
distribution, so let's go. Yes.
So thanks again for having me asalways Lisa, you blow my head up
with all sorts of gratitude and excitement.
But so, yeah, so I had life insurance, annuities, benefits,
(02:55):
pensions globally for Captain and I as well as distribution
for all insurance lines of business based on kind of my
background and multi line carriers and multi line
distribution strategy. So again, it is it is one of my
favorite topics. It is a very, very hot topic as
you know and you're dealing withas well.
(03:16):
So I'm happy to be in the hot seat.
Let's go. Let's go, we're tortured out,
let's go. So you know, and I'm so glad you
came back because often in our industry there's so much focus
on PNC as there should be, but we often overlook life annuity,
health pension, etcetera. And so Samantha is one of my go
(03:38):
TOS because she and I come from that world, We've lived that
world. And today we really want to
tackle, you know, the rising coverage gap, this expansive
coverage gap and the role of empowering agents.
So like really, what are the hard truths?
What are the real fixes out there?
We, we just did one on insuranceunplugged couple weeks ago where
(04:02):
we started getting into distribution, empowering the
agents for life, health annuities.
And Sam and I are going to take,take that into like a detailed,
you know, lipstick on a pig. If you don't tackle that
operational backbone, there's only so much gloss you can put
on it. What, what?
Let's lead into that. Samantha, what, what do you
(04:23):
think is actually broken? Everyone is on the hype cycle.
Everyone is on, you know, and I get it, right.
I used to run AP and L quick mixes immediacy.
Let's talk to you about what's actually broken.
And and then we're going to get into really like, what's it
going to take to fix this? Yeah, honestly, I think what's
what's broken is where we've started, right.
(04:44):
Again, you talk about lipstick on the pay.
Let's throw a portal out here. Let's throw this capability out
there and let's toss it over to the agent.
And we're going to try and make it look like they get access to
do more control more, have more power, more transparency.
And all of these things have allthey've done is create
complexity, right? Nothing is right.
(05:05):
There's no, we didn't solve an actual problem.
We didn't give them the tools that they needed to personalize
their sales approaches or trigger them to the need or a
life trigger for one of their clients.
We just literally put the lipstick on the pig, throw in a
piece of technology at them and expected number one, that they
(05:27):
would use it. OK, without any kind of like
examples of how to use it or, oror what it brings to the table.
But then those that do get into it realize this is nothing.
There's nothing here. You just add one more tool to me
to try and figure out how to use.
That's the crazy thing, I think,Samantha, since I since I've
gotten more and more and more like swimming to the front,
(05:49):
right, more and more to the front end.
The let's take the portal example, right?
And on one hand, I get it, like,you know, carriers are like,
hey, serve yourself. Here's a portal.
And then on the distribution side, I see.
And this is horrifying to me, bythe way.
But the real life is most peopleare having bots.
(06:11):
This is prior to AI even. Right.
Rape into portals and get data out of portals.
And I'm like what? Or, or to your point, the agents
are saying the data and information is so bad, it's so,
you know, whatever convoluted this, that or the other that
we're not even going to. Use.
(06:32):
It. Right.
The foundation is broken. The foundation that feeds
everything, every tool, every everything is broken.
So that lipstick, that portal isdoes nothing for them if they
don't fix the foundation and usethe right information, the right
(06:55):
data in the foundation to feed those agents and the and you
know, and the tasks that need todo the transparency, you know,
compensation and all, none of that can be transparent.
It can't work without the foundation and the data behind.
It and I think it's really interesting, Sonia thought.
(07:15):
Because, you know, I spend a lotof time, you know, both too on
foundation. I I actually, I actually did the
series about like why I don't demo tech.
And my point on that is like same reason why I don't test
drive a car. I don't care about the things
that people can knowingly make pretty the interior, the way it
looks and feels. I am an engine girl.
(07:36):
Open up the hood. I mean, Sam, this is like when
we first met, right? Open up the hood.
And I usually say, get all your sales demo people out of the
room. Bring in the technical
architect, bring in the enterprise architect, and the
signal there for anybody that ever tries to sell to me is I am
looking for architectural foundational congruency.
(07:56):
Everything else is like I can get.
And of course, I look at experience layers.
That's all like an after the fact.
I've got to see if the architecture holds together.
So I have an answer to this, butSamantha, I want to know your
perspective on this. You've worked a lot with Carrier
and Agents. How far behind is the
architecture really? Oh, you know, it depends on the,
(08:20):
the, the which is sure you're looking at, but for the most
part, they're very, very far behind.
There are so many different architectural views of, you
know, I, I, I like it, like to call it the yesterday, the
decades ago, the millennia ago, right?
These different architectural designs.
(08:41):
And then you have like tomorrow's view that people are
just trying to start dabbling in.
So not only as an industry or within the insurer do we have
these brick walls that have created, been created in between
like different organizations throughout, you know like
underwriting and claims and marketing there.
Those brick walls exist within the architecture and as a result
(09:03):
of that, you know, getting back down to that foundational point
of view if we don't. Solve.
For those brick walls and bring all these components together,
then again, you're not going to be successful in anything that's
driven by data, right? Never right, I think.
We're bringing up some amazing points and and you're, you're
giving that also from the carrier lens at the the agencies
(09:28):
are even further behind. Well, exactly if.
We go into agency oh let's just not that yeah, that's even
that's millennia's behind right.Let's go back category.
We think like. Let's take it in order, right?
We've got PNC, then we've got life and annuity and then we'll
go back a couple of decades in in time and then pick up the
agencies. That's right.
It's true. It's true.
(09:49):
So like, like, let's take a simple example because I want to
then connect this to AI, right? Because we're, we're living in
these two worlds and you and I are not here to say, oh, wait,
you can't do anything good. You're so far behind.
You and I are always trying to figure out like, OK, what is the
foundation that we can build on?So like a simple example, Agency
systems weren't created the sameway policy administration
(10:12):
systems were created. So even if we think policy
administration systems and the way they connect between claims
and underwriting and pricing andall the things are siloed and
antiquated, they were created tomanage the life cycle of a
policy. Even if Sam, you and I say
they're not modern or they have problems, right?
Agency systems, which are actually a symbiotic
(10:35):
relationship to reinsure systems, meaning we're not the
core carrier processing the business, but we have to move
the policy kind of in a I, I call it like a self administered
way. It's self administered by the
carrier. But we the agency or we, the
reinsurer in this example need to have policy movement.
We need to know if it cancelled,if it reinstated, if it lapsed,
(10:58):
if it, you know all these things.
Well, Samantha, do you think that systems that were developed
for sales in any way, shape or form had the fortitude to handle
policy level movement? I mean, they don't even have a
concept of master data management, much less robust
policy holder movement. So of course, these poor agents
(11:20):
are now trying to put, we're going to get to AI in a minute.
They're not trying to put these things on top of these in, in my
mind, fractured systems like, you know, like how do you think
about that? Like, are they actually poised
to personalize and convert or they're really operating with a
blindfold on right now? No, the blindfold, total
(11:41):
blindfold. And I mean, and let's add into
the complexity of this that theyonly have, they don't have just
one. If you're looking at some, you
know, MGA who sells, you know, 6different insurers, life
insurance policies, then then you and that's what most of them
do these days. The whole captive agency is is
few and far between. And that's a much easier solve.
(12:02):
But when we get into this more brokerage LED and they're
integrating into multiple systems, then they're dealing
with those multiple carriers data problems, right.
So I mean. And say something like that just
to kind of slightly interject. I can recognize this because I
(12:24):
used to deal with that as a reinsurer.
And I had really, you can say antiquated, but I had really big
data, really big data governanceand really big processing to
deal with this. And by the way, Samantha, I had
really big IT teams. I had really big BA teams.
And I'm not saying we did it elegantly.
Like I spent a decade trying to break it down and rebuild it.
(12:47):
Yeah. But I knew how to deal with.
It. Yes, I, I, I don't think that
people whose budgets are typically margin based on
commissions doing front end, I don't think when they're given
this, I'll call it mishmash of data.
Sam, I don't know that they've been through the.
(13:09):
How do I put all of this together now that we are
becoming so data centric? Yeah.
I mean to your point, the the push has got to come from the
insurer, right? They're the ones that have the
the money, if you will. They also have the IT teams,
they are the ones that should beresponsible for ensuring that
(13:32):
the data. And again, this goes also goes
back to the fact that if you, you we're not competing just for
market share out in out there interms of like shrinking this,
you know, ownership rate of lifeand, and and protection
coverage. We're also competing for the
market share within the agents that sell our business, right?
