Latest / King Dems Podcast | Growth Mindset, Leadership, AI and Business Insights / Is AI Existential for Banking? The Evolution of Fintech Innovation with Ron Shevlin

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Is AI Existential for Banking? The Evolution of Fintech Innovation with Ron Shevlin · King Dems Podcast | Growth Mindset, Leadership, AI and Business Insights

0:00
I think a lot of banks and credit unions are just not
0:02
thinking about correctly when they think about AI as think, oh
0:06
gosh, you know, we're just going to plug this stuff in and it's
0:08
going to create huge capabilities for us.
0:10
Well, and it doesn't. And the reason it doesn't is
0:13
because quite simply, their data sucks.
0:16
They can't access the data that they want.
0:18
It's sitting all over the place. There's no common definition.
0:21
The systems and applications don't agree on the actual
0:25
balances. Working with a bank at a mode
0:28
that has three systems that think you're at any point in
0:30
time your account balance is at 3 different levels because of
0:34
the way they're updated and because of the definitions and
0:36
things like that. So there's huge issues around
0:39
that. Open banking in and of itself
0:42
does not solve any of those problems.
0:45
It just forces people, forces the institutions to move data
0:50
around, does not take care of the problem.
0:53
You don't take care of the problem, you don't reap the
0:55
benefits of the techno, the AI technologies, unless you are
0:59
improving the quality of the data.
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Ladies and gentlemen, once in Welcome to the Kingdoms podcast,
1:49
in the Diary of a CFO, and today I have to be a very, very
1:54
special guest fighting out of Boston, MA.
1:58
Big welcome, Rod Shamlin. How are you today, Rod?
2:03
Great, Adam. Oh, thanks a lot for having me
2:05
on. OK.
2:06
Thank you very much for sharing your time today with us on the
2:09
podcast. Today, we welcome one of the
2:11
most influential thinkers shape in the global Fintech
2:15
conversation. Ron Scheblin is Chief Research
2:18
Officer at Cornerstone Advisors and recently named 1 of Fintech
2:23
Magazine's Top 10 Global Fintech Influencers.
2:28
For more than two decades, he has helped financial
2:30
institutions and fintech innovators separate not signal
2:34
from noise using disciplined research and strategic clarity.
2:39
On this show last year, we hosted Brett King where we
2:42
explored AI era leadership, digital scale and the structural
2:46
sheets away from branch economics.
2:49
The conversation examined the vision to dig go deeper into the
2:53
data, the execution at the economic consequences.
2:56
Ron Shevlin welcome once again to the Kingdoms Podcast.
2:59
Great to be here. Let's start with the vision, you
3:02
know, breakings episode actually exposed AI era leadership and
3:07
digital skill. And you know, today we're going
3:09
into deep dive into the data that is the execution and the
3:13
economic consequences. So when it comes to branch
3:17
economics and banking, right, Brett argued that branch
3:21
economics are structurally broken because digital skill is
3:25
fundamentally more efficient. From your research, is that
3:28
economic conclusion inevitable across all markets?
3:32
Yeah, I wouldn't argue with Brad on this one, Adabola, but I I
3:36
would take it a little bit different tact in when I try to
3:39
convince banks that they need to stop investing so much money in
3:42
the physical branches. And let's take a step back and
3:45
ask the question, why do we even have branches?
3:49
And the reason is because if you go back 60-70 years, at least in
3:53
the United States, people moved there was a a population shift
3:58
back in Emo even more than 70 years ago at this point.
4:00
Got to go back 80 years or so, post World War 2, when people
4:04
started moving out of the cities and into the suburbs.
4:07
And when a lot of the population was congregated in the cities,
4:11
you could reasonably expect them to come down to the brand, to
4:14
the bank headquarters to do their banking.
4:17
But as people moved out to the suburbs, somebody in some bank
4:21
somewhere had this great idea, hey, let's put a building near
4:25
where the people actually live and make it more convenient for
4:28
them. And so it was really all about
4:30
convenience, the idea of putting physical locations out near
4:35
where consumers lived. It isn't simply because people
4:39
want to talk to people at a moment.
4:42
We're having a great conversation here.
4:44
You're thousands of miles away, and yet I can see you and you
4:49
can see me. We don't need to be in the same
4:52
room together. And the problem with the not
4:55
just the branch economics, but the problem with the branch
4:58
concept is that, yeah, people do like to talk to people, but we
5:03
don't have to physically be in the the same space to do that.
5:06
And so look, I don't, I advocate tearing down or or closing
5:11
branches. What I advocate is that if
5:13
you've got a dollar to invest in capabilities, that dollar is far
5:18
better spent creating some digital capability, which is
5:21
going to create better convenience, better
5:24
capabilities, better delivery, better products and services
5:28
then you're going to offer than by building out your branches.
5:32
What has amazed me for 10/15 years now is this branch of the
5:37
future concept that some banks and and credit unions like to
5:40
put out that there's all this technology in the branch Edible.
5:44
This is ridiculous thinking. Why would you build out
5:47
technologies in the branch for technologies that people can use
5:52
sitting at their desk or in their living room or at their
5:55
kitchen table? So I think Brett has right where
5:58
he talks about the branch economics, but it isn't about
6:01
closing down the branch. It is about where do you spend
6:04
spend the next dollar of investment and I find it really
6:08
hard to believe that for the vast majority of banks that
6:12
spending that money in the branch is a good use of money.
6:16
You know, overtime, the cost benefit analysis for ranch
6:20
economics has been swinging in a negative direction.
6:25
As you know, digital scaling across the industry is
6:29
happening. But you know, Bread also
6:31
highlighted that top fintech platforms now serve more
6:35
customers collectively than top incumbent banks.
6:38
So does the data support this claim that fintech has already
6:42
won or is that a narrative that is premature?
6:46
Well. I think we make a little too
6:48
much of the fintech economics. If you take a a company of
6:52
fintech like Chime for example, yeah, it's got 1015 million, 20
6:56
million customers, whatever it's up to these days.
6:59
But I think it's important to recognise that while their
7:02
delivery costs are lower because they don't have branches, their
7:07
marketing costs are astronomical.
7:10
The cost it it that they the investment they have to make in
7:13
creating a brand. A brand name is brand name
7:16
driving awareness, driving affinity.
7:18
They are spending an incredible amount of money compared to what
7:22
the average bank has to spend. Now what's interesting is that
7:26
from a local banking perspective, simply having a
7:30
branch in town actually creates some awareness and creates some
7:34
affinity because it makes people think, oh, here's a bank that's
7:37
committed to our town, our community, They're here and
7:40
they're advertising. Tell you a quick story.
7:42
A couple years ago, I was at ACEO Roundtable for a community
7:46
banks and the CEO's were talking about their branch strategies.
7:50
And one of the CEO's, it was from a bank in a relatively
7:54
small bank in Texas. And he says, yeah, I'll tell you
7:57
what my branch strategy is. He's got that Southern accent.
8:00
He goes, my branch strategy is Basil.
8:02
And everybody's looking at him like Basil AM.
8:05
I What is Basil? What that means they go, it's an
8:08
acronym BASTL, Big ass sign, tiny lobby.
8:11
And his point was with a big sign and a tiny lobby, he didn't
8:15
need a lot of physical space. He needed to say, here we're in
8:19
town and that might be perhaps one of the better benefits of
8:23
having a local presence is that it is a marketing.
8:26
And so without physical presence, the fintechs have to
8:30
spend a lot to generate name awareness and affinity.
8:33
And so well their delivery economics and operational
8:36
economics might be superior because they don't have the
8:40
branch they are have to outspend on marketing.
