July 9, 2025 • 20 mins
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Experian's dramatic evolution from traditional credit bureau to technology innovator takes center stage in this fascinating conversation with Kathleen Peters Chief Innovation Officer who leads Experian's Innovation Lab. She reveals how the 15-year-old lab has become the beating heart of technological advancement at the company, bringing together PhD-level scientists, engineers, and data experts to tackle the financial industry's most challenging problems.
The discussion unveils Experian's surprisingly deep history with artificial intelligence – their teams have been developing machine learning models and neural networks for over a decade, long before generative AI captured public attention. This foundation gave them a significant advantage when implementing cutting-edge solutions like their Experian Assistant, an agentic AI tool that can autonomously complete complex tasks for business users without requiring specialized data science knowledge.
What makes Experian's approach particularly noteworthy is their dual commitment to pushing technological boundaries while maintaining rigorous data protection standards. As Kathleen explains, being responsible stewards of sensitive consumer information is embedded in the company's DNA. Their AI Risk Council and dedicated compliance frameworks ensure innovations remain ethical and unbiased, especially crucial when developing credit risk assessment tools that impact consumers' financial lives.
Looking toward the future, Kathleen shares exciting developments in human-AI collaboration, energy-efficient computing approaches, and even quantum computing research that could revolutionize encryption technology. The conversation challenges the common misconception that established financial companies can't lead in technological innovation – Experian proves legacy organizations can transform themselves into digital pioneers while leveraging their unique data assets and global reach.
Curious about how AI is reshaping financial services or how established companies can successfully navigate digital transformation? This episode provides invaluable insights from one of the companies at the forefront of this revolution. Subscribe now to hear more conversations with technology leaders who are building tomorrow's financial ecosystem.
More at https://linktr.ee/EvanKirstel
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Hey everybody, Fascinating chat today, looking
at a side of Experian that youmay not be familiar with.
Kathleen, how are you?
Speaker 2 (00:09):
I'm doing really well .
It's great to be with you, Evan.
Speaker 1 (00:11):
Well, thanks for joining, really intrigued by the
work you're doing.
And let's start with the bigpicture.
Of course, you were once knownas a credit bureau, but that's
evolved over the years, over thedecade.
Tell us about thattransformation.
How would you describe Experianthese days?
Speaker 2 (00:29):
Yeah, these days, definitely, Experian is a
technology company and ourmission is really to serve both
our clients as well as consumers, to help open new opportunities
and really create financialinclusion for all.
I know we've had a reputationin the past as being a data
(00:51):
company, but certainly over thelast decade plus, we've gone
through a transformation in thatregard where we really are
technology first and it drives alot of what we do in order to
achieve our mission for sure.
Speaker 1 (01:06):
Fantastic and you lead Experience Innovation Lab.
Maybe talk about the lab, whatits role is and what its idea is
for the future?
Speaker 2 (01:16):
Yeah, in my current role I'm really excited to be
able to lead that innovation andstrategy.
I partner very closely with ourchief scientist, and that's Dr
Shanji Zhang, and Shanji leadsthe team of PhD engineers, data
scientists and softwareengineers who comprise our
(01:36):
innovation lab.
So we just recently celebratedthe 15th anniversary of the lab,
and even that has gone througha transformation.
So when Shanji co-founded thelab, we called it Experian Data
Labs and the idea was to bringtogether these PhD level data
(01:57):
scientists from top universitiesto really understand what were
the insights available from thedata that we were collecting and
caretaking.
But then the idea was to ensurethat our clients could feel
(02:17):
comfortable and confident inreaching out to Experian to help
solve some of their trickiestproblems, to Experian to help
solve some of their trickiestproblems.
And so a number of our clients,especially in financial
services industry, would havedata.
They would have some problemsthat they were trying to solve
and said, experian, you've gotdata also, but what could we do
(02:39):
here?
