Navigating the Data Race in Financial Services

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You are listening to Leaders in Lending
from Upstart, a podcast dedicated to helping

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consumer lenders grow their programs and improve
their product offerings. Each week, here

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decision makers in the finance industry offer
insights into the future of the lending industry,

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best practices around digital transformation, and
more. Let's get into the show.

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Welcome to another episode of Leaders and
Lending. I'm at snow here with

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Greg and Amyor from Salesforce live at
CBA. Greg Amyer, Welcome. Why

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don't you take a minute to introduce
yourselves talk about what you do as Salesforce.

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Thanks Matt, happy to be here. My name is Greg Blasei.

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I'm a senior director in our financial
services cloud product organization. In that capacity,

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I work directly with customers and with
industry leaders to help make sure that

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our industry verticalized products and point of
view and solutions are headed in the right

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direction and generally trying to stay to
where the puck is going in the industry.

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Awesome a mere magell Essie, industry
advisor at Salesforce. Been here for

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about three years, but it's spent
almost twenty years in financial services and have

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seen the movie before on how you
know, clients are contending with big business

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problems. In my role, I
cover the US in Canada and we really

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try to unearth what the biggest and
brightest problems are for our clients, bring

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practical solutions to them, and figure
out a way that we can leverage on

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the platform in an extensible way.
So looking forward to that conversation great,

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and it's a pleasure being joined with
two reformed bankers. You know, we

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talked with some of the guests about
like accidental careers in banking, but I'd

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love to hear from each to view
like how you got in and out of

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banking to now more on the service
side. Yeah. So I spent the

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first twenty years of my career working
directly in the industry and may have crossed

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paths with a couple of folks sitting
at this table at various points in that

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career. And you know, I
was very fortunate to have a few different

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roles in banking. I started out
my career in retail banking as a teller,

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like a lot of us have,
actually just kind of worked my way

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around the bank. I worked on
corporate banking team and in the commercial space,

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specifically spending a lot of time in
treasury management and had the opportunity to

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do a lot of roles. In
my last five years I was at the

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bank, I actually was the business
owner of our salesforce deployment across the enterprise,

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which is where I got my experience
with the technology and really really liked

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it and thought I'm gonna make a
leap over there. So about seven years

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ago I did just that. Nice. Yeah, not surprised. So Greg

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and I did work together at Huntington
Bank and Columbus and on the retail side,

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and I just remember your passion around
like the use of technology to enable

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the consumer and banker relationships, So
I'm not surprised to see you on this

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side, like helping many other financial
institutions on that journey. Yeah for sure,

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Yeah, no doubt. So I
started in banking in the retail franchise,

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managing branches, regions, larger territories, zig zagging around the organization quite

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a bit, went to strategy,
went to business banking, managed relationship managers,

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and then having the opportunity to actually
go on and run the consumer strategy

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in another large regional and had the
you know, staffing model, incentive plans,

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data analytics the operating budget for the
firm, and had the pleasure of

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implementing the platform as well at both
institutions, in addition to other platforms as

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well. So it's now I saw
the inflection point of you know, technology

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and the need for technology well before
the pandemic arrived. The pandemic obviously accelerated

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some of the need and the passion
for that, and so I thought which

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organization would take a clunky banker like
me and leverage them in their ecosystem.

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But Salesforce was such a great fit
culturally and for us to find ways to

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really walk in the shoes of our
customers, and so that transition was really

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related to how do we actually more
deeply understand our customer needs and how do

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we apply technology to solve those problems. So it was a great match.

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Awesome, great, Well, I
know technology and AI and we'll get to

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those things. Get a lot of
the press. But one of the things

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that is spoken me at this conference
that you guys have hit on, it's

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just the data and the foundational layer
of data and getting that right, getting

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an organized or how do you make
best use of it? Maybe it Leo's

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start with that, like what are
you seeing in terms of the customers you're

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helping, like what's their state of
their data? What are the biggest needs

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or what advice would you have for
banks like struggling with what do I do?