(13:52):
So as an insurer, it's our responsibility to give them the
tools, not only the tools, but the data itself.
So we, I'm thinking with my, my,my insurer, my carrier hat on
have to change the data structure.
We have to give them in whateverway, shape or form we can that
supports their IT infrastructure.
(14:14):
It can't be the other way around.
It won't work that way. So, so how do you see this
change coming out like as we think about it kind of moving
into the second phase of this, right?
I think of it as like two sides,no bridge.
And by the way, if anybody asks us in the comments and says,
well, that's what AP is are for and portals are for, I will on
(14:38):
demand, like throttle down with you on that because no, and API
cannot solve that. A portal does not solve that.
So Samantha and and I think that's part of the like heresy a
little bit of the we're, we're addicted to the magic Diet Pill,
right? Like quick fixes and hits.
And I'm like, OK, so two sides, no bridge.
(15:01):
Let's acknowledge. I think Sam, if you agree with
me that AP is are not the solution.
No. OK.
Leading the wings not agreed. OK.
So then how do we shift, how do carriers shift enablement and
how do we think about how do we reimagine data sharing and
(15:24):
mutual enablement of the people that we need to sell this?
Again, I think it goes back to we, we, the, the carriers, they
need to rethink #1 their data structure, right in the
architecture, because data is all over the place, right?
What is the right data that thatis needed?
How do we want to share that? How do we want to get that to
(15:46):
those distribution channels withwhatever their distribution,
whether we're talking about captive agents or independent
agents and, and Mgas or, or whatever it is, they need to
rethink what that data and architecture looks like in order
to ensure that we're giving themthe right information to feed.
(16:08):
You know, again, going back to it's not the portal that is, it
is the the end all be all right here.
You have to be able to feed the information to them through that
portal. It's just a conduit.
If the data and if the data thatwe are looking at and and how
the amount of control and power that we want with that flow of
(16:30):
data, the flow of application, the flow of underwriting, the
flow of claims, all of this, if it's not fixed from a carrier's
perspective, then no, no amount of portal fanciness is going to
help them. Yeah.
And I, I think it's fascinating,right?
Because there's been a lot of work on data standardization,
(16:52):
which in my mind hasn't taken, it never has taken, no, how do
you think about that? Because I, I used to say even
back again, back in the reinsuredate, which is like the mirror
image of the agents. I used to say, I'm going to
solve this problem with no codependency on a standard data
model because the standard data model is never going to exist.
And even if it does exist, the dynamic nature of the data we
(17:13):
need is going to outpace that. So I used to think of this as
how do I solve a labyrinth problem?
You know, like I can't be dependent on because it's easy
to kind of, you know, if you're listening to this and you're an
agent, it's easier to sign and say, yeah, you need to come up
with a common data definition. Yeah.
OK, Good luck. Like we'll see you in.
How many centurions from now, Samantha?
Exactly. So what do you think about data
(17:34):
enablement? I started thinking dynamic data
exchange with like security architecture.
It's kind of boring. But like, you know, like I
started thinking about that, I mean, because I narrowed it out
on it. How do you think about that?
And are are you seeing anybody breakdown this in terms of, OK,
we all know we need to exchange data, but what gets hard is the
how? The how is always going to be
(17:56):
the the, the crux of of all of this.
You're not just feeding. Again, if you're talking about a
captive agency, you know, that'sa little easier to deal with,
right, Because it's a one to oneexchange.
But we start to think about these distribution strategies
where you're using multiple different, you know, Mgas and
independent agencies and, you know, the data you decide that
(18:19):
you want to feed one is may not be the same for another or who
may not be the same for another.It all comes down to, and you
know, again, you're, you're architectural, right?
You, you get a little more, moretechie than than than I do.
I keep it a more a little bit more business.
Than I go down into the tech to try to solve it.
Then I come back up to the business problem because I'm
(18:40):
like, wait, I think about it like this Samantha, like OK,
I'll use a simple example. Data is our asset and the the
fundamental issue that we have again, this is business terms.
The fundamental issue that we have in exchanging and sharing
data is I can't just give and I'll use it as a car.
I can't just give my car to thembecause they could use it for
(19:04):
any purpose and they could take anybody else out on a date.
How do I know that, like in thisplace, placing business means
dating? How do I know that they're only
going to use my car to date me? Yep.
Like and and like, let's keep itat that level, right?
Like to me, that's a very business abstracted level.
Because what's, what's interesting about that is of
(19:24):
course we need independent agents.
To your point, we need Mgas thatsell multiple carriers.
We, society doesn't always buy from exclusive agents and we
have a huge independent agent distribution economy.
So the, the, the like, when I started analyzing this, the
(19:44):
reason why I went down into the technical side to like nerd out
was when I started at the business side, I kept saying,
everyone kept saying, well, we need to exchange data.
And I was like, yeah, but like, just think about it logically,
right, Sam? Let's just pretend I'm going to
loan you. I'm going to make it up.
I have a Ferrari. I'm going to give you my Ferrari
(20:04):
this week to use. Well, Sam, how do I know if I
give you my Ferrari that you're only going to use my Ferrari to
entertain like my business people?
Like whatever. Like, you know, do you see what
I'm saying? Like that's the problem with
exchanging data. Yeah.
But so we let's go back to your statement around, you know
standardizing, right. So from my business perspective,
(20:27):
when I think about standardize, you're never going to
standardize the data, right. Like you said, like it's going
to continuously so dynamic, it changes what what data is
important today is, is new data tomorrow, right where it comes
from, all of all of that. But what doesn't necessarily
change is the standardization and how you deal with certain
types of distribution channels. As a carrier, I define my
(20:50):
distribution channel. And in this distribution
channel, you know what, I'm going to give them the Ferrari,
but they're going to have a a cap on the speed at which they
can go. So I'm going to implement that
little trigger that says you hit50 miles an hour.
Thank you. You're done.
But the other one over there a. Regulator on my Ferrari.
(21:10):
I've got a regulator doing the Ferrari, which is a real bummer,
but but we're going to go back to that hierarchical structure
can be standardized and for thisparticular distribution channel,
here's what you're going to get and that's standard.
You get no more no less than theexact person that might be
(21:31):
appear to you. Now my captive agency again,
they get everything they get theentire furry.
They get to go have at it because they're they're captive
they're just selling for me. They're not going to do anything
else with my data. So that and my interview solves
not only the business problem, but it solves the the technical
(21:55):
problem in the sense of now I have something to work towards
in my technical architecture, right.
And what I feed and how I feed, yeah.
Yeah, I do. I I definitely think that that
works on the what is standard. I I still think that there is
(22:16):
lens of how do I know you're only using that you're not
siphoning the gas out of my Ferrari and putting it in
another car, right, because thatright and say, like I started
then going down into like whatever true data, dynamic data
exchange and how I do this and how I know that my data is only
being used. There are concepts that are
(22:36):
emerging, which is interesting, right?
We're also fixated on AI fixated, but what we're fixated
on is the the the gloss, the high gloss parts of AI.
What we're not fixated on yet are the actual architectural,
truly unlocking components of AI.
But I see people just like, well, it's too technical or
(22:58):
like, I'm not going there or like I've got to make copilot
work first. I'm like, whoa, the thing that
you're actually trying to get tofollow the trail like big
shining lights here. And so, Sam, I find it really
interesting because I'm somebodythat always thinks that the
problems that we're solving are relatively the same.
Yeah. I mean the problems that you and
(23:18):
I are talking about, would you say that these have changed that
much over the last? 10 or 20 years.
No, they are the exact same problems.
We just, I mean, I think we put more complexity around it
without solving the actual problem.
So you're, you're you're, you'respending money, you're spending
time, you're creating and layering in more complexity
without solving the actual physical problem.
(23:42):
So let's get into our third theme, which to me is like where
I love to sit, right? The hidden cost of getting it
wrong, which is kind of what we're alluding to, right?
If you it's what, what the definition of insanity, doing
the same thing over and over andexpecting different outcomes.
We can all take that one to the bank.
But like, you know, you and I have both seen it, right?
(24:02):
Depending on the size of the agency, or you know, you may be
somewhere in the neighborhood ofa couple million, you may be all
the way up to $20 million. Tech programs, wasted agents,
unsupported boards, feeling likedashboards are indicating but
they're not. What's the cost of actually
continuing to pretend you can build on top of BI tools from
(24:24):
the back end without fixing the foundation, without enabling the
data, without doing the architectural business
enablement things you and I werejust discussing?