And by bringing the greatestminds together from both sides,
we were able to experiment andresearch and find some really
creative new ways to tacklethose trickiest problems, and
that ended up being insightfuland beneficial to our clients,
adding value to them, and thenultimately also to the consumers
(03:01):
that they serve, as well as theconsumers that we serve
ourselves.
So that's been a real key partof attracting the talent that
has led to technology teams andgroups and focus across our
business and not just in the labtoday.
Speaker 1 (03:18):
Fantastic, and could you share an example or an
anecdote on something that wentfrom idea to making an impact in
the lab?
Speaker 2 (03:26):
Sure, absolutely.
I mean.
A lot of it really started withAI, which is obviously a very
hot topic for all of us thesedays.
At Experian, with the lab, thedata scientists have actually
been working with machinelearning, artificial
intelligence, neural networksfor over a decade, and so some
(03:47):
of the initial work that theydid there led to a lot of the
predictive analytical modelsthat are used in financial
institutions across the worldtoday for predicting credit risk
, risk of fraud, etc.
A lot of that initial workreally helped us to develop a
core expertise in AI to whereit's in nearly everything that
(04:10):
we do today.
But then, if you think a coupleof years back now, when the
world was introduced to thepublicly available generative AI
generative AI wasn't inventedthen.
It was around for some yearsand this is again things that
our lab was experimenting with.
But what really changed acouple years ago was a
(04:31):
democratization of thattechnology.
You no longer needed to have aPhD in data science to be able
to harness the capability ofgenerative AI and we also had
the compute that was able tosupport it now as well.
So when that first becameavailable, we literally had
thousands of employees who werejumping on to chat GPT and to
(04:56):
Claude to try it out and seewhat that could do, and we knew
that we wanted to be able toleverage and harness all of that
curiosity that was reallystarting from the lab and
spilling out across the company,because it was early days,
(05:32):
which is really important to usand it's just been core to our
mission for so long.
So that led to the lab reallygerminating some of the earliest
generative AI projects that wehad, and now we have generative
AI products in production withclients.
So I think that's a greatexample.
(05:54):
Our Experian Assistant, forexample, is one of our B2B tools
.
It's agentic AI and that's onethat really started with
expertise in the innovation laband has made its way to
production use.
That's benefiting our clients.
Speaker 1 (06:11):
Brilliant, wow, so exciting.
Of course, there's a lot ofbuzz around agentic AI, ai
agents in fintech and otherareas.
What's your take on how AIagents will shape financial
services and underwriting orfraud prevention, or all the
areas you're so active in?
Speaker 2 (06:27):
Agentic is really interesting because it takes
what was an interestingcapability of AI and starts to
be able to collect thosecapabilities into autonomous
reasoning multi-step processes.
Autonomous reasoning,multi-step processes we want to
(06:50):
be able to help clients not onlybetter understand how to
interact with their financiallife and I think a lot of
advanced chatbots and naturallanguage are helping people do
that today.
Agents will take the next step.
Agents will help you do it forme, and this can be a direct
consumer or in the case ofsomething like Experian
Assistant, that helps the usersat our clients as well.
(07:12):
So, for example, our ExperianAscend platform is the
technology platform where somany of our capabilities are.
Capabilities are.
We also have our Ascend sandboxthat allows our clients to
experiment, bring their ownanalytics models, create new
ones, and so one of the thingswith Experian Assistant that's
(07:35):
agentic is that at our clients,instead of having to require an
advanced data scientist toexplore in that sandbox, to take
the time to create models, testthem, see if they can be
deployed, our Experian AssistantAgentic AI can do a lot of that
(07:56):
for them.
So, in natural language, even abusiness analyst can explain to
Experian Assistant what theywant to do, and that will launch
a series of activities onbehalf of that user to achieve
what they desire.
So I think that Agentic is soexciting in that regard, because
it allows so many individualsto be able to take advantage of
(08:19):
the capabilities.
Speaker 1 (08:21):
Wow, amazing.
So you know many financialinstitutions fintechs don't have
the deep bench of AI talentthat you have and built.
They don't have certainlyinnovation labs.
