Credit Union Exam Solutions Presents With Flying Colors

Treichel: Hey everyone, this
is Mark Trekel with another

episode of With Flying Colors.

I'm excited today to be speaking with Ray
Reagan and we're gonna talk a lot about

AI and its implications for credit unions.

Ray, how you doing today?

Ray Ragan: I'm doing well, mark.

I'm really happy to be on your show.

You know, I, uh, I, I've been listening
now for, uh, almost two years in

my role as a former interim CEO
and, and back into my role as CIO.

There there's a lot of things that you're
covering in your show that has been of

interest and, you know, candidly, uh.

It's really the absence of, uh, regulatory
guidance that caused me to reach out and

say, Hey, mark, would you be interested
in talking a a little bit about this, uh,

path to bringing AI into a credit union
beyond the LOS beyond fraud and actually

started using it as a strategic tool?

Treichel: Yeah, I'm, I'm excited to talk
about, about ai 'cause I'm, you know, I'm,

I try and dabble in it just to, I've got
a good friend who's in his seventies who.

Who, you know, who always is on the
front end of different technology things.

And I've seen that it's kept him young.

So I'm, I'm looking at this, these
AI things as a way to tap into what's

coming, but also stay educated, but
also to kind of keep that brain fresh.

So, and Ray, I wanna, you
know, you, you've got a, a,

an interesting background.

I'm gonna read through your,
your, uh, thank you bio that.

That I saw here on CU Insight, and
again, thanks for reaching out.

So we could do this, but, uh, you're
currently the CIO of Security plus

Federal Credit Union in Baltimore.

You bring extensive experience from the
defense and financial technology sectors.

Specializing in implementing machine
learning intelligence systems, this and

speech analytics at Enterprise Scale.

You have a master's degree in admin from
Northern Arizona University and studied

strategy and risk at Stanford and the US
Army Command and General Staff College.

That's right.

Uh, you're a credentialed project
manager, professional, PMP, and

hold several agile certifications.

Uh, and thank you for your service, by
the way a combat veteran and decorated

US Army Officer Ray formerly served
with the 75th Innovation Commands

National Capital Regional Team, we're he
focused on AI enabled human detection.

And computer vision.

He's also a serial entrepreneur
and holds a patent in AI and

computer vision technology designed
to improve customer experience.

And NPS Ray, I'm excited to chat
with you today about, uh, about this.

But, but seriously, thank
you for your service.

Uh, and all that that entailed.

I, really appreciate
that side of this too.

Ray Ragan: Yeah.

You know, and, and it's funny, mark, it,
it as a Army Reserve officer, it really

gave me an opportunity to get, um, get
to see some of those applications of AI

way beyond, you know, what we're thinking
about in the financial institution space.

You know, uh, you mentioned in
there the, the whole application

about computer vision.

Well, uh, I actually filed
another patent after that.

Was is, uh, that, um, that blurb came
out and, um, I, I have another, uh,

patent pending for check fraud, and
that's all computer vision based.

And so, you know, it really
has opened up a, a real.

Realm of opportunity for
novel applications of ai.

And you know, as, as you heard me
mention before, you know, I, I really

have been looking to the NCA and the
federal, uh, agencies to push out good

meaty guidance of, you know, how do
we as a federally insured financial

institution go and implement ai, uh, at.

Be beyond what our vendors are
offering, and start thinking

about it as a strategic partner.

And, you know, one of the things
I like to talk about with credit

union leaders is, have you
considered how AI can actually can.

Augment or even add additional
capabilities to your management team,

and many of them are, they're still
thinking about fraud, they're still

thinking about OSS and, you know,
hasn't even occurred to them that they

could feed in their strategic plan into
these AI large language models and get.

Challenging insights and, and observations
that, um, you know, you couldn't do

unless you actually had some sort of
business analyst or somebody doing

the deep analytics, you know, to
gain that, those insights for you.

And, this is exactly what,
uh, really excites me and,

and why I am excited to be.

On your show and talk about, you know,
some of these regulatory concerns

because I, I think the lack of guidance,
uh, has really created a lot of

fear, uncertainty, and doubt within
the credit union space, particularly

at the board and management level.

And so, you know, it, not getting
that signal that all the water's fine.