Because I see a ton of people coming in on a, we can make the
dashboards look pretty and I'm like, OK, but the data
underlying it is what we still have to unlock.
So, So over to you on what you're seeing there.
(24:45):
Yeah. No, I again, it really starts at
that analysis diagnostic gettingdeep into the depths of the
data. We, we are so far behind this
whole idea of data mastery, right?
We keep throwing even even from an, you know, insurer carrier
level, like throwing the throwing data scientists at it.
(25:08):
You know, they're, they're not getting into the depths of the,
the actual data, the the data itself, right?
You can't just create a, you know, total dashboard type
situation where where you're trying to hone in and and I'm
going to say pause real quick. Yeah.
(25:32):
Cut and recut. No worries, you can just say
resume when you're ready. I I even locked the door.
And you know what? Off record.
Mine comes in after a shower. Like mom, I'm looking for
clothes. I'm like, I'm recording the
podcast, dude. What like.
Sorry, that was my whole thing too, I wasn't even a kid.
Well, mine does that too, anyway.
(25:53):
OK. So to our producer, thank you
for understanding that we're working women and this happens.
Yes, indeed. OK, dashboards, is that where
we? Were rewind we were talking
about dashboards and the data scientists thinking that they
can normalize the data basicallythrough data science on top of
(26:14):
ABI tool over to you to resume. Okay, to resume, yes.
So yeah, no, the whole we can't hone in on just throwing more
data scientists down down to it.We have to get back into that
diagnostic level so that we can really, truly understand the
data. Put it in a format that is
actually usable because it's so sporadic so far across.
(26:38):
Bring the right variables in together.
And then from there you have an opportunity to to kind of get
out and start to use those dashboards, right?
You can't just throw like it's ABI tool and we're going to
throw it and we're going to pushall that data up into some
beautiful dashboard can look beautiful, but that doesn't mean
it's actionable or usable. Yeah.
And and I've seen so much work on the, I won't even go there,
(27:03):
but I see so much work where these where these data
governance people are coming from the BI background.
And I'm like, no, no, no, no, no, you can't be making
decisions on data governance anddata enablement because you're
used to everything being this back end thing.
(27:23):
And that's not the way kind of event native processing, which
is where we're going to go with a genetic AI in a moment.
That's not the way it's going tothink about data.
So like, I really feel like, andagain, Samantha, honestly, 15
years ago, we were just scrapingby, like, right.
We were trying to get out of static PowerPoint monthly
(27:46):
summaries. I get it, I get it.
I had a whole VI team working for me, Samantha.
Like, I get it. I lived through it.
I was the one saying build the snow.
But that's not today, right? Like it's like, that's the part
I think of like leapfrogging ahead.
It's like, OK, what am I doing today?
Where are my efforts going? And how can I create true value?
(28:09):
And yeah, sometimes you can takethe data as it is and unlock a
diagnostic or inside or whatever.
But to your point, if the data didn't ever get there, if the
data that was placed there wasn't fully what you needed,
then no amount of reporting in the world can get you those
insights that you're seeking. And I mean, do you see people
(28:32):
starting there like in a lot of the work you do or where, where
do people start? Typically, honestly, they're not
starting deep enough. They're still, they're still
honed in on that superficial I think, but we're seeing more and
more kind of internal realignment, which I think is
great, right? This whole idea behind value
streams and creating value stream alignment and then even
(28:55):
going deeper and, and saying, yeah, we've got some vertical
value streams, but what are the value streams that are
horizontal? And data happens to be one of
those, right? So it touches everything from,
you know, from your, from product to, you know, purchase
experience and customer experience all all the way
through just like your architectural, you know, your
(29:17):
platforms, your policy admins, things of that nature.
So now we've got this whole horizontal and vertical approach
is is where we're seeing, you know, a lot of traction and
solving some of these key data problems so that you can really
utilize it not just from an agency and distribution
standpoint, but across the organization.
(29:39):
Because now you've taken in thatwhole human centric value chain
of data that can now be utilizedacross the entire organization.
Yeah, I love the way people are,you know, basically like I call
it longitude and latitude. But like, how do you?
Rethink overarching things. And I think the important part
(29:59):
there is that often can't be driven by a data scientist.
So as an example, a data scientist and I'm by the way,
I'm not saying data scientists can't run overarching AI shops
or data governance apps. So anybody listening, that's not
what I'm saying, right? But you know, like, like a data
scientist in their role is used to more R&D data.
(30:21):
They're looking for patterns anddata, right?
And say what Samantha is talkingabout.
And I, I often think about it because in my role when I was
like CFO and you know, COO and running, you know, I'm thinking
about it as like an operational internal use of data like this
is not for modelling as an example, you know what I mean?
Right. Well.
In a lot of cases, these these data scientists are being shoved
(30:43):
into the whole architectural component as well, right?
And and that's not necessarily agood, not that they can't do it
and not that some don't have that technical, you know,
capability, But there, there, there is this alignment of where
does data sit from a technical standpoint, from a business and
often point that that they need to intertwine, they need to be
(31:05):
the best of friends, right? But but there there, there is,
there is a, there is a complete separation.
I think that's really interesting because I went
through the evolution of building out a data Science
Center of excellence and how that was going to interact with
underwriting and what the role of the data scientist was going
to be, you know, like on all of this in life and health.
(31:26):
And I, I think there was this initial thought like back in the
like, like 2000, maybe it was a decade ago, you know, that data
scientists were going to kind oflike not take over, but be as
integral as, you know, the underwriter or as the actuary or
whatever. And we're, I think that we're
not kind of compartmentalizing this correctly is in those value
(31:50):
chains, those skill sets are nottechnical.
They're not even business architects.
So Sam, they're definitely not technical architects or
enterprise architects and they are not business architects.
So you need their voice and you need to be thinking along these
streams like how does our data get trained?
How does our data get used? What data do we use when we
(32:11):
detect an algorithm that's usable or pattern or we're
looking at shift analysis or whatever?
How does their feedback loop, like I call it like data flow
and all that, but they're not the people designing load, for
example, on data because they'reused to these really big
databases that don't affect operations, just as a simple
example. Exactly.
So like I think you're right in that.
(32:32):
And one of the things I think islike in terms of where you get
it wrong, where we often get it wrong is we're trying to move
people into insurance, which we should be doing.
And we're trying to commingle experience at the table, which
we should be doing. But and and we're kind of tired
of the old can't be done status quo, which we should be doing,
(32:56):
but like, but just totally make it not make sense now.
And we're like, no, no, no, that's the part we should not be
doing. Right, right.
Exactly. No, you're, you're absolutely,
you're absolutely right. I, I and I, I all too often.
I think that's what we're doing,right?
We're, we're convoluting the data, we're commingling areas
(33:17):
that not necessarily should be. We're not collaborating in areas
that we should be right. And, and that that is creating
unnecessary complication and in,in, in this whole area around
data and even the use of it, right.
Because if we can't get it, justif we can't get this data
ecosystem and architecture right, then we can't expect our
(33:38):
data scientist teams to, to do the job that they're hired to
do, right? And you're never going to be
able to use any kind of agentic AI or AI otherwise, right?
So we have to solve that problem.
And it takes, it's going to takein a village, right?
We're not talking about, you know, it takes a village to, to
(33:59):
raise a child, but it takes a village to, to run your, your,
the data center so that you can utilize the data across your
organization. So, so it's, and again, I think
there's again, I think there's clear ownership here, a little
bit of like there's the architectural piece, right?
There's the the BI piece of this, there's the AI component,
(34:21):
but without the connection, right, the ability to commingle
each kind of owner right within this, you don't have that clear
flow because one flows into the other into the other into the
other. Yeah, and they're all using,
they're all drinking from the same fountain, so to speak,
right? Yes, they are all drinking from
(34:42):
the same fountain, but somehow, some way this person says it
tastes like chocolate milk. And this one ever set here?
So the plates Kool-aid. Like let's, let's get some
optimism in here because you know, there are some good
things. Now I'm joking.
You and I are definitely the contrarians, but only because
you and I are always trying to fix things, which which is why I
(35:02):
love having you on this. We break it down in a very like
we're in it because we want to fix it.
And so we critique it because weknow it has to get fixed.
And we don't just pedal the oh, you look great today, but who is
getting it right? Who's moving in the right
direction? What does real data hygiene look
like to you? And you know, what are some
building blocks that you think agencies or carriers could
(35:23):
invest in? Yeah, No, I, I, I mean, I think
that what good looks like I, andI'm not going to say that
there's any one person out there.