So how do you help thempotentially adopt these emerging
technologies in a practical andsecure way?
Speaker 2 (08:39):
Yeah, it's a great question and it's one that we
have been reaching out andpartnering with our clients in
this regard, I find that thefintechs are some of the most
technologically curious.
They're always experimenting,they certainly continue to lead
the way.
We learn from them as well, andwe strive to make that
(09:02):
capability and these uniqueassets that we have.
Experian has a wealth ofclients, not just in financial
services.
I think some people aren't awareof the work that we do with
health data to facilitate, forexample, billing of health
services between payers andpatients and the providers.
(09:22):
So you can imagine we've beentouching all kinds of very
regulated data across a numberof different clients and
verticals for a long time, ofdifferent clients and verticals
for a long time.
That gives us really uniqueinsights that all of our clients
benefit when we can see thesedifferent aspects of consumers'
(09:43):
lives.
So we're helping with not onlyproviding access to anonymized
data, for example, but also thetools so that these fintechs and
our financial services clientscan experiment themselves and be
able to do that more quickly.
I think that enabling thatrapidity of innovation benefits
everyone and it's been receivedvery well by our partners.
Speaker 1 (10:07):
Very cool.
Speaking of data, you'resitting on, I'm guessing,
terabytes, petabytes of consumerdata, probably all of mine and
yours and everyone else here inthe US listening or watching.
How do you strike that balancebetween pushing the envelope
with these new AI tools andservices while staying compliant
with all the data protection,data privacy regulations out
(10:28):
there?
Speaker 2 (10:29):
Yeah, absolutely.
I mean, it was one thing thatfrom the very beginning, as I
mentioned, we had thousands ofpeople employees curious about
using these tools.
The very first thing we knew wehad to do was how do we ensure
that we maintain our practicesaround being good stewards of
this data?
We're heavily regulated bynumerous agencies, not just in
(10:52):
the US, but in all the countrieswhere we operate, and it's
something that we've pridedourselves on our earned
reputation and respect for doingthat well and protecting
consumer and putting consumersat the heart of what we do.
So that was there at the verybeginning.
The AI Risk Council was one ofthe first bodies that we formed
(11:13):
in this regard.
Risk Council was one of thefirst bodies that we formed in
this regard, as well as acompliance body.
And then thinking about theresponsible use of AI.
Even before generative AI, oneof the risks of AI and machine
learning was bias, and we knowthat when we're talking about
things like credit risk, we needto be so careful that we are
(11:36):
making sure that the data isbeing used properly and the
models are being built in a wayto prevent bias that can creep
in when we're using thesetechnologies.
So that's something that's beenpart of our DNA from the
beginning.
It's part of our mission andwho we are, and so, even as we
(11:57):
look to these new technologiesand how we can innovate with
them, that's a culture that webrought alongside right with us,
and so it is something that weremind ourselves of every day,
because it's so critical to howwe operate and how we serve
consumers and clients socritical to how we operate and
(12:17):
how we serve consumers andclients.
Speaker 1 (12:21):
Brilliant, and, of course, ai is in the news every
day.
Top of the fold, as we used tosay about newspapers.
But what are some of themisconceptions,
misunderstandings that you thinkexist in credit or finance or
data management that you read inthe press or the media?
Speaker 2 (12:34):
Yeah, I think that there are perceptions about the
legacy companies or consumerreporting agencies.
Data companies are going to beleft behind by these
technologies, and that'scertainly not true.
I think that we are goodevidence of the contrary of that
.
One of the most importantfactors in the advancement of AI
(12:55):
is going to be data, and we arepart of a collective dialogue
that the public and ourgovernment officials are
certainly involved in.
What are the roles of companies, of data stewards, of data
providers, as we think about howAI and generative AI is
(13:15):
consuming this?
A whole new dialogue around thetraining of the public models.
There's been a lot ofdiscussion, especially with AI
of late and how much energy it'sconsuming, for example.
I think that the natural marketforces are also going to steer
us toward innovation in the mostefficient ways to use AI.