Folks have been, you know.

Have been reluctant to go forward,
and so that, that's why I really have

been championing this model of how
do you bring AI into a credit union?

Treichel: Well, and it's
interesting too, you know, the.

The Trump administration ha has edicts
out there that, that, that came out

real quick, uh, at the beginning of
the administration about, you know,

you need to get rid of X number of
regulations before you consider one.

You need to hold back on guidance.

What?

Well, the, the, the, the.

The theory of, of this administration and,
and a lot of the folks appointed to the

Supreme Court is that, you know, there's
the Federal Credit Union Act, for example.

There's regulations and guidance
is further and further away from an

actual rule, and it, and it's right,
and it's guidance, and is it binding?

And, you know, NCA in your role, as you've
seen, where NCA will say, we'll save.

This is safety and sound is driven
and it's tied to this letter to

credit unions, like third party due
diligence or, pick, uh, or, or their,

their priority letter that comes out.

And it, there's a general almost
reticence to issue those things right

now because it's not something that
the administration necessarily wants.

And Chairman Halman, you know,
he talks about the concept of, of

being supportive of AI and that
NCOA is dipping their toe into it.

They did a briefing at their
last board meeting, I believe.

And that's right.

It was, it there, but there wasn't a
lot of you probably know more than me,

but my take from that briefing was they
kind of dipped their toe in and didn't

really say much of anything they, they
said they use, they use it for like

balancing their 5,300 data that comes in.

They're contemplating looking at it
possibly about, you know, the fraud side

of it, but he also, he does set the tone.

That, that he wants the industry to know
that he's generally supportive of it.

But that doesn't mean that you're
gonna know how the examiners are

gonna deal with it when they come in,
because everybody, you know, even when

there is guidance, you, you have a
bell curve of how NCA deals with it.

You've got the good, the bad, and the
ugly of that life is a bell curve, right?

I mean.

Everybody kinda looks at it a little
different, but if there isn't the

guidance, that kind of makes it a
little bit bit more wild west maybe.

Is that kind of what your sense of there

Ray Ragan: Yeah.

It and, and that that's exactly it.

And, you know, um, in my uniform capacity,
you know, I've been invited in some of

these you know, high level federal agency
the sausage being made on AI policy.

And, you know, it was interesting
under the Biden administration a

lot of that sausage being made was
actually very, um, uh, in, in the

interest of prove that it's safe.

Before.

We'll, we'll give you the,
uh, the, the go ahead.

With AI and under the Trump
administration, we're we've

definitely have seen that flip and
now it's, uh, it, it's much more,

Hey, you know, um, you know, uh.

Go ahead and, and start implementing it.

And, you know, we have to step back
and consider what's happening globally.

And, we've got the European model
and we have the Chinese model, and

the Chinese have gone all in on ai.

They have taken all the safeguards
and all the safety rails off.

Whereas Europe, on the other hand,
has done the prove that it's safe.

Model and you it.

You know, as, as part of that recent
NCUA meeting they reviewed the

executive order and let me pull it
up just so I'm speaking correctly.

It's the executive order, 14 17 9,
removing barriers to American leadership

in artificial intelligence and, as part
of that briefing, you know, they were

talking about these things that are
paramount within the Trump administration.

And so, we, as you know, folks trying
to interpret the tea leaves, you

know, we're, we're stepping back and
saying, okay, so where do we think this

is really gonna reasonably lead us?

And I, I think what's gonna reasonably
what we're gonna see as reasonable

outcomes out of this is we're gonna see.

Likely our examiners to be a
little bit more open to it.

And when I have talked to the
NCOA examiners about it, they

said, yes we acknowledge that we
haven't published guidance on this.

What we're recommending is that, uh,
credit unions embrace the NIST AI

risk management framework, which,
you know, it, it's a good starting

place and, uh, you know, and.

So obviously a lot of credit unions
are interested in going into AI and

going beyond what their vendors are
packaging to them and start integrating

it into their, their actual operations,
integrating it to their management,

integrating it into their strategy.

And so, you know that, that.

Because there's, there's such a big
gap there between what we know how to

do as, uh, as credit union management
versus what the NCA is asking us

to do, and that's embraced that
nist AI risk management framework.