I think that is, is doing well. I still think we've got quite a
ways to go. But I do think that there are
insurers out there that are doing the right things now.
(35:45):
So what does that mean? They are thinking more value
stream, they are thinking your latitude and longitude, right.
They're getting down into the simplification of even their
vendor landscape, right. So there's not 1000 cooks in the
kitchen. And as a result, you'll, you see
(36:05):
improvements across the entire value chain, you know, and that,
you know, again, that horizontalversus vertical.
So you're seeing a lot of that transition happening right now.
And it's difficult, right, because that's only a culture
shift. It's an everything shift, you
know, Oh my gosh. So, so I think that that
approach, it is happening and tome that's what's going well.
(36:28):
I think that we've recognized that that's the way the right
path forward, right simplifying and also the simplification of
this legacy. I know it's hard.
We can't necessarily pick it up and migrate it and just throw it
over somewhere else. It's not always that easy, but
trying to simplify what that that that legacy landscape looks
(36:49):
like, you know, it is another area where I see a lot of
insurers really honing and focusing on and starting to get
that that right, that vision correctly.
As a result, each one of those things impacts the data because
data is a key consideration for all that's being done.
So again, putting data in in kind of that heart, right?
(37:11):
Data, data is the heart of everything that we do, every
decision that we make. What is it going to do?
How's it going to push our future?
How are we going to use that information to do better?
How we're going to use how we'regoing to get our architecture
correct so that we can use AI across different areas and test
and learn, test and learn, test and learn.
So it's happening. I, I think that a, a lot of, I'm
(37:35):
seeing a lot more as we bubble this back to distribution, if
you will, a lot more on the captive agent side.
And I think once some of these, some, some of these carriers get
a good taste for what that lookslike and how that feels, then
we'll start to see more kind of on that independent agent, you
(37:56):
know, MGA type BGA, you know, solve, solve for some of those
bigger and larger distribution challenges we're facing.
Yeah, I and I, I, I hadn't thought of that.
Kind of like starting at the captive and then figuring out a
way to put the governor on, if you will, and then release it to
the independence, but kind of get that captive side right
first, because that's a, that's a nicely aligned incentive
(38:21):
structure first as well too. And you know, you've got to
still meet your goals and your sales and all the things so
that, that kind of like you're, you're still, I always tell
people we can talk architecture all day long, but at the end of
the day, money is still kind of the guiding factor in terms of
what causes someone to care, right?
Like we are run by the PNL, we are in it, you know, like we, So
(38:45):
I, I think that's an interestingalignment and I love that you're
seeing that. That's really encouraging.
Yes, I, I'm feeling, I'm feelingso much better this year given a
lot of the conversations and, and things that we're doing
today with our clients and, and,and, and then them, you know,
it's not just us pushing them. They're now saying, okay, yeah,
you're right, you're right, but what are we going to do?
(39:05):
How are we going to fix this problem?
And, and I think the other important factor is here that
they're not just saying, let's throw something at it.
They are thinking more foundational.
They are saying yes, we'll, let's do the assessment, let's
understand and and to and to me again that that is the basis for
anything in any kind of transformation to show growth.
(39:27):
Yeah, I, I'm really excited about tracking that.
So I mean, we've covered so muchground, right?
What are, I mean, you've seen somuch and you've got just such a
breadth of experience as we start to kind of wind this
episode down. You know, my hallmark is like
call to action. What would you ask people to
(39:48):
start doing, stop doing and continue to do?
And then kind of feel free to connect that into any final
observations that you have aboutlike how we can really change
the narrative for distribution. Yeah, again, I think it is like
I said just a moment ago, start with the start with the the
diagnosis, start with the analysis, understand where you
(40:09):
are today. What's stopping you from being
from, from really getting out there and, and, and providing
your different distribution channels the the right tools.
Ask yourself all the difficult questions and, and I think that
you'll find that there's a basisand it gives you it gives you
the tools that you need to really kind of track and and
(40:31):
monitor change. Do not layer on any more
technology. Do not throw another portal.
Do not let's not just get out there and start trying to throw
AI or bots or this that and the other at the problem.
Again, it's not going to help you.
You're just spending money. The lipstick on the pig will
continue. You're just layering a different
color. So let's really stop throwing
(40:53):
tech at it thinking that's goingto make everybody happy, because
it's it's really not. And the other thing is, you
know, listen to, you know, continue to listen to your
agents. They're the ones, you know, that
are, that are doing this from day-to-day.
Listen to what they're saying. Continue to think about, you
know, different ways to create better experiences, but tie it
(41:16):
back to where you are today. Why can't you have that, that,
that, that experience? What's stopping you?
And I think the, the kind of thelast one is, you know, and I
don't know if this is a continueor maybe a start.
It's a little bit of both. I think honesty, be honest with
yourself about where you are, what you're trying to achieve
(41:39):
and whether or not you're reallymaking making impact on it.
So I guess those are my start, stop and continue.
And, and again, I think more importantly and again, I know
we're competing for market shareout when it comes to consumers.
We're competing for, for, you know, space within that
(42:00):
independent agent. You're also competing for the
next wave of agents, right? Those younger demographics.
You're not going to solve the insurance ownership gap if you
cannot solve the hiring of new younger agents and attracting
(42:20):
them and retain them. They are 1 to 1.
I love that it's such an it's such an important, you know,
ecosystem in the sense of one thing affects the other, yes,
you know, and couldn't be more relevant.
And I just, I love first of all,I love having you on the
podcast. I feel like you would have to do
(42:40):
a little miniseries. About one series.
I'm ready. Has signed me.
Up but you know every time Samantha calls me with a you
know hey Lisa, are you seeing this you know MERS you know what
are you seeing I'm like yes, totally agree with you and I you
know I think to our listeners it's so easy to think that you
(43:02):
can solve the problem in a traditional way.
What we're telling you is as industry veterans and very
passionate people about solving authentically yeah, don't follow
that inertia right. Saying that like the undertow is
going to catch you, that you're not going to ride that way,
you're going to get pulled under.
And we're just trying to say, hey, we just don't want you to
(43:25):
like get pulled under and, and, and get get caught up in that
undertow. And I love your Start.
Stop and continue. If any of our listeners don't
follow you, you do such wonderful world reports,
studies, blogs, writing. I mean, I, I'm obsessed with
your thought leadership and content that you all produce
(43:45):
both yourself and the Cap Geminireport.
So please follow Samantha Chow and all the work they're doing
at Cap Gemini. It's, it's I, I've been lucky
enough to be featured in one, but all of the reports they do
are just phenomenal. And for all of our listeners,
please continue to stay curious,stay informed and stay plugged
in. Thank you so much, Samantha, to
being a guest today. My pleasure.
(44:07):
In today's episode of Insurance Unplugged, the AI and
Distribution series is proudly sponsored by Iris and Suretech,
your gateway to the future of insurance distribution.
Iris harnesses the power of generative AI to transform data
processing and decision making across the distribution
landscape. The Iris platform integrates AI
(44:29):
driven decision engines, dynamicform generation and configurable
workflows, all underpinned by continuous data quality
management. Discover how Iris is powering
smarter operations and more efficient distribution with
cutting edge AI setting a new standard of excellence across
the entire industry.
Insurance Unplugged in the hot seat where the complex world of
insurance is laid bare. Hosted by Lisa Wardfall, this
podcast promises an unfiltered glimpse into the industry like
never before. Each episode invites you to
listen in on the candid conversations that usually
happen behind closed boardroom doors.
From deep dives with industry leaders and thought leaders to
(00:23):
innovative discussions with minds shaping the future of
insurance, we bring the most genuine talks directly to your
ears. Our guests take the hot seat
alongside me to explore the inner workings, challenges and
triumphs of the insurance world.If you've ever.
Wondered what goes on in the shadows of the insurance
industry? From the boardroom banter to the
(00:44):
behind the scenes strategies, this is your chance for a front
row seat. Prepare for unguarded,
enlightening and engaging discussions that cover every
angle of insurance presented in a way that's both insightful and
accessible. Welcome to the conversation.
Welcome to Insurance Unplugged in the hot Seat with Lisa
Wardbaugh. Welcome to today's episode of
(01:07):
Insurance Unplugged, proudly sponsored by Iris and Suretech,
your gateway to the future of insurance distribution.
At Iris, we harness the power ofgenerative AI to revolutionize
data processing and decision making across the distribution
spectrum. Our platform integrates Gen.