(13:40):
Certainly, as the GPUs and thechips continue to evolve, I
think we'll see more processingcapability cheaper, just as we
have with standard GPUs forcomputers for years.
I think the same will hold true.
We'll find new ways to use morecompute, but at the same time,
because of the costs associatedwith it, I expect to see a lot
(14:03):
of innovation about how weoptimize our training data, how
we optimize the use of thatcompute so that we can make
innovation quicker and takeadvantage of not everyone has
access to the same unlimited,vast resources, so I think
(14:23):
there's a lot of misconceptionthat our energy demand and use
and demand for thesecapabilities will only just
exponentially increase will onlyjust exponentially increase.
Certainly, innovation willdrive that and adoption, but at
the same time I believe we'llsee a lot of innovation around
efficiencies and optimizing theuse of these capabilities.
Speaker 1 (14:45):
Brilliant.
Let's talk a little bit aboutthe lab itself and the culture
of innovation that you'rehelping lead.
How does that look?
What does it look like behindthe scenes and what's working
there?
Give it all of your history andregulatory requirements and
global reach.
How do you manage that?
Speaker 2 (15:08):
We really benefit from a great reputation as
Experian, I think when peoplethink of us, especially data
scientists.
We still have some legacyreputation of having a lot of
data, so that makes any datascientists excited.
But at the same time, I thinkthere's also recognition of the
technology developments and thatfootprint that we have.
(15:29):
It doesn't hurt that we havereally strong leaning in
consumer facing organizations,our direct to consumer
advertising and the tools thatwe use to help consumers, like
Boost here in the US that helpsyou boost your credit score.
That has created an awarenessof who Experian is and the
(15:53):
different parts of lives that wetouch.
And so when we are interviewingcandidates and hearing from
people who want to come work atExperian, they are familiar with
the place that we're playing inthe world and how we might
already be touching their lives.
And so the idea of being able tomake a difference and to be
(16:15):
part of a technology andinnovation group that's so close
to the business, not justtucked away buried in an R&D
center somewhere, has proven tobe really attractive for talent.
We run an organization that'svery open, very communicative.
We allow a hybrid way ofworking so we find the best
(16:39):
talent where they are.
And yet, because we have thesecapabilities and people across
the country in the world,there's usually an Experian
office relatively nearby wherepeople can come together in
person as well.
So I think that ability tocombine the access to data, the
access to consumers directly,the access to the business
(17:03):
leaders who are engaging withour clients, naturally attracts
talent, technology talent notjust to the lab but also to our
businesses, to be a part ofsomething meaningful and to work
with like-minded people andfeel like you're making a real
difference in the world.
Speaker 1 (17:24):
Wonderful.
It's a competitive landscapeout there with big tech
especially.
So, well done.
I used to ask people where doyou see things going in three to
five years, and AI it's morelike three to five days but
short term long term for AI andExperian.
What are you excited about thisyear and maybe next?
Speaker 2 (17:44):
Yeah, one of the things I'm really excited about
is this sort of navigating humanand AI collaboration, and I
like newspapers too, and justearlier this week in the Wall
Street Journal, one of the bigNew York banks was talking about
how they're deploying theseautonomous agents and giving
(18:05):
them email logins and loginssystems, and that every senior
manager will have on their staffthese autonomous agent
assistants.
And so navigating that humanand AI collaboration interaction
.
How do we monitor that?
(18:26):
I really think it's importantto have that human in the loop
when we're working with AI, andthis is an area that I think we
can explore and maybe lead theway in some of the
experimentation around this,especially in the businesses
where we operate so excitedabout that human and AI
(18:47):
interaction, collaboration,risks and how we navigate that.
That's one area.
I already talked about theoptimization and energy use and
how we might think about computegoing forward.
I think another area that isreally interesting is the rise
in quantum computing that isstarting to accelerate.
(19:10):
I'm sure that this new computepower is helping advance the
research at an accelerated pace.
This is an area that ourinnovation labs have been
researching for a while.