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Like how do I get my data
AI? Ready? Yeah, And

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maybe I'll start where I'm going to
end my response, and that is that,

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you know, AI is the hot
topic of the moment, and it

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should come as no surprise that good
data is good AI and bad data is

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quite the opposite. And so it
has, you know, certainly accelerated some

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conversations and strategies at some financial institutions. Although I would say that folks have

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been on a data journey for a
while. I would sometimes say that almost

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the strive for perfection was almost to
a fault in some cases where it was

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like, oh, I think we
have to have this perfect and good wasn't

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good enough. And that's unfortunate because
I think that those same folks are the

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ones that are finding themselves trying to
play catch up to those that you know,

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we're more forward forward looking. So
I'd say just in general, the

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industry has been on this data cleanse, refresh reorganization and truly understanding the value

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of the data that they hold.
Now in this world that we're in,

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it's not only the data, but
what data? What data has a shelf

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life of usefulness? And how do
I consume it? And how do I

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consume it in massive amounts? How
do I harmonize and unify that data so

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that I've got one understanding of who
Matt is as my customer. And so

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that's really kind of I think where
folks are landing right now or what they're

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trying to figure out because they know
that in order to deliver the promise of

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AI, or deliver on that promise
that we are all hoping it brings to

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the industry that is really critical and
foundational. Yeah. I guess that's a

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good nuance. So you're saying that
you don't have to have your data like

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cleanse and strategy strategy set first before
you leap into AI. You can do

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some of these in parallel. Yeah, I mean, I think, and

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that's exactly what we're seeing as some
folks are kind of flying the plane in

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the air right as they're built.
Or maybe I got that backwards, but

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you know what I'm trying to say, right, build the plane while they're

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flying and that's it. But but
it is, I mean, it is

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something that goes and it's also you
know, you get your data to a

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certain state, and I guarantee you
that's not going to be the final state.

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So it's something that you're always kind
of working with and massaging. We

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talked in the panel discussion yesterday,
you know about even just idea of test

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data and making sure that it is
whole and representative of the you know,

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base that you want to serve and
that you've got the right amount of data.

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And then it's fresh data, right
because these models are learning off of

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that data, and so it's just
really important to even give consideration and out

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that kind of element. I'd love
to build on what Greg is talking about

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though, mat because I think I
feel like it's a race without a finish

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line because the amount of data that's
accumulating on a daily basis and listen and

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financial services, it's a knowledge based
industry, and so it's like, how

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do I now interpret that information as
a business line lead and a meatail owner.

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Formerly, you know, I got
a lot of information a lot of

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times, but I'm not sure I
got a lot of insight around that data,

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So it's not only taking what Greg
said, but putting it into an

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actionable format. So you've got data, you've got a UI that you've got

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to worry about so you can deliver
it to people that are interacting with customers.

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But you have models behind that that. Now, whether those are lms

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that are externally purchased and integrated with
or you're building it on your own.

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So there's an opportunity for us to
now build those things together in a cohesive

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way so that you can actually serve
customers in an intelligent and intelligent man Yeah,

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so what do you think is the
fastest path to those insights our customers

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coming to you with, like the
insights they're striving for, looking to connect

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that to the data or vice versa. I think it's challenging because it's broad

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and it's vast kind of set of
options architecturally or the UI that I was

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referring to, or even the models
that you're going to engage in, and

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then how do I account for the
regulatory landscape that Quite frankly, I'm not

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sure that the cement is quite dry
on how you know, our regulating bodies

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feel about the usage of certain data. So I do think it's a matter

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of taking that a mass amount of
information and thinking about the use cases that

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actually impact people's business imperatives, and
then cobbling together the data set, the

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UI and the model that makes the
most sense for the use case, because

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otherwise it's such a large endeavor for
organizations, they can be crushed by the

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magnitude of the effort and that can
be paralyzing for a lot of organizations.

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So we really try to figure out
what's the broad vision, but then let's

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get down to the use case levels, so that solving real business problem.

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Yeah, that's a good point.
We found that the status quo is often

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the biggest competitor to doing some of
these innovative changes. Yeah. Do you

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find then challenges around compliance privacy as
the data it self is ever changing,

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like customers expectations, the regulators view
like, how do you think about those?