AI to provide not just insights,but actionable intelligence,
(01:28):
configurable work flows, and dynamic form generation, all
underpinned by continuous data quality management.
Discover how Iris is pioneering smarter, more efficient
operations in the insurance industry, paving the way for a
new era of distribution excellence.
Let's dive into how Jen AI is transforming the landscape of
insurance distribution today on Insurance Unplugged.
(01:52):
Welcome to another episode of Insurance Unplugged.
I'm your host Lisa Ward Law and joining me in the hot seat
today. Slight repeat guest, but we're
going to take it in a different way.
I I will tell her like you can'tget away from me and it's one of
my dearest and just you know, most mentored idol leaders in
(02:13):
our industry. Samantha Chow Samantha, welcome
to the hot show. Thank you for agreeing like
sorry, Samantha, welcome to the hot seat.
Thank you for agreeing to do it twice after you already
experienced it once. I appreciate your your
fortitude. If you don't mind, you are the
global insurance leader at Captain and I.
If you don't mind giving everyone an introduction to
(02:34):
yourself in case they missed ourfirst episode.
Because today we're going to go.Really detailed into
distribution, so let's go. Yes.
So thanks again for having me asalways Lisa, you blow my head up
with all sorts of gratitude and excitement.
But so, yeah, so I had life insurance, annuities, benefits,
(02:55):
pensions globally for Captain and I as well as distribution
for all insurance lines of business based on kind of my
background and multi line carriers and multi line
distribution strategy. So again, it is it is one of my
favorite topics. It is a very, very hot topic as
you know and you're dealing withas well.
(03:16):
So I'm happy to be in the hot seat.
Let's go. Let's go, we're tortured out,
let's go. So you know, and I'm so glad you
came back because often in our industry there's so much focus
on PNC as there should be, but we often overlook life annuity,
health pension, etcetera. And so Samantha is one of my go
(03:38):
TOS because she and I come from that world, We've lived that
world. And today we really want to
tackle, you know, the rising coverage gap, this expansive
coverage gap and the role of empowering agents.
So like really, what are the hard truths?
What are the real fixes out there?
We, we just did one on insuranceunplugged couple weeks ago where
(04:02):
we started getting into distribution, empowering the
agents for life, health annuities.
And Sam and I are going to take,take that into like a detailed,
you know, lipstick on a pig. If you don't tackle that
operational backbone, there's only so much gloss you can put
on it. What, what?
Let's lead into that. Samantha, what, what do you
(04:23):
think is actually broken? Everyone is on the hype cycle.
Everyone is on, you know, and I get it, right.
I used to run AP and L quick mixes immediacy.
Let's talk to you about what's actually broken.
And and then we're going to get into really like, what's it
going to take to fix this? Yeah, honestly, I think what's
what's broken is where we've started, right.
(04:44):
Again, you talk about lipstick on the pay.
Let's throw a portal out here. Let's throw this capability out
there and let's toss it over to the agent.
And we're going to try and make it look like they get access to
do more control more, have more power, more transparency.
And all of these things have allthey've done is create
complexity, right? Nothing is right.
(05:05):
There's no, we didn't solve an actual problem.
We didn't give them the tools that they needed to personalize
their sales approaches or trigger them to the need or a
life trigger for one of their clients.
We just literally put the lipstick on the pig, throw in a
piece of technology at them and expected number one, that they
(05:27):
would use it. OK, without any kind of like
examples of how to use it or, oror what it brings to the table.
But then those that do get into it realize this is nothing.
There's nothing here. You just add one more tool to me
to try and figure out how to use.
That's the crazy thing, I think,Samantha, since I since I've
gotten more and more and more like swimming to the front,
(05:49):
right, more and more to the front end.
The let's take the portal example, right?
And on one hand, I get it, like,you know, carriers are like,
hey, serve yourself. Here's a portal.
And then on the distribution side, I see.
And this is horrifying to me, bythe way.
But the real life is most peopleare having bots.
(06:11):
This is prior to AI even. Right.
Rape into portals and get data out of portals.
And I'm like what? Or, or to your point, the agents
are saying the data and information is so bad, it's so,
you know, whatever convoluted this, that or the other that
we're not even going to. Use.
(06:32):
It. Right.
The foundation is broken. The foundation that feeds
everything, every tool, every everything is broken.
So that lipstick, that portal isdoes nothing for them if they
don't fix the foundation and usethe right information, the right
(06:55):
data in the foundation to feed those agents and the and you
know, and the tasks that need todo the transparency, you know,
compensation and all, none of that can be transparent.
It can't work without the foundation and the data behind.
It and I think it's really interesting, Sonia thought.
(07:15):
Because, you know, I spend a lotof time, you know, both too on
foundation. I I actually, I actually did the
series about like why I don't demo tech.
And my point on that is like same reason why I don't test
drive a car. I don't care about the things
that people can knowingly make pretty the interior, the way it
looks and feels. I am an engine girl.
(07:36):
Open up the hood. I mean, Sam, this is like when
we first met, right? Open up the hood.
And I usually say, get all your sales demo people out of the
room. Bring in the technical
architect, bring in the enterprise architect, and the
signal there for anybody that ever tries to sell to me is I am
looking for architectural foundational congruency.
(07:56):
Everything else is like I can get.
And of course, I look at experience layers.
That's all like an after the fact.
I've got to see if the architecture holds together.
So I have an answer to this, butSamantha, I want to know your
perspective on this. You've worked a lot with Carrier
and Agents. How far behind is the
architecture really? Oh, you know, it depends on the,
(08:20):
the, the which is sure you're looking at, but for the most
part, they're very, very far behind.
There are so many different architectural views of, you
know, I, I, I like it, like to call it the yesterday, the
decades ago, the millennia ago, right?
These different architectural designs.
(08:41):
And then you have like tomorrow's view that people are
just trying to start dabbling in.
So not only as an industry or within the insurer do we have
these brick walls that have created, been created in between
like different organizations throughout, you know like
underwriting and claims and marketing there.
Those brick walls exist within the architecture and as a result
(09:03):
of that, you know, getting back down to that foundational point
of view if we don't. Solve.
For those brick walls and bring all these components together,
then again, you're not going to be successful in anything that's
driven by data, right? Never right, I think.
We're bringing up some amazing points and and you're, you're
giving that also from the carrier lens at the the agencies
(09:28):
are even further behind. Well, exactly if.
We go into agency oh let's just not that yeah, that's even
that's millennia's behind right.Let's go back category.
We think like. Let's take it in order, right?
We've got PNC, then we've got life and annuity and then we'll
go back a couple of decades in in time and then pick up the
agencies. That's right.
It's true. It's true.
(09:49):
So like, like, let's take a simple example because I want to
then connect this to AI, right? Because we're, we're living in
these two worlds and you and I are not here to say, oh, wait,
you can't do anything good. You're so far behind.
You and I are always trying to figure out like, OK, what is the
foundation that we can build on?So like a simple example, Agency
systems weren't created the sameway policy administration
(10:12):
systems were created. So even if we think policy
administration systems and the way they connect between claims
and underwriting and pricing andall the things are siloed and
antiquated, they were created tomanage the life cycle of a
policy. Even if Sam, you and I say
they're not modern or they have problems, right?
Agency systems, which are actually a symbiotic
(10:35):
relationship to reinsure systems, meaning we're not the
core carrier processing the business, but we have to move
the policy kind of in a I, I call it like a self administered
way. It's self administered by the
carrier. But we the agency or we, the
reinsurer in this example need to have policy movement.
We need to know if it cancelled,if it reinstated, if it lapsed,
(10:58):
if it, you know all these things.
Well, Samantha, do you think that systems that were developed
for sales in any way, shape or form had the fortitude to handle
policy level movement? I mean, they don't even have a
concept of master data management, much less robust
policy holder movement. So of course, these poor agents
(11:20):
are now trying to put, we're going to get to AI in a minute.
They're not trying to put these things on top of these in, in my
mind, fractured systems like, you know, like how do you think
about that? Like, are they actually poised
to personalize and convert or they're really operating with a
blindfold on right now? No, the blindfold, total
(11:41):
blindfold. And I mean, and let's add into
the complexity of this that theyonly have, they don't have just
one. If you're looking at some, you
know, MGA who sells, you know, 6different insurers, life
insurance policies, then then you and that's what most of them
do these days. The whole captive agency is is
few and far between. And that's a much easier solve.