8:43
The other thing they have to do Adamola, which I think sometimes
8:46
gets overlooked, is while the era customer support costs have
8:50
to be really, really good. I mean, they're operational
8:53
capabilities have to be really good because fintech customers
8:57
can't just take a drive or walk down the street and go to the
9:00
branch and talk to somebody. And so when you've got phone
9:04
trees and that prevent people from talking to people and you
9:07
try to keep them and say, oh, you can do this on the website.
9:11
Hey, fintech customers know you can do it on the website.
9:14
They signed up and have been interacting on the website or
9:16
the mobile app since the day they started.
9:18
When they've got a problem, they need to talk to some embody and
9:22
so the the call centre contact centre costs are are I think
9:27
higher for the Fintech than it is for a branch, but for a bank
9:32
with branches, but again that's a departmental thing.
9:34
So I think we make a little too much of the Fintech operational
9:38
cost benefits because they do have to spend a lot more on
9:41
marketing to to create the branding.
9:45
It's quite a delicate situation, handling the customer
9:48
satisfaction. It is very frustrating and it's
9:51
interesting. It's something that came up in
9:53
the last conversation on this podcast that sometimes you call
9:57
an institution and an AI part is responding to you and you're
10:02
like, can I just speak to a human, please?
10:04
It's a delicate situation. So I think that makes some some
10:08
people very sceptical about doing big banking with the
10:15
fintech giants. But I think how do we achieve a
10:19
position of trust in society for the fintech operators?
10:24
Well, here's another one where I think people are looking at it
10:28
the wrong way. Trust isn't developed just
10:30
simply because you can talk to somebody or you trust somebody,
10:35
a organisation, and I don't know, it doesn't even have to be
10:37
a fintech or infinite financial services.
10:40
Any provider that delivers and provides a good product or
10:45
service that's reliable and it works and it gets the job done,
10:50
people will start to trust that provider of the pricing aspect
10:54
becomes an important aspect as well.
10:56
There's a value equation and that's a very subjective thing
11:00
in people's heads. Am I paying the right amount?
11:03
Am I getting the value for what I'm I'm getting?
11:07
And what's interesting in banking is that thanks to the
11:11
old days of free checking accounts, you know, a lot of
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people said, well, this is a free product or service.
11:18
It, it allows me to do things and OK for free, it's great.
11:21
But once banks started charging 5-6 dollars a month, people
11:26
started thinking, you know what, this value equation doesn't work
11:29
for me. And as a result, a lot of
11:31
fintechs swooped in and said, well, we have lower fees or no
11:34
fees and we won't charge you $35 for an overdraft.
11:39
In fact, we'll see you $200 and basically, basically they were
11:42
giving away money. Now that might not be profitable
11:44
in the long run, but as a strategy for attracting
11:48
customers, it worked. And so the idea that well, the
11:51
only way to develop trust is people talking to people is
11:54
absolutely wrong. It's if you are delivering and
11:57
providing a reliable product or service for a price that
12:02
consumers the OR the user sees as value added, that's driving a
12:07
lot of trust. And I think that's an advantage
12:10
that fintechs have had is that for the segment of consumers
12:13
they're going after, they're creating a lot of trust because
12:16
there's that value equation and the product or service delivers.
12:20
Now, if there's a service issue and I can't talk to somebody and
12:24
I can't get the product, service or problem resolved, that's when
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the trust breaks down. Interesting.
12:32
So let's talk about the AI ambition gap.
12:35
The AI era leadership was a core theme in that discussion.
12:40
And in your view, what is the biggest gap between executive AI
12:45
ambition and operational AI capability?
12:48
Well, this is all over the map animal and we've got a, we've
12:51
got a terminology problem here. I've said this many times in the
12:55
last year and a half or so, and my colleagues are actually
12:58
getting really sick and tired of me saying this, but I'm going to
13:00
say it again one more time here. We got to stop using the term
13:04
AI. It's an umbrella term that
13:06
relates to a lot of different technologies under the manner of
13:10
AI. There's machine learning,
13:12
there's conversational AI, there's generative AI, there's
13:15
agentic AI, there's computer visioning, there's all these
13:18
other things. And it's really the the 1st 4:00
13:21
so that I met machine learning, conversational AI, generative AI
13:24
and agentic AI that are really having the biggest impact on
13:28
banking right now. It's important to recognise that
13:31
machine learning's been around for a long time, 40-50 years
13:35
we've had machine learning. The problem of the challenge
13:39
we've had is that we really haven't had the technology
13:41
infrastructure to really support the scaling of machine learning,
13:46
meaning it wasn't until we had PCs sitting on people's desks.
13:50
And by the way, I'm sure that you and most of your listeners
13:54
don't even remember back when PCs, personal computer that is,
13:58
started hitting people's desks. You know, there was no Internet
14:01
back then. In fact, they weren't even tied
14:03
together. They weren't even local area
14:05
networks when PCs started hitting people's desks.
14:08
So it wasn't that ability to kind of collect a mass and share
14:13
data. It wasn't until the Internet
14:14
came that help, but then we needed mobile banking and then
14:17
we needed the the cloud computing.
14:20
So we've gone through this evolution of capabilities that
14:23
have finally now allowed us to really take advantage of machine
14:26
learning. But then along comes
14:28
conversation away, which helps us understand computer human
14:32
language and to understand it and reply to it whether it's
14:35
typed or or verbal. And then that got integrated
14:38
with generative AI capabilities that were powered by large
14:41
language models that never existed before because we didn't
14:45
have that infrastructure. And now the agentic AI sits on
14:48
top of those large language models as well.
14:51
So this idea of a gap is totally understandable.
14:55
Listen, when personal computers first hit people's desks, there
15:00
were no PC based enterprise applications.
15:03
It took 10 to 1520 years for those applications to move off
15:07
of mainframes onto the PCs. And it's the same thing with AI.
15:11
We have these. The mainframes.
15:13
Now, but the, the, the initial uses for these are going to be
15:18
personal AI uses, just like it was with PCs.
15:22
When PCs hit people's desks, what, what did it give us?
15:25
It gave us word processing, spreadsheets, PowerPoint,
15:29
e-mail, calendaring. They were personal Productivity
15:32
Tools. And that's what's happening now
15:34
with all these artificial intelligence based whether it's,
15:38
and it isn't really machine learning because that's more for
15:40
modelling and prediction. But the conversational AI, the
15:44
generative AI now the emerging agentic AI, the early
15:48
applications for this are all going to be personal
15:52
productivity enhancers, not enterprise type things.
15:55
That's going to take a few years to get there.
15:58
So I don't know why people are so hung up on this gap.
16:01
It's totally predictable. It takes a few years.
16:04
It will be happen a lot faster now that we have all this
16:07
infrastructure built and what we didn't have back in the mid to
16:11
early 80s, nineteen 80s. So that gaps, if that really is
16:15
a gap, is closing very rapidly. The banks that you observe
16:19
today, are they transforming strategically, right And what is
16:24
the strategy about having modernising that legacy
16:27
infrastructure and just calling it transformation?
16:29
Yeah, these are just labels that managers and executives love to
16:33
just slap on these things, transformation this and
16:36
automation that. You know, you go back to when
16:39
computers and even mainframes, nobody talked about Cobalt
16:43
transformation or Fortran transformation.
16:47
They weren't these buzzwords that that existed back then.