I, I, I've gone as far as starting
to offer an ai, a free ai.

Workshop to management and boards,
just to help them understand, you know,

all the work that needs to be put into
place to be reasonably aligned with

that nist AI risk management framework.

Because it, it's kind of a big lift
and, you know, unless an, unless a

leader's been through a previous, uh.

Malcolm Baldridge Award
or a ISO certification.

These are things that we don't typically
do within the credit union space.

And so that, that's the reason why,
you know, I really wanted to offer

this this workshop to help those
credit union leaders and their boards

say, okay, we're gonna be brave.

We're gonna be bold.

You know, we, we think the NCA is is
going to let us, uh, embrace this.

So how do we do this with sufficient
safeness and soundness for our

depositors and our membership?

And so that, that's, that,
that's really where we're at.

And I know that was a very long response,
but, you know, this is, no, that's good.

This, there's a lot of
nuance going on right now.

Treichel: Well, and, and your timing's
good as far as those the seminars

that you're offering out there
because it's that time of year, right?

It's NC a's gonna be looking
at their strategic plan.

Credit unions, uh, if they haven't
just gathered, they're gonna gather

in the fall for, you know, and start
defining what 20, 26 and beyond brings.

So, uh, it's, it's really
a, a ripe season for.

For taking some of that on.

So let's, let's talk a little bit about.

The, that offering that you're having
the, you know, what, what would the if

you had to give the top X number of, of
topics that you'd, you'll be, without

going into a deep dive and actually doing,
doing the presentation here, but what,

what would the, you know, what would
the, if someone signed up for that, what

would their expect expectations be of
what would the major topics would be?

Ray Ragan: Yeah, so, um, I offer
it in a couple different formats.

One is kind of a more of a shortened
format, uh, where it's delivered

in just a simple just one hour.

And really what I talk about there
is what a credit union would need

to do to reasonably align with that
NIST AI risk management framework.

And really what it boils down to Mark
is having that board approved policy.

But, that sounds really easy.

There's, so, you know,
it's the iceberg, right?

There's so much work that goes on in
order to get that, uh, that policy

ready for the board to be considered.

And, you know, it comes down to
a risk a, a comprehensive um.

Risk assessment within the credit union,
it take, it requires review of their

data governance and how they're handling
their data classification and how the

credit union intends to use that data
and what they consider to be, that,

that public, that internal and that
confidential restricted levels of data.

Because, ultimately at the end of the
day, AI is it's almost like bringing.

Someone onto your management team and,
you know, and you know, we, we can

sign those NDAs, uh, with consultants,
you know, obviously as employees,

uh, and management members, you
know, we have that duty to protect.

Confidential information, but these
large language models, whether it's

Gemini Chat, GPT, or Cloud, or, or
whatever your favorite one is, you

know, they don't have that duty.

And so, you know, it, it takes that
board proof policy, that deliberate

risk assessment for uh, a credit union
to be able to start using those tools.

And so, you know, that that's kind
of my short, and then, you know, the,

the longer is, you know, I get into
some of the tools and techniques about

how, how you can really leverage,
you know, uh, chat GPT and, and,

and, and that family of tools for.

Broader management purposes.

You know, that's, that's conducting market
research you know, expansion and merger

opportunities, those types of things.

Because you know, I, I, I
always like to point out that.

If you go and use chat GPT,
it's like talking to somebody

that has a PhD in everything.

And if you talk to somebody with
a PhD in everything, you know, it,

it's, they're gonna give you very good
advice, but it's not gonna be really.

Directed to what your purposes are.

And so, you know, I talk about those
techniques of setting the persona, setting

the constraints, and in when you're
actually working with chat GPT or Gemini

or, or again, your, your tool of choice.

And because those types of
techniques really help the large

language models produce very.

Accurate and actionable insights
that if you just asked it.

You know, hey would you would
you review my strategic plan

and make a recommendation?

It, it's, it's not gonna be very good.

But if you do the priming, if you, and
you know, there's this whole joke in

ai, is AI engineering a real thing?

I would actually go, I, I
would actually argue in, in.

In the realm of Yeah, I, I think it is.