(12:02):
But when we get into this more brokerage LED and they're
integrating into multiple systems, then they're dealing
with those multiple carriers data problems, right.
So I mean. And say something like that just
to kind of slightly interject. I can recognize this because I
(12:24):
used to deal with that as a reinsurer.
And I had really, you can say antiquated, but I had really big
data, really big data governanceand really big processing to
deal with this. And by the way, Samantha, I had
really big IT teams. I had really big BA teams.
And I'm not saying we did it elegantly.
Like I spent a decade trying to break it down and rebuild it.
(12:47):
Yeah. But I knew how to deal with.
It. Yes, I, I, I don't think that
people whose budgets are typically margin based on
commissions doing front end, I don't think when they're given
this, I'll call it mishmash of data.
Sam, I don't know that they've been through the.
(13:09):
How do I put all of this together now that we are
becoming so data centric? Yeah.
I mean to your point, the the push has got to come from the
insurer, right? They're the ones that have the
the money, if you will. They also have the IT teams,
they are the ones that should beresponsible for ensuring that
(13:32):
the data. And again, this goes also goes
back to the fact that if you, you we're not competing just for
market share out in out there interms of like shrinking this,
you know, ownership rate of lifeand, and and protection
coverage. We're also competing for the
market share within the agents that sell our business, right?
(13:52):
So as an insurer, it's our responsibility to give them the
tools, not only the tools, but the data itself.
So we, I'm thinking with my, my,my insurer, my carrier hat on
have to change the data structure.
We have to give them in whateverway, shape or form we can that
supports their IT infrastructure.
(14:14):
It can't be the other way around.
It won't work that way. So, so how do you see this
change coming out like as we think about it kind of moving
into the second phase of this, right?
I think of it as like two sides,no bridge.
And by the way, if anybody asks us in the comments and says,
well, that's what AP is are for and portals are for, I will on
(14:38):
demand, like throttle down with you on that because no, and API
cannot solve that. A portal does not solve that.
So Samantha and and I think that's part of the like heresy a
little bit of the we're, we're addicted to the magic Diet Pill,
right? Like quick fixes and hits.
And I'm like, OK, so two sides, no bridge.
(15:01):
Let's acknowledge. I think Sam, if you agree with
me that AP is are not the solution.
No. OK.
Leading the wings not agreed. OK.
So then how do we shift, how do carriers shift enablement and
how do we think about how do we reimagine data sharing and
(15:24):
mutual enablement of the people that we need to sell this?
Again, I think it goes back to we, we, the, the carriers, they
need to rethink #1 their data structure, right in the
architecture, because data is all over the place, right?
What is the right data that thatis needed?
How do we want to share that? How do we want to get that to
(15:46):
those distribution channels withwhatever their distribution,
whether we're talking about captive agents or independent
agents and, and Mgas or, or whatever it is, they need to
rethink what that data and architecture looks like in order
to ensure that we're giving themthe right information to feed.
(16:08):
You know, again, going back to it's not the portal that is, it
is the the end all be all right here.
You have to be able to feed the information to them through that
portal. It's just a conduit.
If the data and if the data thatwe are looking at and and how
the amount of control and power that we want with that flow of
(16:30):
data, the flow of application, the flow of underwriting, the
flow of claims, all of this, if it's not fixed from a carrier's
perspective, then no, no amount of portal fanciness is going to
help them. Yeah.
And I, I think it's fascinating,right?
Because there's been a lot of work on data standardization,
(16:52):
which in my mind hasn't taken, it never has taken, no, how do
you think about that? Because I, I used to say even
back again, back in the reinsuredate, which is like the mirror
image of the agents. I used to say, I'm going to
solve this problem with no codependency on a standard data
model because the standard data model is never going to exist.
And even if it does exist, the dynamic nature of the data we
(17:13):
need is going to outpace that. So I used to think of this as
how do I solve a labyrinth problem?
You know, like I can't be dependent on because it's easy
to kind of, you know, if you're listening to this and you're an
agent, it's easier to sign and say, yeah, you need to come up
with a common data definition. Yeah.
OK, Good luck. Like we'll see you in.
How many centurions from now, Samantha?
Exactly. So what do you think about data
(17:34):
enablement? I started thinking dynamic data
exchange with like security architecture.
It's kind of boring. But like, you know, like I
started thinking about that, I mean, because I narrowed it out
on it. How do you think about that?
And are are you seeing anybody breakdown this in terms of, OK,
we all know we need to exchange data, but what gets hard is the
how? The how is always going to be
(17:56):
the the, the crux of of all of this.
You're not just feeding. Again, if you're talking about a
captive agency, you know, that'sa little easier to deal with,
right, Because it's a one to oneexchange.
But we start to think about these distribution strategies
where you're using multiple different, you know, Mgas and
independent agencies and, you know, the data you decide that
(18:19):
you want to feed one is may not be the same for another or who
may not be the same for another.It all comes down to, and you
know, again, you're, you're architectural, right?
You, you get a little more, moretechie than than than I do.
I keep it a more a little bit more business.
Than I go down into the tech to try to solve it.
Then I come back up to the business problem because I'm
(18:40):
like, wait, I think about it like this Samantha, like OK,
I'll use a simple example. Data is our asset and the the
fundamental issue that we have again, this is business terms.
The fundamental issue that we have in exchanging and sharing
data is I can't just give and I'll use it as a car.
I can't just give my car to thembecause they could use it for
(19:04):
any purpose and they could take anybody else out on a date.
How do I know that, like in thisplace, placing business means
dating? How do I know that they're only
going to use my car to date me? Yep.
Like and and like, let's keep itat that level, right?
Like to me, that's a very business abstracted level.
Because what's, what's interesting about that is of
(19:24):
course we need independent agents.
To your point, we need Mgas thatsell multiple carriers.
We, society doesn't always buy from exclusive agents and we
have a huge independent agent distribution economy.
So the, the, the like, when I started analyzing this, the
(19:44):
reason why I went down into the technical side to like nerd out
was when I started at the business side, I kept saying,
everyone kept saying, well, we need to exchange data.
And I was like, yeah, but like, just think about it logically,
right, Sam? Let's just pretend I'm going to
loan you. I'm going to make it up.
I have a Ferrari. I'm going to give you my Ferrari
(20:04):
this week to use. Well, Sam, how do I know if I
give you my Ferrari that you're only going to use my Ferrari to
entertain like my business people?
Like whatever. Like, you know, do you see what
I'm saying? Like that's the problem with
exchanging data. Yeah.
But so we let's go back to your statement around, you know
standardizing, right. So from my business perspective,
(20:27):
when I think about standardize, you're never going to
standardize the data, right. Like you said, like it's going
to continuously so dynamic, it changes what what data is
important today is, is new data tomorrow, right where it comes
from, all of all of that. But what doesn't necessarily
change is the standardization and how you deal with certain
types of distribution channels. As a carrier, I define my
(20:50):
distribution channel. And in this distribution
channel, you know what, I'm going to give them the Ferrari,
but they're going to have a a cap on the speed at which they
can go. So I'm going to implement that
little trigger that says you hit50 miles an hour.
Thank you. You're done.
But the other one over there a. Regulator on my Ferrari.
(21:10):
I've got a regulator doing the Ferrari, which is a real bummer,
but but we're going to go back to that hierarchical structure
can be standardized and for thisparticular distribution channel,
here's what you're going to get and that's standard.
You get no more no less than theexact person that might be
(21:31):
appear to you. Now my captive agency again,
they get everything they get theentire furry.
They get to go have at it because they're they're captive
they're just selling for me. They're not going to do anything
else with my data. So that and my interview solves
not only the business problem, but it solves the the technical
(21:55):
problem in the sense of now I have something to work towards
in my technical architecture, right.
And what I feed and how I feed, yeah.
Yeah, I do. I I definitely think that that
works on the what is standard. I I still think that there is
(22:16):
lens of how do I know you're only using that you're not
siphoning the gas out of my Ferrari and putting it in
another car, right, because thatright and say, like I started
then going down into like whatever true data, dynamic data
exchange and how I do this and how I know that my data is only
being used. There are concepts that are
(22:36):
emerging, which is interesting, right?
We're also fixated on AI fixated, but what we're fixated
on is the the the gloss, the high gloss parts of AI.
What we're not fixated on yet are the actual architectural,
truly unlocking components of AI.
But I see people just like, well, it's too technical or
(22:58):
like, I'm not going there or like I've got to make copilot
work first. I'm like, whoa, the thing that
you're actually trying to get tofollow the trail like big
shining lights here. And so, Sam, I find it really
interesting because I'm somebodythat always thinks that the
problems that we're solving are relatively the same.