16:50
It's the same thing now that the tools and the AI technologies
16:55
are simply technologies that enable something to get done and
16:58
without a vision for how are you competing and on what are you,
17:05
what are you selling and to who, and how do you ensure that what
17:09
you're selling is the right product or service to meet those
17:12
customers needs and making it more efficient, effective,
17:15
convenient, value added. The technology is just an
17:18
enabler and putting the the technology ahead of the business
17:22
strategy is a huge mistake. The idea that, yeah, we're,
17:26
we're going to be an AI native bank, who cares?
17:29
Customer doesn't care. The product might suck.
17:32
If the product sucks, what good is being AI native and an
17:36
animal? This is, this is too much.
17:37
There are too many, too many damn influencers out there
17:40
trying to go, oh, we need to transform around AI and there's
17:44
going to be an AI transformation.
17:46
It's like it's all nonsense buzzwords.
17:48
They're just, we're just looking for attention on LinkedIn.
17:51
Too many buzzwords out there actually.
17:53
But you know, let's talk about the behavioural banking and
17:56
financial Wellness. So Brett King on his own
18:00
episode, he talked about moving, how it was designed to help
18:03
customers save through behavioural nudges and real time
18:06
spending feedback. Why have so few banks fully
18:09
embraced behavioural financial design despite clear evidence it
18:13
drives better outcomes? Yeah, well a lot of that's
18:16
cultural and and history. The idea if you've spent twenty,
18:20
30-40 years in in any job, it doesn't even have to be
18:23
necessarily within a bank. But if you've worked in a bank
18:26
twenty, 30-40 years, you start to look at the world from a very
18:30
internal perspective. How are we organised?
18:33
What are we good at building and delivering and not taking
18:36
necessarily an outside in view. And if you're a big bank, it's
18:39
really hard to historically been very hard to serve niches of the
18:45
market because you're a huge organisation, you're spending a
18:49
lot of money on technology and people and delivery
18:52
capabilities. And to customise and personalise
18:56
every product or service for every niche segment in the
18:59
market has historically become an incredibly expensive
19:03
proposition. And so the idea of how do you
19:06
develop products or how do you design products from a big bank
19:10
perspective has always been. So what's the common denominator
19:13
of features and functionality that we could package and put
19:16
together that will reach the mass market?
19:19
The behavioural approach that Brett has always advocated for
19:23
is driven more by a customer segmentation type view that
19:27
says, OK, let's identify the niches in the market and
19:30
understand what their individual needs and wants are.
19:34
And, and from a behaviour, your perspective to how do they like
19:37
to interact? What channels do they like to
19:40
use? How do they prefer to use
19:42
technology or not? And so the behavioural aspect is
19:46
really a segmentation type approach.
19:49
But you know, I, I don't think that's the end all be all
19:52
either. Sorry, Brett.
19:53
And I know he'll agree with me on this actually that the
19:55
behavioural piece is a is a great view to take, but you've
19:58
really got to look at it not just from the behavioural
20:01
perspective, but from the needs slash wants perspective, which
20:05
is. You know, how does a particular
20:07
segment of the population need a differentiated type of product?
20:12
Let me give you an example of that.
20:14
In the US, we've got 10s of millions of gig workers, folks
20:18
who really don't have what the old traditional full time nine
20:21
to five job is. They don't have a single
20:24
employer. They work for multiple employers
20:27
or more on projects or gigs, and they have very unique financial
20:32
needs because they don't have a regular paycheck coming in.
20:35
They have very unpredictable cash flow. 2nd, and very
20:39
importantly, is they're really kind of like small businesses in
20:43
that they have to invoice for their services.
20:46
They have to account for these things.
20:48
They're working for multiple employers even though they're
20:51
not really traditional employees.
20:53
And so they have a lot of needs. And the traditional bank account
20:57
offered by most banks, at least in the US, don't serve the needs
21:01
of those gig workers. So it's great if you understand
21:04
what they want from a behavioural perspective.
21:07
But banks need to start looking at these segments from a product
21:12
need perspective, not just a behavioural perspective.
21:16
All this AI stuff, Adam, all, it may help in actually creating
21:20
and delivering the product, but it's got to start with more of a
21:23
vision and understanding of the segmentation in the market and
21:27
understanding how these things, how they're, those needs differ.
21:31
And that's not something that AI is necessarily going to just do
21:35
by itself. It could help from a product
21:37
design perspective, but you've got to start with some human
21:40
intelligence first. I appreciate that and I think
21:43
that, you know, AI can actually help accelerate a product market
21:47
feet for these financial institutions, you know on
21:52
talking about AI agents as money coaches.
21:54
If AI agents become integrated money coaches embedded in daily
21:58
life, what happens to traditional revenue models built
22:02
on overdrafts and credit margins?
22:04
Well, so a couple of things. First of all, I think we
22:08
overestimate AI agents ability to just kind of take over the
22:12
management of our financial lives.
22:13
And the reason is, is that sure agents can do things, but they
22:18
do what what we tell them to do. And so in their be some some
22:23
capability that says, OK, agent go off and open an account with
22:27
the best provider in the market. Well, now that begs the question
22:31
out of Mola, who's determining who the best provider in the
22:35
market is? Now, we've gone through a period
22:37
here, 1520 years where companies like Google have tried to create
22:42
product selector tools that say, OK, we're going to capture all
22:46
of the attributes of a particular product and list
22:49
them. And you know, what are they?
22:50
Prices, fees, interest rates? You might be looking at some
22:54
digital banking capabilities to make that decision.
22:57
But the way we actually decide incorporating things like, well,
23:01
where are the nearest branches? Have I had an interaction with
23:04
them? What do my friends think about
23:06
this provider? We have family members who've
23:09
worked with this provider before.
23:11
The way we actually make decisions is pretty complex, and
23:15
I'm not convinced that just because you have an AI tool that
23:19
it is necessarily helping you to make a better decision.
23:23
You can go on to bankrate.com and find plenty of providers, at
23:27
least here in the USI don't know where what which you've got out
23:30
in South Africa. There are other parts of the
23:32
world, the US, we've got bankrate.com, Tom, we can go on
23:35
there and find the provider with the lowest fees or the highest
23:39
interest rates on a checking account or the lowest rates for
23:43
a loan or a mortgage. But it's that's never, almost
23:46
never the only criteria that somebody uses to make a
23:50
decision. Now, some AI tool or agent might
23:53
be able to go out and get more information, but MO, what do you
23:57
do when when the the agent says, OK, we're going to move 100,000
24:02
of your dollars from bank A to bank B?
24:04
Oh, and by the way, it ignored the fact that there's a pricing
24:07
stipulation that says if your balance goes below $100,000,
24:12
you're going to get hit with a $5 a month fee.
24:14
Now, maybe the AI tools can, can capture that, but the idea that
24:18
there's going to be all this kind of money moving around and,
24:21
and the idea, So what happens next week when somebody else
24:25
comes out with a better rate or a better fee?
24:27
Does your AI tools move the money again?
24:31
So I think we're kind of over thinking the real impact of a
24:36
lot of this stuff because it's just not the way people really
24:39
want their how to manage, manage their money.
24:42
They want to feel like, OK, I got my money in the right place
24:45
and it's doing the right things for me.
24:46
And if something comes up and I got a problem, I might not even
24:50
know who to call because I can't keep track of how my AI agent is
24:55
moving the money around. And I don't think a lot of the
24:57
folks who are pushing a lot of this stuff are really thinking
25:00
through the details on this. Oh, look at all these great
25:03
capabilities and we're going to do this and AI agents are going
25:06
to do all of this. Now, here's the other question
25:09
that that's really important here.
25:11
Adam Mullen. OK, let's say there is this
25:13
amazing AI agent capability. Where are you going to get that
25:17
AI agent from? Who's going to provide that?
25:21
Google, Microsoft, Amazon, Do you trust them enough to do
25:25
that? They're going.