Particularly interested in, wehave experts who have spent
their PhD time in this area.
Spent their PhD time in thisarea and being also very
(19:34):
personally passionate aboutfraud detection techniques, I'm
watching this area very closely.
Quantum computing has thepotential to force a rethink in
a lot of the encryptiontechnologies we use today.
Technologies we use today, andso this is an area of great
interest as well.
Speaker 1 (19:57):
Well, I can't wait to dive deeper into some of these
topics in the future, maybe withsome of your experts.
In the meantime, be keeping aneye on all these new use cases
and services that you'll becoming out with Congratulations.
Thanks so much, evan.
Thanks so much for joining.
Thanks everyone for watchingand listening, sharing and be
sure to check out our new TVshow at techimpacttv on now on
Bloomberg and Fox Business.
Thanks very much.
Thanks, kathleen.
Speaker 2 (20:17):
Thank you.
Hey everybody, Fascinating chat today, looking
at a side of Experian that youmay not be familiar with.
Kathleen, how are you?
Speaker 2 (00:09):
I'm doing really well .
It's great to be with you, Evan.
Speaker 1 (00:11):
Well, thanks for joining, really intrigued by the
work you're doing.
And let's start with the bigpicture.
Of course, you were once knownas a credit bureau, but that's
evolved over the years, over thedecade.
Tell us about thattransformation.
How would you describe Experianthese days?
Speaker 2 (00:29):
Yeah, these days, definitely, Experian is a
technology company and ourmission is really to serve both
our clients as well as consumers, to help open new opportunities
and really create financialinclusion for all.
I know we've had a reputationin the past as being a data
(00:51):
company, but certainly over thelast decade plus, we've gone
through a transformation in thatregard where we really are
technology first and it drives alot of what we do in order to
achieve our mission for sure.
Speaker 1 (01:06):
Fantastic and you lead Experience Innovation Lab.
Maybe talk about the lab, whatits role is and what its idea is
for the future?
Speaker 2 (01:16):
Yeah, in my current role I'm really excited to be
able to lead that innovation andstrategy.
I partner very closely with ourchief scientist, and that's Dr
Shanji Zhang, and Shanji leadsthe team of PhD engineers, data
scientists and softwareengineers who comprise our
(01:36):
innovation lab.
So we just recently celebratedthe 15th anniversary of the lab,
and even that has gone througha transformation.
So when Shanji co-founded thelab, we called it Experian Data
Labs and the idea was to bringtogether these PhD level data
(01:57):
scientists from top universitiesto really understand what were
the insights available from thedata that we were collecting and
caretaking.
But then the idea was to ensurethat our clients could feel
(02:17):
comfortable and confident inreaching out to Experian to help
solve some of their trickiestproblems, to Experian to help
solve some of their trickiestproblems.
And so a number of our clients,especially in financial
services industry, would havedata.
They would have some problemsthat they were trying to solve
and said, experian, you've gotdata also, but what could we do
(02:39):
here?
And by bringing the greatestminds together from both sides,
we were able to experiment andresearch and find some really
creative new ways to tacklethose trickiest problems, and
that ended up being insightfuland beneficial to our clients,
adding value to them, and thenultimately also to the consumers
(03:01):
that they serve, as well as theconsumers that we serve
ourselves.
So that's been a real key partof attracting the talent that
has led to technology teams andgroups and focus across our
business and not just in the labtoday.
Speaker 1 (03:18):
Fantastic, and could you share an example or an
anecdote on something that wentfrom idea to making an impact in
the lab?
Speaker 2 (03:26):
Sure, absolutely.
I mean.
A lot of it really started withAI, which is obviously a very
hot topic for all of us thesedays.
At Experian, with the lab, thedata scientists have actually
been working with machinelearning, artificial
intelligence, neural networksfor over a decade, and so some
(03:47):
of the initial work that theydid there led to a lot of the
predictive analytical modelsthat are used in financial
institutions across the worldtoday for predicting credit risk
, risk of fraud, etc.