Yeah. I mean the problems that you and
(23:18):
I are talking about, would you say that these have changed that
much over the last? 10 or 20 years.
No, they are the exact same problems.
We just, I mean, I think we put more complexity around it
without solving the actual problem.
So you're, you're you're, you'respending money, you're spending
time, you're creating and layering in more complexity
without solving the actual physical problem.
(23:42):
So let's get into our third theme, which to me is like where
I love to sit, right? The hidden cost of getting it
wrong, which is kind of what we're alluding to, right?
If you it's what, what the definition of insanity, doing
the same thing over and over andexpecting different outcomes.
We can all take that one to the bank.
But like, you know, you and I have both seen it, right?
(24:02):
Depending on the size of the agency, or you know, you may be
somewhere in the neighborhood ofa couple million, you may be all
the way up to $20 million. Tech programs, wasted agents,
unsupported boards, feeling likedashboards are indicating but
they're not. What's the cost of actually
continuing to pretend you can build on top of BI tools from
(24:24):
the back end without fixing the foundation, without enabling the
data, without doing the architectural business
enablement things you and I werejust discussing?
Because I see a ton of people coming in on a, we can make the
dashboards look pretty and I'm like, OK, but the data
underlying it is what we still have to unlock.
So, So over to you on what you're seeing there.
(24:45):
Yeah. No, I again, it really starts at
that analysis diagnostic gettingdeep into the depths of the
data. We, we are so far behind this
whole idea of data mastery, right?
We keep throwing even even from an, you know, insurer carrier
level, like throwing the throwing data scientists at it.
(25:08):
You know, they're, they're not getting into the depths of the,
the actual data, the the data itself, right?
You can't just create a, you know, total dashboard type
situation where where you're trying to hone in and and I'm
going to say pause real quick. Yeah.
(25:32):
Cut and recut. No worries, you can just say
resume when you're ready. I I even locked the door.
And you know what? Off record.
Mine comes in after a shower. Like mom, I'm looking for
clothes. I'm like, I'm recording the
podcast, dude. What like.
Sorry, that was my whole thing too, I wasn't even a kid.
Well, mine does that too, anyway.
(25:53):
OK. So to our producer, thank you
for understanding that we're working women and this happens.
Yes, indeed. OK, dashboards, is that where
we? Were rewind we were talking
about dashboards and the data scientists thinking that they
can normalize the data basicallythrough data science on top of
(26:14):
ABI tool over to you to resume. Okay, to resume, yes.
So yeah, no, the whole we can't hone in on just throwing more
data scientists down down to it.We have to get back into that
diagnostic level so that we can really, truly understand the
data. Put it in a format that is
actually usable because it's so sporadic so far across.
(26:38):
Bring the right variables in together.
And then from there you have an opportunity to to kind of get
out and start to use those dashboards, right?
You can't just throw like it's ABI tool and we're going to
throw it and we're going to pushall that data up into some
beautiful dashboard can look beautiful, but that doesn't mean
it's actionable or usable. Yeah.
And and I've seen so much work on the, I won't even go there,
(27:03):
but I see so much work where these where these data
governance people are coming from the BI background.
And I'm like, no, no, no, no, no, you can't be making
decisions on data governance anddata enablement because you're
used to everything being this back end thing.
(27:23):
And that's not the way kind of event native processing, which
is where we're going to go with a genetic AI in a moment.
That's not the way it's going tothink about data.
So like, I really feel like, andagain, Samantha, honestly, 15
years ago, we were just scrapingby, like, right.
We were trying to get out of static PowerPoint monthly
(27:46):
summaries. I get it, I get it.
I had a whole VI team working for me, Samantha.
Like, I get it. I lived through it.
I was the one saying build the snow.
But that's not today, right? Like it's like, that's the part
I think of like leapfrogging ahead.
It's like, OK, what am I doing today?
Where are my efforts going? And how can I create true value?
(28:09):
And yeah, sometimes you can takethe data as it is and unlock a
diagnostic or inside or whatever.
But to your point, if the data didn't ever get there, if the
data that was placed there wasn't fully what you needed,
then no amount of reporting in the world can get you those
insights that you're seeking. And I mean, do you see people
(28:32):
starting there like in a lot of the work you do or where, where
do people start? Typically, honestly, they're not
starting deep enough. They're still, they're still
honed in on that superficial I think, but we're seeing more and
more kind of internal realignment, which I think is
great, right? This whole idea behind value
streams and creating value stream alignment and then even
(28:55):
going deeper and, and saying, yeah, we've got some vertical
value streams, but what are the value streams that are
horizontal? And data happens to be one of
those, right? So it touches everything from,
you know, from your, from product to, you know, purchase
experience and customer experience all all the way
through just like your architectural, you know, your
(29:17):
platforms, your policy admins, things of that nature.
So now we've got this whole horizontal and vertical approach
is is where we're seeing, you know, a lot of traction and
solving some of these key data problems so that you can really
utilize it not just from an agency and distribution
standpoint, but across the organization.
(29:39):
Because now you've taken in thatwhole human centric value chain
of data that can now be utilizedacross the entire organization.
Yeah, I love the way people are,you know, basically like I call
it longitude and latitude. But like, how do you?
Rethink overarching things. And I think the important part
(29:59):
there is that often can't be driven by a data scientist.
So as an example, a data scientist and I'm by the way,
I'm not saying data scientists can't run overarching AI shops
or data governance apps. So anybody listening, that's not
what I'm saying, right? But you know, like, like a data
scientist in their role is used to more R&D data.
(30:21):
They're looking for patterns anddata, right?
And say what Samantha is talkingabout.
And I, I often think about it because in my role when I was
like CFO and you know, COO and running, you know, I'm thinking
about it as like an operational internal use of data like this
is not for modelling as an example, you know what I mean?
Right. Well.
In a lot of cases, these these data scientists are being shoved
(30:43):
into the whole architectural component as well, right?
And and that's not necessarily agood, not that they can't do it
and not that some don't have that technical, you know,
capability, But there, there, there is this alignment of where
does data sit from a technical standpoint, from a business and
often point that that they need to intertwine, they need to be
(31:05):
the best of friends, right? But but there there, there is,
there is a, there is a complete separation.
I think that's really interesting because I went
through the evolution of building out a data Science
Center of excellence and how that was going to interact with
underwriting and what the role of the data scientist was going
to be, you know, like on all of this in life and health.
(31:26):
And I, I think there was this initial thought like back in the
like, like 2000, maybe it was a decade ago, you know, that data
scientists were going to kind oflike not take over, but be as
integral as, you know, the underwriter or as the actuary or
whatever. And we're, I think that we're
not kind of compartmentalizing this correctly is in those value
(31:50):
chains, those skill sets are nottechnical.
They're not even business architects.
So Sam, they're definitely not technical architects or
enterprise architects and they are not business architects.
So you need their voice and you need to be thinking along these
streams like how does our data get trained?
How does our data get used? What data do we use when we
(32:11):
detect an algorithm that's usable or pattern or we're
looking at shift analysis or whatever?
How does their feedback loop, like I call it like data flow
and all that, but they're not the people designing load, for
example, on data because they'reused to these really big
databases that don't affect operations, just as a simple
example. Exactly.
So like I think you're right in that.
(32:32):
And one of the things I think islike in terms of where you get
it wrong, where we often get it wrong is we're trying to move
people into insurance, which we should be doing.
And we're trying to commingle experience at the table, which
we should be doing. But and and we're kind of tired
of the old can't be done status quo, which we should be doing,
(32:56):
but like, but just totally make it not make sense now.
And we're like, no, no, no, that's the part we should not be
doing. Right, right.
Exactly. No, you're, you're absolutely,
you're absolutely right. I, I and I, I all too often.
I think that's what we're doing,right?
We're, we're convoluting the data, we're commingling areas
(33:17):
that not necessarily should be. We're not collaborating in areas
that we should be right. And, and that that is creating
unnecessary complication and in,in, in this whole area around
data and even the use of it, right.
Because if we can't get it, justif we can't get this data
ecosystem and architecture right, then we can't expect our
(33:38):
data scientist teams to, to do the job that they're hired to
do, right? And you're never going to be
able to use any kind of agentic AI or AI otherwise, right?
So we have to solve that problem.
And it takes, it's going to takein a village, right?