And, and so in that, in that more,
that longer workshop, that four

hour workshop you know, we get into
the much more hey, this is, this

is how you would actually use it.

Use, uh, to s.

To solve a management problem, a strategic
or operational management problem,

much beyond just, you know, using this
as a fraud detection or, you know, an

automated attendant in your call center.

Because that's where like the
bigger ahas start happening and

we haven't even talked about.

AI yet.

And that's, that's a whole
nother, uh, uh, realm.

And, uh, and I'm, I'm, excited to talk
about that a little bit more later.

Treichel: Absolutely.

Absolutely.

Well, you know, and, and, and as, as
we're talking through this and I'm, I'm

thinking about the, when I've my toe into
the AI world, it, it reminds me of the

quote, if you ask the wrong question,
you're gonna get the wrong answer.

Yeah.

And the prompts help you help you.

Get a better answer.

And it, and one thing I, that I've also
stuck on in my head on AI is it's like

having a, you, you, you, it's like having
a team of researchers, of summer interns

that can gather some information for you.

But when I have looked at it at
something where, let, let's say,

let's say I personally have a.

A college level.

So I have a, I have a
degree in accounting.

If I was to ask it an accounting
degree, accounting type question I

can be more precise in my question
because I know enough going in.

Right?

Yeah.

If I was, if I was to start asking
questions about how can I build a

kayak that will hold water and not.

Sink.

Right.

Which I know nothing about.

If I ask that type of question
I'm more likely to get a wrong

answer and get misled it seems,
from what I've been experiencing.

And so, uh, any general thoughts
on, on that, that statement

and, and agree, disagree?

Well

Ray Ragan: absolutely agree and, you know.

The one thing I always like to
point out to anybody that that

said, Hey, you know, I asked chat
GPT and it gave me a wrong answer.

Yeah, it, it, they certainly can.

But the thing I always
like to point out is

the large language
models we're using today.

Are the worst ones you will use
your entire professional career,

Treichel: right?

Ray Ragan: And so they're, they're
only gonna continue to get better.

So this is where that, that
prompt engineering that, uh,

really starts coming in, you know?

Um.

If you're gonna ask it that question
about the kayak, you know, you should

preface it as, hey, you know, you are
a seasoned carpenter who have, who

has been creating artisanal kayaks,
you know, for the last 20 years.

And it's funny 'cause like.

You wouldn't think like that kind
of persona setting before you start

having your engagement with the,
uh, AI is really that important.

But it is, and, and, the, the way I
like to look at it is, think about

it as, you know, as a seasoned
professional, you know, if you get

just asked a, a general question.

You're gonna give a, a general answer, but
if somebody says, Hey, mark, you know, I'm

studying for my CPA, and you know, I, I'd
like to know what your thoughts are about,

uh, about this kind of amateurization.

Under the gap rules and, and
you're gonna give a much better

right response than yeah.

You just need to make sure
your balance sheet looks good.

Yeah.

Yeah,

Treichel: exactly.

Well, and, and another thing is you, as
you're you're talking about, you know,

using it beyond the normal things like
the call center or the fraud detection.

You know, when I was at NCUA.

Organizationally, and, and it's not just
NCA, but you have, you know, you have

this office and they're responsible.

The Office of Examination is
responsible for, for the exam program.

You have the Office of General Counsel
who writes legal opinions and regulations.

You have the regional directors and
their teams who, who do the exams.

You have the HR director, you have the.

You know, you have the security
people and they all have their roles

and responsibilities and they all
have the world that they live in.

And there it had, but there's
a bit of a stove type.

Stove pipe mentality mm-hmm.

Is where I know what I know.

And he knows what he knows.

And she knows what she knows.

And you get together and you do this
strategic planning, and you try, you

know, you, you have the wisdom of crowds.

You bring all those people to the table.

But there's this wealth of, of
data and information as opposed to

bringing their brains to the table.

You have all this.

Plethora of data that you could also
under the large language models bring

to make, make a, a better decision.

But, and have I have, I hit on a theme
there that it, it's that if you, if

you embrace this it is kind of like
having that wisdom of crowds and

breaking down some of those stove pipes
to improve your your end result and

then your end performance, et cetera.

Ray Ragan: You, you're
exactly right, mark.