A lot of that initial workreally helped us to develop a
core expertise in AI to whereit's in nearly everything that
(04:10):
we do today.
But then, if you think a coupleof years back now, when the
world was introduced to thepublicly available generative AI
generative AI wasn't inventedthen.
It was around for some yearsand this is again things that
our lab was experimenting with.
But what really changed acouple years ago was a
(04:31):
democratization of thattechnology.
You no longer needed to have aPhD in data science to be able
to harness the capability ofgenerative AI and we also had
the compute that was able tosupport it now as well.
So when that first becameavailable, we literally had
thousands of employees who werejumping on to chat GPT and to
(04:56):
Claude to try it out and seewhat that could do, and we knew
that we wanted to be able toleverage and harness all of that
curiosity that was reallystarting from the lab and
spilling out across the company,because it was early days,
(05:32):
which is really important to usand it's just been core to our
mission for so long.
So that led to the lab reallygerminating some of the earliest
generative AI projects that wehad, and now we have generative
AI products in production withclients.
So I think that's a greatexample.
(05:54):
Our Experian Assistant, forexample, is one of our B2B tools
.
It's agentic AI and that's onethat really started with
expertise in the innovation laband has made its way to
production use.
That's benefiting our clients.
Speaker 1 (06:11):
Brilliant, wow, so exciting.
Of course, there's a lot ofbuzz around agentic AI, ai
agents in fintech and otherareas.
What's your take on how AIagents will shape financial
services and underwriting orfraud prevention, or all the
areas you're so active in?
Speaker 2 (06:27):
Agentic is really interesting because it takes
what was an interestingcapability of AI and starts to
be able to collect thosecapabilities into autonomous
reasoning multi-step processes.
Autonomous reasoning,multi-step processes we want to
(06:50):
be able to help clients not onlybetter understand how to
interact with their financiallife and I think a lot of
advanced chatbots and naturallanguage are helping people do
that today.
Agents will take the next step.
Agents will help you do it forme, and this can be a direct
consumer or in the case ofsomething like Experian
Assistant, that helps the usersat our clients as well.
(07:12):
So, for example, our ExperianAscend platform is the
technology platform where somany of our capabilities are.
Capabilities are.
We also have our Ascend sandboxthat allows our clients to
experiment, bring their ownanalytics models, create new
ones, and so one of the thingswith Experian Assistant that's
(07:35):
agentic is that at our clients,instead of having to require an
advanced data scientist toexplore in that sandbox, to take
the time to create models, testthem, see if they can be
deployed, our Experian AssistantAgentic AI can do a lot of that
(07:56):
for them.
So, in natural language, even abusiness analyst can explain to
Experian Assistant what theywant to do, and that will launch
a series of activities onbehalf of that user to achieve
what they desire.
So I think that Agentic is soexciting in that regard, because
it allows so many individualsto be able to take advantage of
(08:19):
the capabilities.
Speaker 1 (08:21):
Wow, amazing.
So you know many financialinstitutions fintechs don't have
the deep bench of AI talentthat you have and built.
They don't have certainlyinnovation labs.
So how do you help thempotentially adopt these emerging
technologies in a practical andsecure way?
Speaker 2 (08:39):
Yeah, it's a great question and it's one that we
have been reaching out andpartnering with our clients in
this regard, I find that thefintechs are some of the most
technologically curious.
They're always experimenting,they certainly continue to lead
the way.
We learn from them as well, andwe strive to make that
(09:02):
capability and these uniqueassets that we have.
Experian has a wealth ofclients, not just in financial
services.
I think some people aren't awareof the work that we do with
health data to facilitate, forexample, billing of health
services between payers andpatients and the providers.
(09:22):
So you can imagine we've beentouching all kinds of very
regulated data across a numberof different clients and
verticals for a long time, ofdifferent clients and verticals
for a long time.
That gives us really uniqueinsights that all of our clients
benefit when we can see thesedifferent aspects of consumers'
(09:43):
lives.