25:26
To move to. AI, whoever's paying them the
25:28
most to do it. So it's more likely in my eyes
25:32
that you're going to get that AI agent from the financial
25:36
institution or fintech that you trust the most and like the
25:40
most. This is why I had a conversation
25:42
with Klarna CEO about two years ago at this point when they
25:45
started announcing all their AI customer service capabilities.
25:49
And I was like, OK, this is this is not the story.
25:52
They were touting that AI was going to do the work of 700
25:56
customer service agents. And I looked at that and said,
25:59
something's wrong here because I know Clara doesn't even have 700
26:02
customer service agents because it outsources its customer
26:06
service to a third party. And Clara wouldn't have the
26:09
slightest idea how many customer service agents that company has.
26:13
So I had a chance to talk to the CEO and said, OK, this is not
26:17
the story. What's really behind the story
26:19
of your AI use? And he said, we envision
26:22
becoming the the provider of services to directly to the
26:26
consumer to help them make the best choice of product and
26:30
provider, which I thought was very interesting.
26:32
And that's where AI agents could come in.
26:34
So you see even companies like Klarna want to be the provider
26:38
of those agents. So I think that at least in the
26:42
short run at meaning five years, maybe even 10 years out, the
26:46
idea that AI agents are going to come in and run everybody's
26:49
financial life, shake things up and put financial institutions
26:52
out of business is way oversold. You know, go back to the late
26:56
90s when Wingspan Bank came out and the online banks started
27:00
appearing. Everyone's like, oh, these are
27:02
going to put the banks in out of business.
27:04
And look who's left standing out of all.
27:07
It's JP Morgan Chase, Bank of America, Wells Fargo all still
27:11
there, little tiny digital Internet banks not still there.
27:16
So, I mean, look, five years from now, I could be really
27:20
wrong on this one, but history says the incumbents don't get
27:24
tossed out as easily as as you think.
27:26
Absolutely, and delving into financial Wellness, return on
27:30
investment from your research, does financial Wellness
27:34
generally increase long term profitability for institutions
27:38
or is that still more theory than measurable practise?
27:41
So we've got a terminology technology problem again,
27:45
because what is financial Wellness here?
27:48
A lot of that in terms of financial health, financial
27:50
Wellness, financial education, financial literacy, those are
27:54
all very worthy goals. But I would argue, Adam, all
27:57
that what most people want is better financial performance,
28:02
Meaning I want my money to return more.
28:05
I want it there when I need it. I want the interest rates that
28:08
I'm earning to be as high as possible.
28:10
I want the return on my investments to be as high as
28:14
possible, I want to pay the least amount of fees for the
28:17
services I get, and I honestly don't care if I'm educated or
28:22
literacy. It's like driving a car.
28:24
I have a pretty good driving record.
28:26
Knock on wood. I'm a fairly safe driver.
28:29
I don't have. I haven't had a ticket in a long
28:31
time, haven't had an accident in a long time, knock on wood.
28:34
And I don't know the first thing about the insides of the car.
28:37
I know too get an oil change, I know to keep gassing it, I know
28:41
to do a couple of things. But I don't have to be car
28:44
literate in order to be a good driver.
28:48
And I think it's the same thing with managing your money.
28:51
You don't need to have the highest level of literacy as
28:54
long as you have some good behaviours and and good tools
28:58
and technologies to help you do that.
29:00
And I think what's happened in the industry is that the terms
29:04
Wellness, literacy, financial health tend to apply to the
29:10
lower to middle income type of consumer.
29:13
But it's really more of a spectrum.
29:15
Listen, but even even the person making a quarter of $1,000,000 a
29:19
year could have be in a worse financial literacy or Wellness
29:24
or health space or level than somebody making a fifth of that
29:27
if there's good money management capabilities and tools that
29:31
apply to them. So I try to get financial
29:33
institutions and even the fintechs to think more in terms
29:37
of financial performance. What's going to help people to
29:41
get better value, better, higher performance on their money, not
29:46
just Wellness or health or literacy.
29:50
Amazing. So in terms of metrics for
29:52
measuring or tracking financial indices for CFOs, right, So what
29:58
metrics should CFOs track if they want to quantify the return
30:02
on behavioural banking initiatives that is CFOCFOS of
30:08
small businesses, especially these platforms?
30:10
Yeah. So from a small business
30:12
perspective, you know, they're looking at cash flow, do they
30:16
have the cash they need? They're looking at, you know, a
30:19
lot more operational type things.
30:21
And I think it's important for a lot of financial institutions to
30:24
not just applying the financial Wellness, health literacy to a
30:29
from a retail perspective, but the small businesses are, are in
30:33
dire need of better money management type capabilities as
30:37
well. It's funny how a lot of banks
30:39
will go out there and talk about their treasury management
30:42
capabilities where a lot of small businesses and startups
30:45
don't have the slightest clue what treasury management is.
30:49
They're just thinking about it. As you know, I need cash when I
30:52
need it. I want to make sure that my cash
30:54
flow is good and I've I'm able to pay my bills and pay my
30:57
people and have enough money coming in.
31:00
So there's a lot of opportunity from a banking perspective to,
31:03
to better support some of the startups and small businesses.
31:07
And this is where I'll go back to my example of the gig
31:10
workers. I mean, these are, they don't
31:12
think of themselves as small businesses, but they they
31:15
operate and act behaviorally a lot more like a small business
31:18
than they do with a typical retail consumer.
31:21
OK. So in terms of the
31:23
infrastructure, the structural infrastructure of banks, it was
31:28
stated in our conversation with Bret King that EI based banking
31:32
cannot function without open banking connectivity.
31:35
Do you agree that Open Data Infrastructure is a direct site
31:39
for AIDTCP Financial Services? My gosh, there's a huge can of
31:44
worms on that one. I could come back right now and
31:46
go, what do you mean open banking?
31:49
What is that? And once again, we've got a
31:51
problem around terminology because if you ask 10 different
31:54
people what open banking is, you're you're bound to get at
31:57
least six or seven different definitions.
31:59
The idea that some people have around open banking is that it
32:02
forces the financial institution or the provider to have to share
32:07
all of the data around a customer because it's they
32:10
because the customer quote owns the data.
32:13
That's too simplistic of you. If a bank could just simply say,
32:16
OK, you know, here's here's your OK, Adam all I will share your
32:21
banking information with the the other provider you want me to
32:26
and I tell the other provider, OK, here's Adam, always account
32:29
balances. What good does that do?
32:31
That's that, that count balance might be different an hour from
32:35
now after you've made some big deposits or do withdrawals.
32:39
There's no transaction history involved with that.
32:42
There's no context around that. So I get where Brett's coming
32:46
from pushing the whole open banking thing.
32:49
But what's really more important is better definition around what
32:53
the data means. I mean, even internally, and
32:57
this is the, you know, you may, we may get to this in this
33:00
conversation, or maybe we'll get to it sooner rather than later.
33:03
But you know, one of the things that I think a lot of banks and
33:06
credit unions are just not thinking about correctly when
33:08
they think about AI is think, gosh, we're just going to plug
33:12
this stuff in and it's going to create huge capabilities for us.
33:15
Well, and it doesn't. And the reason it doesn't is
33:18
because quite simply, their data sucks.
33:20
They can't access the data that they want.
33:23
There's different, it's sitting all over the place.
33:25
There's no common definition. The systems and applications
33:29
don't agree on the on on the tool balances.