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Well, I think it's again down
to the use case level. So

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if there's something that you know,
a regulatory clot starts, well, then

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the amount of information that you need
and the speed in which and the access

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and who has that I think becomes
paramount. Right. So yeah, I

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think we're seeing that in some of
the early use cases that are emerging,

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it's typically going to be a little
bit lower risk use case one where we

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definitely would still want to keep a
human in the loops so that there is

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that at least guidance of you know, critical thinking human being engaged in the

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process. Gotcha, what other aspects
of AI are you saying introduced into you

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know, the questions your customers are
coming to you with, are maybe some

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of the innovations even within salesforce.
Yeah, so a term if you're not

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familiar with this term, and watch
they'll play this podcast six months or now

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and everybody will know it. But
it's retrieval augmented generation and you will hear

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people talk about it in its acronym
form RAG. And so heavily regulated industries

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like financial services are running to RAG. And what that really means is rather

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than going out and searching these vast
ll ms that you know, may have

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training data that might be you know, months old, to rely on,

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what this is really doing is saying, number one, it's kind of reducing

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compute power that would be needed to
generate some responses, because what it's doing

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is saying, Okay, here's my
scenario here's my situation, here's my customer

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and what I know about them.
Now only go search this universe. And

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it is really a way that I
think regulated industries, you know, like

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financial services, are really going to
be able to adopt generative AI more quickly.

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And you know, it's also in
addition to that, there's like this

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whole auditibility component on it. So
an example might be, you know,

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you call in, You've got a
question about say a wire transfer or something,

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right, and and okay, great, I'm an agent. I just

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need to really quickly, I'll do
a query on that and return it and

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say I'm giving math this response.
And I'm giving math this response because my

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bank says that this is our policy
as it relates to that question. And

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this is our policy because of this
regulation. And so there's this auditibility factor

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that is really critical. And then
that human in the loot component, right,

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so it's even bigger than that.
It's like, okay, great,

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well I've got this response back.
Well, what did the human do?

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Did they execute as I said or
as the tool responded, or did we

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have to kind of nudget in a
different direction because it wasn't quite right.

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Yeah, I'll admit that was a
new term for me, So I did

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search that immediately after you said that
in the session. Do you view that

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as like a completely separate class of
like GPT's or is it a different flavor.

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I'd say it's kind of a subset
of it, right, So,

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like, as we've been kind of
thinking about generatively I just in general,

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not we as in sales sorce,
but is generally as a as a population,

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you know, everybody is very focused
on these lens and it's still it's

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still a part of it that you're
saying, like, only search this part

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of it, right, because I
know my answer is in there somewhere.

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Yeah, right, Yeah, Okay, that's helpful. What else in terms

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of you know, AI driven it
seems like personalization was also a key topic

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for sure. Yeah, you know, And here's the thing, Matt,

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I've been thinking about And you know, I've been gone from the bank for

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seven years, and I remember seven
years ago still talking about personalization, and

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so personalization is certainly interesting, and
a part of personalization is gonna be a

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segmentation strategy and kind of getting down
to this small group. But what I'm

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really excited about with generative AI is
you know, A more current term around

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personalization is kind of category of one, and so I've been using that terminology

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because I think that really resonates with
marketers because at the end of the day,

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we are all different, and we
all have very different needs and finance,

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and although we may have a common
set of tools in the financial services

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space, you know, to solve
some of these challenges, the way I

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might consume something versus a mere versus
you could be totally different based on on

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our scenario. And that's why I
think this category one idea is really really

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important, and it kind of in
my mind is like next level personalization stuff.

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I want to build on that just
for a second, because I think

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historically I see personalization and conversations going
on to generate demand and convert the top

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of the funnel. If you will
have activity an opportunity. I also think

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there's a tremendous opportunity to think about
the service excellence that you're trying to deliver

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for your clients. And so when
I think of lending, if I'm a

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business banker or a commercial officer,
I've got renewals, what's the level of

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intelligence that I have at my disposal
and does the client have the same amount

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of information. If I'm sitting there
in a mortgage situation, how am I

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actually able to personalize the experience in
such a way that it's reflective of their

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financial position, the experience that they
are looking for and expect, and of

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course what the capabilities we can deliver
in that time. So I always feel

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like there's an opportunity to go much
broader than just the top of the funnel.