We're not talking about, you know, it takes a village to, to
(33:59):
raise a child, but it takes a village to, to run your, your,
the data center so that you can utilize the data across your
organization. So, so it's, and again, I think
there's again, I think there's clear ownership here, a little
bit of like there's the architectural piece, right?
There's the the BI piece of this, there's the AI component,
(34:21):
but without the connection, right, the ability to commingle
each kind of owner right within this, you don't have that clear
flow because one flows into the other into the other into the
other. Yeah, and they're all using,
they're all drinking from the same fountain, so to speak,
right? Yes, they are all drinking from
(34:42):
the same fountain, but somehow, some way this person says it
tastes like chocolate milk. And this one ever set here?
So the plates Kool-aid. Like let's, let's get some
optimism in here because you know, there are some good
things. Now I'm joking.
You and I are definitely the contrarians, but only because
you and I are always trying to fix things, which which is why I
(35:02):
love having you on this. We break it down in a very like
we're in it because we want to fix it.
And so we critique it because weknow it has to get fixed.
And we don't just pedal the oh, you look great today, but who is
getting it right? Who's moving in the right
direction? What does real data hygiene look
like to you? And you know, what are some
building blocks that you think agencies or carriers could
(35:23):
invest in? Yeah, No, I, I, I mean, I think
that what good looks like I, andI'm not going to say that
there's any one person out there.
I think that is, is doing well. I still think we've got quite a
ways to go. But I do think that there are
insurers out there that are doing the right things now.
(35:45):
So what does that mean? They are thinking more value
stream, they are thinking your latitude and longitude, right.
They're getting down into the simplification of even their
vendor landscape, right. So there's not 1000 cooks in the
kitchen. And as a result, you'll, you see
(36:05):
improvements across the entire value chain, you know, and that,
you know, again, that horizontalversus vertical.
So you're seeing a lot of that transition happening right now.
And it's difficult, right, because that's only a culture
shift. It's an everything shift, you
know, Oh my gosh. So, so I think that that
approach, it is happening and tome that's what's going well.
(36:28):
I think that we've recognized that that's the way the right
path forward, right simplifying and also the simplification of
this legacy. I know it's hard.
We can't necessarily pick it up and migrate it and just throw it
over somewhere else. It's not always that easy, but
trying to simplify what that that that legacy landscape looks
(36:49):
like, you know, it is another area where I see a lot of
insurers really honing and focusing on and starting to get
that that right, that vision correctly.
As a result, each one of those things impacts the data because
data is a key consideration for all that's being done.
So again, putting data in in kind of that heart, right?
(37:11):
Data, data is the heart of everything that we do, every
decision that we make. What is it going to do?
How's it going to push our future?
How are we going to use that information to do better?
How we're going to use how we'regoing to get our architecture
correct so that we can use AI across different areas and test
and learn, test and learn, test and learn.
So it's happening. I, I think that a, a lot of, I'm
(37:35):
seeing a lot more as we bubble this back to distribution, if
you will, a lot more on the captive agent side.
And I think once some of these, some, some of these carriers get
a good taste for what that lookslike and how that feels, then
we'll start to see more kind of on that independent agent, you
(37:56):
know, MGA type BGA, you know, solve, solve for some of those
bigger and larger distribution challenges we're facing.
Yeah, I and I, I, I hadn't thought of that.
Kind of like starting at the captive and then figuring out a
way to put the governor on, if you will, and then release it to
the independence, but kind of get that captive side right
first, because that's a, that's a nicely aligned incentive
(38:21):
structure first as well too. And you know, you've got to
still meet your goals and your sales and all the things so
that, that kind of like you're, you're still, I always tell
people we can talk architecture all day long, but at the end of
the day, money is still kind of the guiding factor in terms of
what causes someone to care, right?
Like we are run by the PNL, we are in it, you know, like we, So
(38:45):
I, I think that's an interestingalignment and I love that you're
seeing that. That's really encouraging.
Yes, I, I'm feeling, I'm feelingso much better this year given a
lot of the conversations and, and things that we're doing
today with our clients and, and,and, and then them, you know,
it's not just us pushing them. They're now saying, okay, yeah,
you're right, you're right, but what are we going to do?
(39:05):
How are we going to fix this problem?
And, and I think the other important factor is here that
they're not just saying, let's throw something at it.
They are thinking more foundational.
They are saying yes, we'll, let's do the assessment, let's
understand and and to and to me again that that is the basis for
anything in any kind of transformation to show growth.
(39:27):
Yeah, I, I'm really excited about tracking that.
So I mean, we've covered so muchground, right?
What are, I mean, you've seen somuch and you've got just such a
breadth of experience as we start to kind of wind this
episode down. You know, my hallmark is like
call to action. What would you ask people to
(39:48):
start doing, stop doing and continue to do?
And then kind of feel free to connect that into any final
observations that you have aboutlike how we can really change
the narrative for distribution. Yeah, again, I think it is like
I said just a moment ago, start with the start with the the
diagnosis, start with the analysis, understand where you
(40:09):
are today. What's stopping you from being
from, from really getting out there and, and, and providing
your different distribution channels the the right tools.
Ask yourself all the difficult questions and, and I think that
you'll find that there's a basisand it gives you it gives you
the tools that you need to really kind of track and and
(40:31):
monitor change. Do not layer on any more
technology. Do not throw another portal.
Do not let's not just get out there and start trying to throw
AI or bots or this that and the other at the problem.
Again, it's not going to help you.
You're just spending money. The lipstick on the pig will
continue. You're just layering a different
color. So let's really stop throwing
(40:53):
tech at it thinking that's goingto make everybody happy, because
it's it's really not. And the other thing is, you
know, listen to, you know, continue to listen to your
agents. They're the ones, you know, that
are, that are doing this from day-to-day.
Listen to what they're saying. Continue to think about, you
know, different ways to create better experiences, but tie it
(41:16):
back to where you are today. Why can't you have that, that,
that, that experience? What's stopping you?
And I think the, the kind of thelast one is, you know, and I
don't know if this is a continueor maybe a start.
It's a little bit of both. I think honesty, be honest with
yourself about where you are, what you're trying to achieve
(41:39):
and whether or not you're reallymaking making impact on it.
So I guess those are my start, stop and continue.
And, and again, I think more importantly and again, I know
we're competing for market shareout when it comes to consumers.
We're competing for, for, you know, space within that
(42:00):
independent agent. You're also competing for the
next wave of agents, right? Those younger demographics.
You're not going to solve the insurance ownership gap if you
cannot solve the hiring of new younger agents and attracting
(42:20):
them and retain them. They are 1 to 1.
I love that it's such an it's such an important, you know,
ecosystem in the sense of one thing affects the other, yes,
you know, and couldn't be more relevant.
And I just, I love first of all,I love having you on the
podcast. I feel like you would have to do
(42:40):
a little miniseries. About one series.
I'm ready. Has signed me.
Up but you know every time Samantha calls me with a you
know hey Lisa, are you seeing this you know MERS you know what
are you seeing I'm like yes, totally agree with you and I you
know I think to our listeners it's so easy to think that you
(43:02):
can solve the problem in a traditional way.
What we're telling you is as industry veterans and very
passionate people about solving authentically yeah, don't follow
that inertia right. Saying that like the undertow is
going to catch you, that you're not going to ride that way,
you're going to get pulled under.
And we're just trying to say, hey, we just don't want you to
(43:25):
like get pulled under and, and, and get get caught up in that
undertow. And I love your Start.
Stop and continue. If any of our listeners don't
follow you, you do such wonderful world reports,
studies, blogs, writing. I mean, I, I'm obsessed with
your thought leadership and content that you all produce
(43:45):
both yourself and the Cap Geminireport.
So please follow Samantha Chow and all the work they're doing
at Cap Gemini. It's, it's I, I've been lucky
enough to be featured in one, but all of the reports they do
are just phenomenal. And for all of our listeners,
please continue to stay curious,stay informed and stay plugged
in. Thank you so much, Samantha, to
being a guest today. My pleasure.
(44:07):
In today's episode of Insurance Unplugged, the AI and
Distribution series is proudly sponsored by Iris and Suretech,
your gateway to the future of insurance distribution.
Iris harnesses the power of generative AI to transform data
processing and decision making across the distribution
landscape. The Iris platform integrates AI
(44:29):
driven decision engines, dynamicform generation and configurable
workflows, all underpinned by continuous data quality
management. Discover how Iris is powering
smarter operations and more efficient distribution with
cutting edge AI setting a new standard of excellence across
the entire industry.
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