33:33
You may have to be working with a bank at a mode that has three
33:36
systems that think you're at any point in time your account
33:39
balance is at 3 different levels because of the way they're
33:42
updated and because of the definitions and things like
33:44
that. So there's huge issues around
33:47
that. Open banking in and of itself
33:50
does not solve any of those problems.
33:53
It just forces people, forces the institutions to move data
33:57
around, does not take care of the problem.
34:01
You don't take care of the problem.
34:02
You don't reap the benefits of the technology, the AI
34:05
technologies, unless you are improving the quality of the
34:09
data. I wrote a blog post a couple of,
34:11
I guess a couple of months ago at this point now and it was
34:14
titled if your data quality is low, your AI strategy will blow.
34:19
And I use that in every speech. I hope it doesn't offend
34:22
anybody, the terminology, but it's really is the point is that
34:26
having just the technology itself does not solve any
34:30
problems because of some of the data issues.
34:32
And I don't think open banking solves anything either because
34:36
it's just forcing institutions to move data around and that it
34:40
doesn't doesn't, it doesn't fix the problem or or accomplish
34:44
what anybody thinks it's accomplishing.
34:46
Garbage in, garbage out to see, you know, Speaking of our
34:50
security, quantum and systemic risk.
34:53
So quantum resilience, the possibility of quantum competing
34:58
breaking current and creation standards was raised as a
35:02
serious threat. Our financial institutions, they
35:04
can quantum proceed and CSB now oh.
35:06
Gosh, so again, you won't remember.
35:09
You're not old enough. That's not a bad thing.
35:11
This could be, and I would imagine a lot of folks listening
35:15
will not remember. Let's go back to history.
35:17
Personal computers. Oh my gosh, personal computers
35:19
are going to change. Everything is going to put, it's
35:22
going to, you know, 'cause all these problems and issues for
35:25
financial institutions, they adapted that the Internet, oh,
35:29
this new technology is going to be the death of financial
35:32
institutions. It wasn't mobile.
35:34
No, it's the death of financial institutions.
35:37
It isn't. And now, you know, the next set
35:40
of technology is AI and the one after that quantum.
35:44
Everybody adapts. There will always be new
35:47
providers that will come in and and capture share.
35:51
But you know, if you look at the history of failed banks over the
35:54
last 30 years, almost if not every one of them, 99.99% failed
36:00
because of poor risk related decisions, not because of they
36:06
did not deploy or employ technology.
36:08
So there's going to be a huge shift in, in, in changes and
36:12
adapt, adoption and adapting to quantum computing, quantum
36:17
technology. But it's hard for me to think
36:20
that today in 2026, that a lot of banks, CEO's and management
36:25
teams need to be that worried about quantum computing right
36:29
now. And I know Brett, you know, he's
36:32
a futurist. He his mind, his head's 10 years
36:35
from now, you know, quick story. I went to bred a number of years
36:38
ago. I've been friends with him for
36:40
1516, maybe even a little more years now.
36:43
And you know, he speaks all over the world.
36:45
I went and I go, you know, you ought to really bring me along
36:48
on all your speeches. And he kind of laughed.
36:50
And in that great Australian accent, he goes, and why would I
36:53
do that, mate? I said because Brett, you're a
36:55
futurist. You're telling people what's
36:58
going to happen in 10 to 20 years from now.
37:00
I'm a nowist. I tell people what's happening
37:03
now and what they need to do now.
37:05
So he laughed and I'm still waiting for that invite to go
37:09
with him on all the cool places he goes to.
37:11
The Futurist and the Now is so Speaking of now, how should
37:16
boards think about AI risk, biometric authentication, and
37:21
cryptographic transformation over the next five years?
37:25
The boards should be hammering the management teams for what
37:29
problem are you solving or what opportunity are you taking
37:33
advantage of that AI is going to be involved and they should also
37:38
be hitting them with why do we need AI?
37:41
Could we do this with some other technology?
37:43
And that would make the management teams really think
37:46
through what is the, the business opportunity or
37:49
challenge that they are addressing with the technology
37:52
and making them be able to make forcing them to explain well,
37:57
what is it about the quote AI technology that that's going to
38:02
help solve the problem. I think it gets too much
38:05
buzzword and gets too buzzwordy. And you know, well, we need to
38:08
do something. And I've said into way too many
38:11
board meetings and I see this over time.
38:13
If you go back 15 years or so, maybe not quite 20 years at this
38:17
point, Adam, all, you know, I would hear this in the board
38:20
meetings all the time. I'd hear my granddaughters on
38:23
Facebook. What are we doing about social
38:26
media? And then the management teams
38:28
would all just sort of run and go and you know, go well, we now
38:33
have a Facebook account and what did it do for them?
38:35
Nothing. The boards need to be asking and
38:38
I think many of them do ask smart questions around what's
38:42
the business opportunity? What's the the OR business
38:45
challenge that we are addressing with the technology and why AI
38:49
technologies? Can't we get this stuff done
38:51
with the technology we already have announced or why can't we
38:55
all get it done with the technology we have in house?
38:58
And that forces the management team to answer the question of
39:02
why AI for some of the other stuff.
39:04
And then the question also that the boards need to be asking is
39:08
should we have be developing these capabilities internally or
39:13
can we rely on 3rd party sources to do it?
39:16
Because I think it's going to get to a point where a lot of
39:20
banks are going to say, you know what, we're especially smaller
39:24
banks, mid sized banks in this country, call them 100 billion
39:27
in assets and below. I'm going to get to the point
39:30
where they say, gosh, our, our vendors are not delivering this
39:34
technology fast enough for us. And should we need to be
39:38
building some better capabilities around technology
39:41
internally and not just be vendor managers.
39:44
We're also getting to a point at a MO where the whole model
39:48
around this is potentially getting shook up.
39:51
And what I mean by that is look when when 12 year olds sitting
39:56
in their bedrooms can use clawed code to develop applications in
40:01
minutes, a lot of software vendors need to be rethinking
40:05
their existence. Why do they even exist when 212
40:09
year olds can develop an application and get it out to
40:11
the market? And so what's happening is the
40:15
whole model of generating and building competitive advantage
40:19
on top of technology is really getting challenged.
40:22
It's something that I have embraced for nearly 40 years,
40:26
the idea that technology can help create a competitive
40:29
advantage. And I think it's getting to a
40:31
point where that logic is doesn't take hold anymore.
40:35
The idea, you know, for the past 40 years is that you had a
40:38
vision for how technology could create a better product, deliver
40:43
something faster, create more convenience, and then you went
40:46
out and built it. And that was a serious
40:49
commitment of time and resources to develop that capability.
40:54
And then you have to sell it and support it.
40:56
And so it wasn't easily copied or as easily copied as you think
41:00
that in terms of really making those advantages, of creating
41:03
those advantages. But now the time to develop that
41:08
technology, get it out to market and support it and sell it is
41:12
minutes and days, not years and years.
41:16
And so it's really time for the boards and and management teams
41:20
to be thinking what generates competitive advantage in the
41:23
market today and creates A sustainable competitive
41:27
advantage because it's no longer just technology.
41:30
And I think it actually, interestingly enough, shifts
41:33
back to the human. It still needs to be the human
41:36
that has a vision for what is the problem that we're solving
41:40
or the opportunity we're taking advantage of.
41:43
The AI technologies and tools can help you visually that
41:47
solution and actually build and support that solution, but it's
41:51
got to come from some human creativity to really define and
41:55
understand what is the right problem to solve or what is the
41:58
right opportunity to take advantage of.
42:00
That reminds me of an episode we've had on the podcast before.
42:03
Title Humans fest AI Next. And with the proliferation of
42:09
generative AI and agentsic AI these days, the obsolecence, the
42:15
speed of obsolecence of new technology is very, very
42:19
alarming. It's amazing.