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Yeah, totally agree, And to
borrow your phrase, I think personalization

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is another race without a finished line
or an end, right. It's a

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negotiation or a dance continually with the
consumer, with the company in terms of

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what's okay for the company to know
and use and leverage in terms of the

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data or information they may have,
consumer expectation and what they're coming with all

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to help facilitate or think about a
recent regulatory change that's happening DoD Frank Section

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ten seventy one. You're going to
collect a lot more information, but you

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can't share that information with every single
person in the ecosystem if you're going to

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do a lending opportunity right now,
because underwriters cannot see the incremental information that's

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being collected on that data expectation.
So I do think that personalizing it for

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even the personas that are inside the
organization is actually a real interesting opportunity for

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us to move faster for funds.
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dot Com slash AI Certification. Thanks, and now back to the show.

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00:15:16.399 --> 00:15:20.440
Interesting. Are there other pieces of
automation you see happening either on like the

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direct to consumer or with bank staff? Anything that helps well, you know,

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I think personally, you know,
my personal experience has been the middle

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and back office that has this heavy
intensive human infrastructure need is where there's ripe

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opportunity. I think there's plenty of
studies that demonstrate there's trillions of dollars an

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annual economic value associated to actually improving
the efficiency. Because it takes a lot

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of human intervention a lot of times
to help deliver a product or service or

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a request from a client, why
don't we augment with digital client excuse me,

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digital colleagues, if you will.
That was sure to help either delegate

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to AI or maybe in the new
wave as we kind of talked about an

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R deep dive, how about the
client being able to delegate to AI and

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then actually having the human in the
loop ensure that is appropriate for that customer.

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So there's a there's this kind of
interesting way that's going on right now.

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Yeah, interesting, and I still
have islands in the stream, like

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in the back of my head.
So thank you for that. You're earlier.

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But if for those of you at
the session, we'll get that joke.

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But the other thing I think about
talking about data again is how do

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you know you know what data is
relevant for what decisions? And you had

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some really interesting ideas around either like
reaching data where it was or thinking about

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and using data, not maybe at
a stored or artificial way, but you

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know going to those sources when you
need it. Like, can you describe

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more of that? Yeah, I
mean in my mind, it's kind of

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like there's you know, data that's
trapped in you know, in enterprise application,

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like a core banking application, right, and we might want to access

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say transaction information or even transition details
in a service context or something like that.

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Right. So that's kind of one
element of data. Then there's you

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know, data that might be in
or provided and integrated via a third party

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data aggregate. Right, who's you
know we're able to consume data in that

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regard, And then there's kind of
like all these other places that data might

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live. And one of the stats
on that very slide that we had the

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Islands in the same joke about was
you know that seventy one percent of companies

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say that I'm sorry, of the
applications at an organization, on average,

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seventy one percent of them are disconnected. That was a proprietary research that our

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team at Mealsoft did with our customer
base, which I think is just really

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astounding and speaks to kind of like
the missed opportunities that we're talking about.

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We don't have a good data strategy. Now. One thing on that slide

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those we were talking about and if
you can visualize our river with data islands

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sitting in it, there was a
there were some examples of big islands in

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there and they had you know,
logos of part nor is it ours on

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there, right, Amazon, Google
Snowflakes as examples, And certainly that is

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part of a data strategy, right, Like I got to get my data

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in order, and they play a
very important part in that. But the

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challenge is then you may have your
data in order, and you may have

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at some place that's useful for data
scientists and marketers potentially to go in in

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mind, but if you're really just
thinking about serving it up in an actual

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way in the context of work or
a banker's day, that's a miss,

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right because it's not like your bankers
are going and logging into snowfake directly.

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Then if they did, they wouldn't
know what to do when they were in

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there, right, They're bankers,
not data scientists. And so I'm just

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this idea of freeing that data up
and then in terms of like the right

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data at the right time. You
know, that's I think a matter of

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well, what's the situation, right, is it a service situation, it

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is a is a sales opportunity?
You know? Is it more just kind

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of routine financial wellness type of activity
that we're doing and what might that do?