So we're helping with not onlyproviding access to anonymized
data, for example, but also thetools so that these fintechs and
our financial services clientscan experiment themselves and be
able to do that more quickly.
I think that enabling thatrapidity of innovation benefits
everyone and it's been receivedvery well by our partners.
Speaker 1 (10:07):
Very cool.
Speaking of data, you'resitting on, I'm guessing,
terabytes, petabytes of consumerdata, probably all of mine and
yours and everyone else here inthe US listening or watching.
How do you strike that balancebetween pushing the envelope
with these new AI tools andservices while staying compliant
with all the data protection,data privacy regulations out
(10:28):
there?
Speaker 2 (10:29):
Yeah, absolutely.
I mean, it was one thing thatfrom the very beginning, as I
mentioned, we had thousands ofpeople employees curious about
using these tools.
The very first thing we knew wehad to do was how do we ensure
that we maintain our practicesaround being good stewards of
this data?
We're heavily regulated bynumerous agencies, not just in
(10:52):
the US, but in all the countrieswhere we operate, and it's
something that we've pridedourselves on our earned
reputation and respect for doingthat well and protecting
consumer and putting consumersat the heart of what we do.
So that was there at the verybeginning.
The AI Risk Council was one ofthe first bodies that we formed
(11:13):
in this regard.
Risk Council was one of thefirst bodies that we formed in
this regard, as well as acompliance body.
And then thinking about theresponsible use of AI.
Even before generative AI, oneof the risks of AI and machine
learning was bias, and we knowthat when we're talking about
things like credit risk, we needto be so careful that we are
(11:36):
making sure that the data isbeing used properly and the
models are being built in a wayto prevent bias that can creep
in when we're using thesetechnologies.
So that's something that's beenpart of our DNA from the
beginning.
It's part of our mission andwho we are, and so, even as we
(11:57):
look to these new technologiesand how we can innovate with
them, that's a culture that webrought alongside right with us,
and so it is something that weremind ourselves of every day,
because it's so critical to howwe operate and how we serve
consumers and clients socritical to how we operate and
(12:17):
how we serve consumers andclients.
Speaker 1 (12:21):
Brilliant, and, of course, ai is in the news every
day.
Top of the fold, as we used tosay about newspapers.
But what are some of themisconceptions,
misunderstandings that you thinkexist in credit or finance or
data management that you read inthe press or the media?
Speaker 2 (12:34):
Yeah, I think that there are perceptions about the
legacy companies or consumerreporting agencies.
Data companies are going to beleft behind by these
technologies, and that'scertainly not true.
I think that we are goodevidence of the contrary of that
.
One of the most importantfactors in the advancement of AI
(12:55):
is going to be data, and we arepart of a collective dialogue
that the public and ourgovernment officials are
certainly involved in.
What are the roles of companies, of data stewards, of data
providers, as we think about howAI and generative AI is
(13:15):
consuming this?
A whole new dialogue around thetraining of the public models.
There's been a lot ofdiscussion, especially with AI
of late and how much energy it'sconsuming, for example.
I think that the natural marketforces are also going to steer
us toward innovation in the mostefficient ways to use AI.
(13:40):
Certainly, as the GPUs and thechips continue to evolve, I
think we'll see more processingcapability cheaper, just as we
have with standard GPUs forcomputers for years.
I think the same will hold true.
We'll find new ways to use morecompute, but at the same time,
because of the costs associatedwith it, I expect to see a lot
(14:03):
of innovation about how weoptimize our training data, how
we optimize the use of thatcompute so that we can make
innovation quicker and takeadvantage of not everyone has
access to the same unlimited,vast resources, so I think
(14:23):
there's a lot of misconceptionthat our energy demand and use
and demand for thesecapabilities will only just
exponentially increase will onlyjust exponentially increase.
Certainly, innovation willdrive that and adoption, but at
the same time I believe we'llsee a lot of innovation around
efficiencies and optimizing theuse of these capabilities.
Speaker 1 (14:45):
Brilliant.