42:20
And with agentsic AI of autonomous liability, so if AI
42:27
agents transact autonomously on behalf of customers, who
42:31
ultimately carries the liability?
42:33
Look, we love to to pick on the AI technologies where all it
42:37
could be making mistakes and doing these things and what are
42:39
the liabilities. But a human is just as likely if
42:43
not more likely to make a major mistake as well.
42:46
I mean, the idea of going back to last fall when I'm blanking
42:51
out on who the company was minted $300 trillion in stable
42:55
coins they make and everyone made a big deal.
42:57
See, see what the problems are with AI.
42:59
It's going to create these problems.
43:02
But reality was is that the tools caught the mistake in
43:05
incredibly speedy time and fixed things before it became a major
43:09
mistake. If you go back a couple years,
43:11
there was a big UK investment provider where some guy fat
43:15
fingered, you know, a $3 billion trade instead of a $3,000,000
43:20
trade. And that rippled through major
43:22
implications of of issues and problems.
43:26
So I think it's funny as a lot of the naysayers, you know,
43:29
everybody, I think both sides of the coin have of the AI coin
43:33
sort of have their weaknesses. The proponents love to say look
43:37
how this is going to transform everything and the life's going
43:39
to be great. And then on the other side of
43:41
the coin are the people say, yeah, but look at the
43:43
liabilities and look at the problems, overlooking the fact
43:46
that, hey, people make mistakes too.
43:49
So you know, the answer I think comes somewhere in the middle,
43:53
but I do think it's inexorable that we are moving towards a
43:57
very heavy automation computing delivery of of everything and
44:03
the AI tools and technologies are going to be key pieces
44:06
around this. Complaining about the liability,
44:09
complaining about this or that may slow things down a little
44:13
bit, but I don't think it changes the the overall
44:16
trajectory of the changes. Interest in.
44:19
So let's talk about the global markets and Greenfield
44:22
opportunities. So the Greenfield advantage,
44:25
emerging markets, we're framed as having potential Greenfield
44:29
opportunity because they are not burdened by legacy
44:33
infrastructure. Do you see data supporting that
44:36
advantage? Oh yeah, absolutely.
44:37
Just look at South America and the growth of new bank.
44:41
But once again, well, they want to put this in a historical
44:44
context. You know, one reason why mobile
44:48
phones were adopted so much more rapidly in emerging countries,
44:53
especially around in South Africa, South America, and in
44:56
certain Asian countries, is because they didn't have the
44:58
telco infrastructure that developed companies have
45:02
developed countries have. So this isn't just a banking
45:06
specific thing, nor is it specific to just AI.
45:10
It's a history of lesser developed countries from a
45:13
technology perspective, leapfrogging certain building of
45:17
infrastructure with newer technologies.
45:20
But even even those things change.
45:22
And what's what should be scary for a lot of countries that are
45:26
more on the developing side is that the change of of pace in
45:30
the from a technology perspective means that, yeah,
45:33
you might be not burdened by legacy technologies that a
45:37
country like the United States has, but in five years, you
45:40
could be burdened by the legacy of the today's technologies.
45:45
If you're not keeping up on that might go back to your quantum
45:48
computing type example where that even not just certain
45:51
countries, but companies that are more AI native in five or
45:55
ten years might find that to be the new legacy technology.
45:59
Everything becomes legacy after some point in time.
46:02
And what's what's kind of scary is that the time from deployment
46:07
to legacy status seems to be declining very rapidly.
46:11
It's typically a very short period of time nowadays and you
46:14
know, talking from a geographical innovation
46:17
standpoint standpoint. So from a research standpoint,
46:21
where do you see the most aggressive AI driven banking
46:25
innovation happening? Right now from a functional
46:29
perspective, probably the the risk areas part of that's
46:33
because there's been a history of using machine learning from a
46:37
risk perspective. But now with broader large,
46:39
large language models incorporating a lot more data
46:43
points, I think the risk world. I would also say that from a
46:46
compliance perspective, huge opportunities to to kind of
46:50
leverage and change the way compliance gets done in a lot of
46:53
financial institutions to build on mostly generative AI
46:57
capabilities, but to a certain extent agentic as well.
47:01
It's interesting that I've done a study recently, I do it every
47:05
year actually, called What's going on in banking.
47:07
I generally survey between 3 and 400 senior executives at mid
47:11
sized U.S. financial institutions.
47:14
Past couple years I've asked them about where they see
47:17
applications in the use of generative AI and agentic AI
47:21
from a functional perspective in their organisations.
47:24
And it's kind of scary to me that there are some really low
47:28
numbers for areas like finance and compliance and even IT where
47:34
it is going to be totally transformed by a lot of these
47:37
technologies. And yet there seems to be a a
47:40
real lack of education around what these tools and
47:43
technologies can do and will do. So from an innovation
47:47
perspective, I think what happens is especially in a lot
47:50
of mid sized institutions, they lock in on a particular
47:54
function. I was actually recently on a
47:57
podcast we surveyed the there was about 350 folks.
48:01
And again, could not have no idea if it was representative of
48:04
any population or anything like that.
48:06
But we asked them, where are you with your AI in your AI journey?
48:10
And Adam Mullet was ridiculous. What a high percentage of them
48:13
said that yeah, we're doing in AI pilot or an AI experiment,
48:20
really just one. I mean, this impacts every
48:24
function in the organisation. So, you know, I think, I think
48:28
part of the problem or challenge is that it starts with even the,
48:32
the use of the term innovation. Well, we're going to do a test
48:36
or a pilot. It's like, no, just throw these
48:38
tools at people, get them to use generative AI to review their
48:42
documents, start using them to, to build capabilities to answer
48:46
questions about policies and procedures in the organisation.
48:50
The contact centre has long been a use of, of AI or AI ish type
48:55
tools around chat bots and things like that.
48:59
But I think it's also important to remember that a lot of those
49:01
chat bots were not necessarily AI technologies, they were just
49:05
rules based engines. But some of the, the leaders are
49:08
really looking at how to employ, first of all, are truly
49:11
employing AI driven conversational AI capabilities
49:16
now starting to use RAG techniques to to improve that
49:19
and to get more of the human aspect of intelligence into the
49:23
equation. So you know, this is happening
49:26
in spots in certain areas. So I don't know.
49:28
I'm, I think I'm at this point rambling and not answering your
49:31
questions. So I'll go back to focus more on
49:35
this one. OK.
49:36
So in terms of tech versus banks, right, is the future of
49:41
financial services more likely to be dominated by technology
49:44
companies that do banking rather than banks that become
49:48
technology companies? I struggle with that because I'm
49:52
not even sure you know the distinction between a technology
49:56
company and a bank. I don't think makes any sense to
49:59
me. A bank is sampling, at least in
50:01
the US, is simply an organisation that is chartered
50:04
or licenced to provide financial services.
50:07
What is chime? Is chime a bank or a technology
50:09
company? At a MOA they don't have a
50:12
financial licence or charter so therefore they are not allowed
50:17
to call themselves a bank. But to their 10/15/20 million
50:20
customers, if you say who do you bank with, they'll say Chime.
50:25
The idea that a lot of bankers have that you know, well, we're
50:29
really a technology company that just happens to have a banking
50:33
licence. No, that's not correct in it
50:36
because there's an implication of what a technology company is.
50:39
The implication of or the the insinuation when you say a
50:43
technology company is somebody who sells technology to somebody
50:48
else, what is the bank? The bank is someone who provides
50:51
financial services. Now if you look at especially in
50:54
the US and you look at most mid sized banks and credit unions,
50:57
the digital banking platforms that they have, vast majority of
51:02
them are not in house developed platforms.