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And just kind of having the ability
to put the controls in place or

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levers in place and die. Yeah, to be able to surface that right

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information to the banker or the client
at the right time is incredible. I'll

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just add too that, you know, it feels like it's all types of

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forms of data too, right,
So there's streaming data, whether that's mobile

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or onine engagement, or there's batch
data because maybe sometimes the integration effort is

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a herculean if you will, in
nature, and it doesn't feel like that's

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that's achievable by some clues. So
again, finding a way to say,

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what do I need for what reason
for which persona, and where is this

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going to come from and where can
I use it intelligently? So it's not

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just the kind of traditional sources of
data, but what about digital signals,

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the hand raises that are happening with
your client all throughout their experience and ecosystem.

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Are you able to pick that up, interpret that and actually use it

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in an experience or interaction that's I
think when magic and really gotcha. Do

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you envision a future where either the
platforms are more natively integrated, or there's

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middleware layers that facilitate that discovery fact
store or will it that always be a

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challenge of well, listen, I
think I always describe it as simple but

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not easy, right, So we
talk about it in such simple ways,

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but when you have been part of
an organization that has thousand applications and potential

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various platform aggregating and wrangling that data
can be extraordinarily challenging. But I'll be

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honest and forgive me for the shameless
plug. But I think that's what Salesforce

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is focused on is our data cloud
product that allows for the variety of different

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types of data sources, different data
types, and even in file or assuming

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fileing, batch or streaming types of
data to be able to aggregate that.

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The key though, is being able
to unify so that we know when we

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talk to Matt, it is the
same math that's using the mobile app,

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It's the same math that has an
unsecured loan potentially and the same math that

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has a small business and as an
entrepreneur, and how do we really understand

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the full relationship is that helps us
with relationship pricing, disc management, and

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quite frankly, people are just used
to that in their personal lives and every

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way it's connected, it's social and
quite frankly, when they have their experience

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with many financial institutions, it's the
end tothis of that. Yeah. True.

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And one of the things you mentioned, Greg, I think was around

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like metadata like that is important and
making sure all that connects well and that

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that's like that's kind of the secret
sauce right at the end of the day

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to where we're we're spending our time
and really working. As you know,

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we're fortunate in that we're CRM right
at our our at our roots, and

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that is where customer data is sitting. In this understanding and this it creates

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this opportunity to engage with customers.
So not only do we have that customer

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data, but we have the metadata
about it in our product set. It's

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those for all of those apps that
we have build on that common metadata layer,

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so it's you know, kind of
the data about the data, right

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and understanding what the data elements actually
each represent, and then the ability to

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We've got a couple of different avenues
to bring data in or to engage the

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data where it sits, so in
a totally zero copy kind of way,

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because you know, some data security
folks aren't going to be too excited about

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moving data from one place to another. So great, how can I look

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at it where it is, but
visualize it in the context of my customer

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engagement and with that metadata layer on
top of it. Right, So I've

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got this understanding, then make use
of it in generative AI, and that's

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really where the magic comes to life, and that can unlock the scale.

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Probably like you're talking about getting down
to an audience one, Like you could

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do that at an institution, but
the cost continues to escalate as you break

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down the audience. So yeah,
like, are there metrics because you did

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talk a lot about driving down costs
of acquisition or servicing? Are there other

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metrics you help your customers like understand
their programs or how they're making improvements.

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00:22:48.960 --> 00:22:51.079
How do they know they're Yeah,
it's still on this journey. Yeah,

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00:22:51.079 --> 00:22:53.119
it's gonna it's sort of you know, depends on where they're starting. Right,

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If I'm a marketer, I'm really
going to be concerned about the effectiveness

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of my campaigns, right, my
offers that I'm putting out there, and

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my personalization efforts, Like am I
really getting the return on that that I

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00:23:06.400 --> 00:23:10.039
wanted to do? The pull through? You know, managing something like this

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on platform that gives you visibility not
only to your marketing efforts, but your

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sales efforts and then even farther down
the road loyalty, right, Like,

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00:23:17.839 --> 00:23:23.440
you can have a much better and
straight line view on your effectiveness actually of

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your of your your marketing. So
like that's one example, you know.