Let's talk a little bit aboutthe lab itself and the culture
of innovation that you'rehelping lead.
How does that look?
What does it look like behindthe scenes and what's working
there?
Give it all of your history andregulatory requirements and
global reach.
How do you manage that?
Speaker 2 (15:08):
We really benefit from a great reputation as
Experian, I think when peoplethink of us, especially data
scientists.
We still have some legacyreputation of having a lot of
data, so that makes any datascientists excited.
But at the same time, I thinkthere's also recognition of the
technology developments and thatfootprint that we have.
(15:29):
It doesn't hurt that we havereally strong leaning in
consumer facing organizations,our direct to consumer
advertising and the tools thatwe use to help consumers, like
Boost here in the US that helpsyou boost your credit score.
That has created an awarenessof who Experian is and the
(15:53):
different parts of lives that wetouch.
And so when we are interviewingcandidates and hearing from
people who want to come work atExperian, they are familiar with
the place that we're playing inthe world and how we might
already be touching their lives.
And so the idea of being able tomake a difference and to be
(16:15):
part of a technology andinnovation group that's so close
to the business, not justtucked away buried in an R&D
center somewhere, has proven tobe really attractive for talent.
We run an organization that'svery open, very communicative.
We allow a hybrid way ofworking so we find the best
(16:39):
talent where they are.
And yet, because we have thesecapabilities and people across
the country in the world,there's usually an Experian
office relatively nearby wherepeople can come together in
person as well.
So I think that ability tocombine the access to data, the
access to consumers directly,the access to the business
(17:03):
leaders who are engaging withour clients, naturally attracts
talent, technology talent notjust to the lab but also to our
businesses, to be a part ofsomething meaningful and to work
with like-minded people andfeel like you're making a real
difference in the world.
Speaker 1 (17:24):
Wonderful.
It's a competitive landscapeout there with big tech
especially.
So, well done.
I used to ask people where doyou see things going in three to
five years, and AI it's morelike three to five days but
short term long term for AI andExperian.
What are you excited about thisyear and maybe next?
Speaker 2 (17:44):
Yeah, one of the things I'm really excited about
is this sort of navigating humanand AI collaboration, and I
like newspapers too, and justearlier this week in the Wall
Street Journal, one of the bigNew York banks was talking about
how they're deploying theseautonomous agents and giving
(18:05):
them email logins and loginssystems, and that every senior
manager will have on their staffthese autonomous agent
assistants.
And so navigating that humanand AI collaboration interaction
.
How do we monitor that?
(18:26):
I really think it's importantto have that human in the loop
when we're working with AI, andthis is an area that I think we
can explore and maybe lead theway in some of the
experimentation around this,especially in the businesses
where we operate so excitedabout that human and AI
(18:47):
interaction, collaboration,risks and how we navigate that.
That's one area.
I already talked about theoptimization and energy use and
how we might think about computegoing forward.
I think another area that isreally interesting is the rise
in quantum computing that isstarting to accelerate.
(19:10):
I'm sure that this new computepower is helping advance the
research at an accelerated pace.
This is an area that ourinnovation labs have been
researching for a while.
Particularly interested in, wehave experts who have spent
their PhD time in this area.
Spent their PhD time in thisarea and being also very
(19:34):
personally passionate aboutfraud detection techniques, I'm
watching this area very closely.
Quantum computing has thepotential to force a rethink in
a lot of the encryptiontechnologies we use today.
Technologies we use today, andso this is an area of great
interest as well.
Speaker 1 (19:57):
Well, I can't wait to dive deeper into some of these
topics in the future, maybe withsome of your experts.
In the meantime, be keeping aneye on all these new use cases
and services that you'll becoming out with Congratulations.
Thanks so much, evan.
Thanks so much for joining.
Thanks everyone for watchingand listening, sharing and be
sure to check out our new TVshow at techimpacttv on now on
Bloomberg and Fox Business.
Thanks very much.
Thanks, kathleen.
Speaker 2 (20:17):
Thank you.
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