51:05
They come from companies like FIS and Fiserv and Jack Henry
51:09
and Q2 and Alchemy and Candescent and other providers
51:13
as well. Now here's the here's the
51:15
problem or not the problem, but the challenge once again around
51:18
terminology. I would argue that to the
51:21
typical 30 year old consumer, the banking product that they
51:26
buy is the mobile, is the mobile or digital banking app.
51:32
That's what they use predominantly.
51:35
Yeah. Every once in a while they have
51:36
a problem and they might go and have to call somebody, but the
51:40
product they're buying is a product that is almost 100%
51:45
delivered and used through that mobile banking or digital
51:49
banking app. And that app is being provided
51:52
by some third party technology company.
51:55
What's the difference? It is the bank a technology
51:59
company? Is the technology company a
52:01
bank? This doesn't make any sense.
52:04
The terminology doesn't make any sense, and it doesn't matter
52:07
whether you're fintech, a bank, a bank, tech company.
52:10
The terminology doesn't make any sense to me at all.
52:13
Regardless of where you stand, you have responsibilities to the
52:17
customers and speaking from a global landscape perspective as
52:23
well, we have the ongoing prices in what I say the Middle East,
52:29
right. So what implication would this
52:32
have on the global financial services landscape?
52:36
Oh, you got to you're every time you use the word global, you
52:39
scare me because I am so narrowly focused on the US
52:44
banking market. And I think you're asking a
52:46
question that you know, really goes more to politics and
52:51
society and not banking. So I am going to respectfully
52:55
ask you to move on to the next question and because I'm not
52:59
sure I have great answers for that.
53:02
One, OK, OK, it's just we'll pass on that one.
53:06
So Speaking of AI, Aaron, leadership for boards and CFOs.
53:10
So we have a platform called the Diary of a CFO and our listeners
53:16
are very much finance leaders and would appreciate advice on
53:22
how to evaluate AI investment differently from traditional IT
53:27
spend. So I am fond of saying that a
53:32
lot of AI tools and technologies are infrastructure.
53:36
We wrestled with this once again historically with every new
53:39
technology investment like personal computers and Internet
53:43
and things like that and wanting the CFO's always want to know
53:46
what's the ROI, what's the ROI on this.
53:49
But at some point they learned that putting APC on everybody's
53:53
desk did not have a direct measurable return on investment.
53:57
It's infrastructure. There are certain capabilities
54:00
you have to have around hardware, software,
54:03
communications capabilities, things like that, data
54:06
collection, data movement. That in and of itself, the
54:10
investment in those capabilities in and of themselves does not
54:14
produce a direct ROI. It enables something else to get
54:19
done. It enables those things to that
54:21
get done maybe faster, maybe with better quality, maybe with
54:25
less people at a lower cost, or it may enable some something to
54:29
be sold that actually generates a revenue or it might actually
54:34
replace people. That's the getting things done
54:37
with less people. And so there's a cost reduction.
54:40
I think we got to remember that technically speaking, turn on
54:43
investment strictly the way it's defined means you're either
54:47
generating revenue or you're reducing costs.
54:50
That's the only way to truly get return on investment from a
54:55
return on investment perspective.
54:57
But that's not to say that there are other and other benefits.
55:00
And so it's important to look at the the process that's being
55:04
impacted by these technologies and is it enabling something to
55:08
get done faster? If it enables something to get
55:11
done faster, there's a ripple effect that might be measurable
55:15
from a dollar perspective, or maybe it isn't and it just
55:19
creates more productivity, something some of these tools
55:22
might require less people to do the same amount of work.
55:25
That's a benefit. Now, the ability to actually
55:28
generate the return on investment by firing people
55:31
might not be there, but if it creates capacity, meaning, well,
55:36
we could take on twice the volume that we're doing today
55:39
with the same number of people. There isn't a hard return on
55:43
investment. But that is certainly a benefit.
55:45
And I do think that a lot more CFOs are, are really grasp this
55:50
now and understand that it isn't simply a hard dollar cost,
55:53
whether it's a there, it's a reduction in cost of people or a
55:57
increase in revenue where the benefit is important to look at
56:01
this from a process and productivity perspective.
56:04
But some even still, sometimes it's, it's hard.
56:06
I mean, the ability to just use a tool to to have a bank
56:11
employee query a large language model and say, hey, I've got
56:15
this customer who's asking about ABC.
56:18
What do we do when that type of problem comes up?
56:21
You know, today there are some tools, but they're not very good
56:25
at providing the answer to that. And generally what happens is
56:28
somebody has to ask somebody else who then has to go back and
56:32
respond to that original person. That could take 24 to 48 hours
56:36
or more to do. But imagine a technology tool
56:39
that actually answers the question in real time and saves
56:45
that time. Well, it's only saving somebody
56:47
5 minutes of work, but over housing interactions over the
56:52
course of a year, that starts to add up.
56:54
Are you going to fire that person who's answering answering
56:56
the questions? Because they no longer have to
57:00
spend 5 minutes 100,000 times during the year.
57:02
No, they're going to be doing more higher value added work.
57:06
In fact, they're already doing that work.
57:08
They just have to spend some of their time at this lower app
57:10
value work. So I think it's really important
57:13
for the CFOs who are kind of looking at the impact of this to
57:17
not be totally focused on just the hard dollar cost and the
57:22
ROI, but to look at it from a process impact perspective and
57:26
just really make some logical, rational decisions and recognise
57:30
that this is infrastructure. It is going to enable other,
57:34
It's going to enable either people to do things or it's
57:38
going to enable other systems to do things.
57:40
And that's where the whole agentic AI type thing comes in.
57:43
You can't just slap an agentic AI tool into your tech stack and
57:47
think it's going to have an impact unless you've got a large
57:50
language model already in place that drives that.
57:53
And going back from that model isn't going to be doing much
57:56
good unless there is good data quality, good data definition,
58:00
and good data flow moving from wherever it is into the large
58:04
language model. So, you know, the CF OS really
58:07
need to start taking up more of an infrastructure type view of
58:11
AI and stop asking what's the ROI?
58:13
What's the ROI? Interest intake?
58:16
So at the board level, what are the board level questions every
58:20
financial institution should be asking about AI right now?
58:24
They need to start by asking how are we really going to be
58:28
competing in the market over the next couple years?
58:31
What's going to differentiate us?
58:33
What are the threats to our existing positioning?
58:36
And that's not even a technology question until they get answers
58:39
to those questions. And when they get, I should say,
58:41
when they get answers to those questions, then they can say,
58:44
OK, how is AI going to create opportunities for us?
58:49
How is AI going to be a threat to us?
58:51
I think there are sometimes the board's a little too focused on
58:54
the functional level when they should be looking at this more
58:56
broadly from a broader strategic and positioning perspective,
59:01
looking at the opportunities and threats.
59:04
Anxious. And lastly, the uncomfortable
59:06
truth, right? So what uncomfortable truth
59:10
about AI in banking do executives need to hear?
59:14
I mentioned this before, I'm going to reiterate it now.
59:18
If your data quality is low, your AI strategy will blow.
59:22
This all starts. It all starts from having great
59:26
data quality in terms of definitions, appropriateness,
59:31
accuracy, timeliness, integrated capabilities.
59:36
It all starts from data at Ebola, and without that, the
59:39
ripple effects of poor data through the AI capabilities and
59:43
infrastructure is going to to be a real problem for a lot of
59:47
financial institutions over the next couple years.