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The other would be, you know, something as simple as well as simple,

348
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but something but your your c SAT
scores, right, I mean,

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because I really do think this stuff
and what I'm terribly excited about, Matt,

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and I think it'll be a minute
before we get there. But a

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mirror was kind of alluding to it
is when we when we are able to

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combine AI and automation all in one
where things can kind of just happen.

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I mean, don't you sometimes have
the expectations like, well, they know

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that about me, why didn't they
just do it? Right? Like Why

355
00:23:59.160 --> 00:24:02.680
did we have to make a you
know, conversation back and forth and fourteen

356
00:24:02.759 --> 00:24:06.079
emails just to do something really really
simple? Why didn't they just do it?

357
00:24:06.319 --> 00:24:08.839
And I think we're getting We're really
close. The technology exists today.

358
00:24:10.119 --> 00:24:11.839
You know. The fact of the
matter is we could go out there and

359
00:24:11.839 --> 00:24:14.839
put something like that in place today. It's just there's a little trepidation.

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Yet We've got to get past that
as an industry and begin to really trust

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the tool in order to I think, allow those experiences to get there.

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But that's a great example of c
set where it's just like, yeah,

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my bang me, they really do
take care of me. I do want

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to build on that because I think
one of the other things that I quite

365
00:24:30.640 --> 00:24:33.119
frankly talk a lot about is because
a lot of people coming in and go,

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how do we drive privacy, It's
like, well, that's a lagging

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indicator of sorts. So like,
how do you know that you're implementing technology

368
00:24:41.319 --> 00:24:47.440
and facilitating conversation digitally orienting human or
hybrid scenario, and what are the leading

369
00:24:47.480 --> 00:24:52.039
indicators that your actual technology investments are
transiting into real economic value. So it

370
00:24:52.160 --> 00:24:56.799
might be a level of adoption readership, there might be the time to actually

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00:24:56.839 --> 00:25:02.720
serve up a solution. I think
it marked. I would have very expensive

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00:25:02.799 --> 00:25:08.119
data scientists work on our behalf to
interpret marketing campaign ROI and the responsiveness of

373
00:25:08.200 --> 00:25:12.079
even certain control groups or otherwise.
Deposit growth is a little bit of a

374
00:25:12.119 --> 00:25:17.160
hot topic these days. Sure,
what about the opportunity to expedite what is

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00:25:17.240 --> 00:25:22.720
normally a seven eight nine week turnaround
on insights of a previous campaign. By

376
00:25:22.759 --> 00:25:26.160
the way, I've already set the
budget and the marketing campaign for the following

377
00:25:26.200 --> 00:25:29.599
quarter, but I yet I have
not had the insights already. So think

378
00:25:29.599 --> 00:25:33.240
about the speed and agility and velocity
that you can move around sub segmentation.

379
00:25:33.559 --> 00:25:38.000
When you think about sales productivity,
when you're in retail banking, it's about

380
00:25:38.119 --> 00:25:41.680
leeds, It's about referrals. How
can we reduce the time that it takes

381
00:25:41.680 --> 00:25:45.599
to getting from you to a partner? How do we improve the KPIs around

382
00:25:45.599 --> 00:25:51.480
the conversion of those referrals. These
are still core competencies of a retail bank

383
00:25:51.519 --> 00:25:55.440
today, and quite frankly, people
still struggle with actioning because every day an

384
00:25:55.480 --> 00:26:00.799
hour that goes by that's not actioned
on that referral, a less likelihood of

385
00:26:00.839 --> 00:26:03.519
them buying whatever cent product or service
that's being teed up. So like that's

386
00:26:03.559 --> 00:26:07.680
where I see a lot of the
interaction of and that's why I think I

387
00:26:07.720 --> 00:26:10.880
emphasize use case by use case.
Yeah, because if we think about like

388
00:26:10.960 --> 00:26:17.119
the organizational objectives, if we're being
honest, every organization has very similar financial

389
00:26:17.240 --> 00:26:21.759
ratios that they need to improve when
that's efficiency or net income or cost of

390
00:26:21.880 --> 00:26:25.759
deposits or funding. But like one
of the real levers and drivers that we

391
00:26:25.799 --> 00:26:29.759
can pull on to really go forward
in an outperformance way, if you will.