59:49
Amazing take so I, we typically end with the quick fire round or
59:55
before I put you through the quick fire round, I'm going to
59:57
put you on the hot seat. So it's actually been a really,
1:00:00
really amazing session with you today and it's amazing having.
1:00:05
The what I say the elite level thought leaders of my own time
1:00:10
right have conversations that share really bad and highly
1:00:15
resourceful insights. I grew up reading the likes of
1:00:18
the great philosophers like the Aristotle's, the Socrates, the
1:00:22
Plateaus, and I wish I could delve into a conversation with
1:00:27
them. But now Many thanks to the
1:00:29
technology in our agent time, we're able to connect across
1:00:33
several kilometres to make these important, you know,
1:00:37
conversations happen. So it's a very, very big thank
1:00:40
you from me to you. I say gratitude is good attitude
1:00:43
and when a person shares the time with you to share the life
1:00:45
with you. So which you are the hearted,
1:00:48
who would you like to see on this podcast, right?
1:00:51
So she named one name you can invite yourself and one name
1:00:55
that I should invite myself. So leave it to names you would
1:00:59
like to see on this forecast. Yeah, I'd love to see you get
1:01:02
Jack Dorsey the podcast. You know, he has been the
1:01:06
creator of a couple of amazing companies, Twitter and Square
1:01:10
now called Block. He also just fired 40% of his
1:01:14
staff and said that AI is and will be creating the
1:01:17
capabilities to replace them. So I would love for you to to
1:01:21
put him through the ringer and defend a lot of his actions and
1:01:24
beliefs. So would love to see you get
1:01:26
Jack Dorsey on the. Jack Dorsey at ease, right?
1:01:30
So I would invite Jack Dorsey myself.
1:01:32
So who are you going to invite on the forecast?
1:01:34
Oh, I have to invite somebody. Okay, but I got to think about
1:01:38
that a little bit. So somebody that I know that
1:01:41
that you could put on the podcast.
1:01:44
So I think you should get Wade Arnold runs a company, founded a
1:01:49
company called Move MOO. He also was the founder of a
1:01:53
company called Bano that was acquired by Jack Henry.
1:01:56
And when it comes to really understanding technology and how
1:02:01
it applies, especially in the payments world these days, but
1:02:04
just in general into the financial services world, he's
1:02:07
super, super knowledgeable. He can go into depths around the
1:02:11
technology that I couldn't, excuse me, ever come close to.
1:02:14
And if you'd like, I'll make an introduction to Wade for you.
1:02:17
Absolutely, absolutely amazing. And of course, shout out to him
1:02:21
right now. So we are officially inviting
1:02:23
you on the podcast. So we'll go with a quick fire.
1:02:26
Round five quick questions and quick answers.
1:02:30
So #1 is what is 1 Fintech narrative that the data simply
1:02:36
does not support. The data does not support the
1:02:39
idea that the impact of AI technologies will have such an
1:02:44
immediate impact as some people think it will.
1:02:47
Interesting. So #2 what is 1 AI capability in
1:02:51
banking that is under height? 1 AI capability in banking that
1:02:55
is under height. OK, ready for this one?
1:02:58
The answer is spell check. I cannot tell you how many times
1:03:01
I write stuff and Microsoft does not catch the spell check or the
1:03:05
grammatical errors. I upload my document into either
1:03:08
chat sheet PT or Claude and it comes back and tells me all the
1:03:12
mistakes that Microsoft Word didn't capture.
1:03:15
Spell check. Amazing capability, totally
1:03:17
under hype. Amazing, amazing, amazing.
1:03:21
Just like what I tell people that my favourite productivity
1:03:23
tool is the calendar. Calendar.
1:03:26
And you're surprised because the simpler the solution, the more
1:03:31
effective and more efficientities for me.
1:03:34
I, I stand to be corrected #3 what is your three-year
1:03:38
prediction that you're willing to stand behind?
1:03:41
Well, first of all, I'm always willing to stand behind every
1:03:44
one of my predictions, and I don't even really make that many
1:03:48
predictions. Remember, Brett's the Futurist.
1:03:50
I'm the nowest I'm looking. At The Futurist.
1:03:53
Right now, so Brett's the the guy and he'll even say that he
1:03:56
doesn't look out three years from now.
1:03:59
But here's my prediction is that again, I go back to the the
1:04:03
research I did that found that, you know, a relatively low, and
1:04:06
I'm talking single digit percentages or even low double
1:04:10
digit percentages of bankers think that tools like generative
1:04:14
AI and agentic AI will not be impacting many of their
1:04:17
functions. Three years from now.
1:04:19
Generative AI will become a core P of every function within the
1:04:24
bank. Interesting.
1:04:25
So what is 1 Leadership behaviour every CEO must develop
1:04:30
in the AI era? They must develop not a
1:04:33
behaviour, but an understanding that technology can be used to
1:04:39
develop a relationship with a customer.
1:04:43
Relationships are not just people to people.
1:04:45
The idea that people do business with people, yeah, that's
1:04:49
nonsense. I can't even begin to tell you,
1:04:51
Adam MOA, how many Apple products I have in my house and
1:04:54
I don't know anybody who works at Apple, but yet I got a
1:04:58
million Apple products that the attitude or behaviour, it's not
1:05:02
really a behaviour, but the attitude, the technology can be
1:05:06
critical in developing a customer relationship has to be
1:05:09
embraced by every financial services leader.
1:05:11
Interest in. So the last one is a challenge
1:05:15
to Brett King. So Brett positioned AI as
1:05:19
existential for banking. We have.
1:05:21
Would you respectfully challenge him on this?
1:05:23
Every new technology development is gets threatened to be
1:05:29
existential. And it is.
1:05:32
I think we're making too big an idea to make a deal of.
1:05:34
It is every bank is on the Internet, Every.
1:05:37
Every bank eventually had PCs on people's desks, every bank
1:05:41
eventually had Internet access. Every bank has some form of
1:05:45
cloud computing in it. Every bank is utilising mobile
1:05:49
technologies now. Isn't it existential?
1:05:52
Yes, of course it's existential. Is that a bad thing, A threat?
1:05:55
No. Everybody will adapt to this.
1:05:58
Some will do it sooner rather than later.
1:06:01
They will have temporary advantages that their lives will
1:06:04
be a little bit easier because they will have spent more money,
1:06:08
but ultimately have made their lives a little more easier
1:06:11
because they were early adapters or adopters.
1:06:14
But guess it's existential. But it's that's a good thing,
1:06:17
not a bad thing, so. Finally, Ron, if you had one
1:06:22
word on one piece of advice for the next generation of financial
1:06:27
services leaders listening today, what would that piece of
1:06:31
advice be? Get your data in order.
1:06:35
Amazing. I know it does.
1:06:39
It is not very sexy, but you got to get your data in order.
1:06:42
Wow. Thank you very much for your
1:06:44
time, Ron. So I like to tell the audience,
1:06:47
guys, I'll just go for something in your lives, conceive beliefs,
1:06:50
tractor pride and achieve. But do you want to leave a
1:06:53
charter to anyone before you take your?
1:06:57
Hey, shout out to my buddy Brett.
1:06:59
Thanks for leading the way on this.
1:07:01
He's been a visionary in this space for a long time now.
1:07:04
And I I walk in his shadow, on his coattails to to translate
1:07:09
all his great visions into like, What do banks and credit unions
1:07:13
do now? Amazing, amazing.
1:07:14
Thank you very much once more. Like I always love to see when a
1:07:18
person he shares a town with you this year like it because the SI
1:07:21
unit for measuring time for measuring life is time.
1:07:25
So thank you. Thanks for having me on at a
1:07:26
moment.