392
00:26:29.880 --> 00:26:32.599
Yeah, So you definitely hit a
hot topic on deposits. Are you

393
00:26:32.680 --> 00:26:40.000
guys seeing anything outside of price wars, customers who are succeeding in winning deposits,

394
00:26:40.000 --> 00:26:42.920
bringing in new customers, deepening relationships. Where are the success is happening

395
00:26:42.960 --> 00:26:49.440
there? Yeah? I think while
smart intelligent elasticity pricing and MODELNK can help

396
00:26:49.480 --> 00:26:55.200
you understand the right segment that's going
to buy at the right ray and give

397
00:26:55.200 --> 00:26:57.279
you the balance performance that you're looking
for, because that's kind of it's not

398
00:26:57.440 --> 00:27:00.640
deposit growth at all costs, right, right, I think it's like deposit

399
00:27:00.759 --> 00:27:06.880
growth at a prudent ray and what
the funding needs of the organization might look

400
00:27:07.000 --> 00:27:10.720
like. And so I think it's
yes, being smart about the intelligence and

401
00:27:10.759 --> 00:27:14.400
the analytics around how we can interact
with those customers. That's a big winning

402
00:27:14.440 --> 00:27:18.000
recipe. But it's also simple things
like what's the on boarding experience when somebody

403
00:27:18.000 --> 00:27:22.680
actually raises their hand and says yes, Because there's a massive amount of an

404
00:27:22.680 --> 00:27:26.720
abandonment rate when you're digitally engaging or
even in a branch environment, if you

405
00:27:26.759 --> 00:27:32.599
can't fully bring the account to fruition
fund the account and getting used quickly,

406
00:27:33.279 --> 00:27:34.720
that leads to privacy, by the
way, at some point, right,

407
00:27:34.880 --> 00:27:38.519
So I think there's some pretty big
opportunities and we're seeing some clients succeed in

408
00:27:38.559 --> 00:27:42.640
that way. Okay, yeah,
probably one of the benefits of a company

409
00:27:42.680 --> 00:27:45.640
like salesforces you have many other verticals
to learn from. So are you seeing

410
00:27:45.680 --> 00:27:52.000
with your peers and maybe less regulated
spaces, things that you're learning from or

411
00:27:52.079 --> 00:27:59.480
ideas that could potentially catch on in
banking sometime or I this was a really

412
00:27:59.519 --> 00:28:03.759
it's almost like you knew this because
onboarding is a really it is a passion

413
00:28:03.839 --> 00:28:07.200
of mine because I just think that
experience has for far too long been rocky,

414
00:28:07.400 --> 00:28:11.519
and you know, I don't know
about you, Matt Billy, Like,

415
00:28:11.640 --> 00:28:12.960
if we went out, you know, to a lot of banks and

416
00:28:14.000 --> 00:28:15.319
we said, we want to open
a checking account and we want to take

417
00:28:15.319 --> 00:28:19.839
out a loan, and we want
to do so simultaneously. I don't know

418
00:28:19.880 --> 00:28:23.240
what the stat is, but I
can guarantee it's more than not. We're

419
00:28:23.240 --> 00:28:26.319
probably going to have to go down
one path first with a series of questions

420
00:28:26.480 --> 00:28:33.319
that's very product driven, and another
path going down the loan that's very product

421
00:28:33.400 --> 00:28:36.960
driven, and probably sixty percent of
the questions are going to be the same.

422
00:28:37.240 --> 00:28:41.559
Right. And so I say that
because I look to retail, you

423
00:28:41.599 --> 00:28:45.160
know, and think about just how
easy some online retail experiences have gotten in

424
00:28:45.319 --> 00:28:52.319
And so we're learning from our friends
over in that particular industry and what those

425
00:28:52.359 --> 00:28:55.440
experiences might look like. And I
said, this notion of like shopping cart

426
00:28:56.119 --> 00:28:59.400
is really starting to take hold in
some conversations at banks. So how can

427
00:28:59.440 --> 00:29:03.720
I make my experience more Amazon like? Frankly, right, and extreamly,

428
00:29:03.799 --> 00:29:06.880
you already know a lot of the
stuff about me in theory. If I'm

429
00:29:06.920 --> 00:29:10.400
an existing customer, why is it
in just as simple as adding these to

430
00:29:10.440 --> 00:29:14.119
my cart and getting checked. Yeah, it makes sense. I would offer

431
00:29:14.160 --> 00:29:19.359
a couple of very public logos that
we've talked about one Disney, Formula one,

432
00:29:19.960 --> 00:29:26.200
Gucci. They are all there's a
commonality between all these stories around the

433
00:29:26.599 --> 00:29:32.119
ingestion of behavioral activity and digital signals
that their comments are exhibiting be able to

434
00:29:32.160 --> 00:29:37.359
harness that and serve it up in
such a personalized way that it blows people's

435
00:29:37.400 --> 00:29:41.240
socks off that they know exactly what
ride that I want to ride, or

436
00:29:41.240 --> 00:29:45.240
what movies that I've seen. If
I'm a Disney fan, if I met

437
00:29:45.279 --> 00:29:48.799
Formula one, the ecosystem that's built
around Formula one, and the amount of

438
00:29:49.279 --> 00:29:52.799
fanfare that's being built right to it, I think what they have, what

439
00:29:52.920 --> 00:29:57.519
they don't have are some of the
handcuffs. Quite frankly, because they're not

440
00:29:57.599 --> 00:30:02.480
highly regulated industries. Interesting they get
to use that data and probably a more

441
00:30:02.519 --> 00:30:04.640
expansive and a little bit more free
way with tests and learn. Yeah,

442
00:30:04.720 --> 00:30:07.319
Whereas I think, you know,
it's a bit of a cautionary approach and

443
00:30:07.400 --> 00:30:11.920
financial services, and rightfully so.
I mean, we're systemically important to people's

444
00:30:12.039 --> 00:30:17.680
lives and well being in financial services
versus if I don't make it to some

445
00:30:17.720 --> 00:30:21.240
of those other brands, no one's
going to get hurt, right, yeah,

446
00:30:21.359 --> 00:30:25.279
yeah, great, Well I appreciate
both of you taking some time out

447
00:30:25.279 --> 00:30:27.480
of your busy day to do this. Any other closing thoughts you want to

448
00:30:27.559 --> 00:30:33.279
leave with, don't be scared and
get in there now, because guess what,

449
00:30:33.400 --> 00:30:37.160
the world is not waiting for you, and everybody else is talking about

450
00:30:37.160 --> 00:30:38.759
it and doing this. So when
it comes to AI, let's get in

451
00:30:38.799 --> 00:30:44.279
their test, let's learn, let's
you know, talk and have great conversations

452
00:30:44.279 --> 00:30:48.160
because not only is everybody else doing
it that competes against you, but your

453
00:30:48.160 --> 00:30:52.039
customers are going to become accustomed to
it in the space, and so there's

454
00:30:52.079 --> 00:30:55.559
no better time than right now.
Right, I would just offer that,

455
00:30:55.640 --> 00:30:59.559
you know, think about an outcomes
based approach. So again I kind of

456
00:30:59.599 --> 00:31:03.160
parked labeling this point, probably too
much at this point, but the use

457
00:31:03.200 --> 00:31:07.599
case by use case opportunities, what
are the expected outcomes and let's build a

458
00:31:07.640 --> 00:31:11.599
business case that's really relevant to what
you're trying to achieve. And we can

459
00:31:11.640 --> 00:31:15.240
pressure us that with your internal data
and outside of view, but let's build

460
00:31:15.279 --> 00:31:19.759
a business case for change that is
palatable and on whatever time horizon the institutions

461
00:31:19.799 --> 00:31:23.319
looking for find of thing. It's
like jumped into your point brain. Let's

462
00:31:23.400 --> 00:31:27.519
understand what it is that it takes
to be successful. Let's build a financial

463
00:31:27.559 --> 00:31:32.119
model, because you know in financial
services, I opened that the business spase

464
00:31:32.400 --> 00:31:34.960
dost strategies and innovation, so looking
forward to that. Awesome, Well,

465
00:31:36.079 --> 00:31:37.240
Eric, great, thanks so much
for that. Yeah, thank you.

466
00:31:38.039 --> 00:31:42.640
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467
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