And you know, this goes back
to the fact that yeah, it, it

can give you wrong responses.

And so, you know, that's where
having, you know that that experience,

that know-how to be able to ask it.

The, the tough follow-on
questions and, you know, um, I.

Generally speaking, most of the AI systems
out there right now are very sycophantic,

you know, and, and the they'll,
you'll upload your strategic plan and.

If you haven't primed it to
begin with, it's gonna say, oh,

this is a great strategic plan.

It's sure to drive your credit
union to your, your goals.

And but if you, if you preface
it, if you say, Hey, chat GPT,

you are a season credit union.

A consultant that had been
working for 20 years you know,

you are working in this market.

I, I'm in the Baltimore market
and, consider my strategic plan

for all the issues that, that I
may have overlooked, and it's gonna

give you a much better response.

Than if you just said, Hey, you
know, look at my strategic plan

and let me know if it's good.

It's gonna be like, oh yeah, it's great.

And, you know, it's just these
models are, out of the box.

They're sycophantic.

When you are a senior member
of management, you don't

want s sycophantic answers.

You know, you don't want a yes man.

You want somebody that's gonna
really challenge your thinking

and challenge your, your insights.

And so you know that that's, that's
part of that, that, that duty of,

if you are gonna use those tools,
and, and this is again, you know,

stuff that you'd be covering in,
what the management has agreed to

use AI for and what the board is, has
authorized and those controls in place.

And, you know, I, I talk a lot
about ethical AI and, you know, I,

I boiled it down to four principles.

One is that it's disclosed.

Two, it's consensual.

Three, it actually adds
something to the human condition.

And then lastly, and probably
most importantly, is that

it's human accountable.

And you know, if you, as a credit
union leader are going to use AI as

defined by the board approved policy,
ultimately you've gotta be responsible

for the output that, that it gives.

And you know, 'cause it.

And I think this is the, the fear of,
of policy makers is that, you know, at

some point in the future, uh, a, member
management is gonna say, oh, AI did that.

That's as we saw with Sarbanes Oxley,
that that's not an okay answer anymore.

Right.

And, you know, so that, you know,
I, if, if you're accepting that

accountability, that human accountability,
you know, I, I think that's.

Gonna be one of those things
that really goes a long way.

And, you know, you mentioned earlier
about the bell curve and, some things

are gonna be okay with this region's
examiner and some things are not gonna

be okay with that region's examiner.

And it'll be interesting as this
community practice continues to grow.

It's just, it's so new and just, Nope.

Yeah.

And it's just, it, it's very hard.

Treichel: You know, and I,
and when I think about the,

starting at the policy, um.

And I think about, you know, ERM and board
governance, uh, at credit unions and some

of the conversations I've had, with credit
unions, we you'll have situations, where,

uh, the credit union has a strategic plan
that says we're gonna do A, B, and C.

Right.

Uh, and then it's a five year plan.

It's a three year plan.

And then, then in the middle of that
timeline, they get a, they get an

opportunity to buy a bank or take on
a merger of a like sized institution,

but it wasn't in the strategic plan.

And then, uh, they.

They go down that path, they end
up contemplating wanting to do it.

And then NCA comes in on the back end
and says, we didn't see this in your

strategic plan, and it's a big initiative.

Yeah.

What was the, and then that's
followed by, was the board involved?

What did the board know?

When did they know it and did
you need to document that more?

And so.

I can see in my mind the evolution
wherever it, when there does come to be

some guidance, you know, there's gonna
be talking about, about the, the board

governance side of it and the policy and,
and you know, the NIST framework needs

to have it have A, B, and C and D in it.

But I can envision as they
start to understand it and,

and it gets more vetted out.

This example of, well, the AI made
me do it, and then back and saying.

You know, you didn't have a policy
or your policy said you would do it

this way and you did it that way.

Yeah.

So, yeah, it, it, it's, you know, it,
it's kind of the, the, the wild west

of, of exploring the west, right.

And, and all that it entails.

But putting up some frameworks I could
see why, you know, that's, that's a great

first starting point of how you're gonna
do it, what you're gonna use it for.

What's the human intervention, right?

What what, yeah, exactly.

Who signs off on it?

'cause you can't say, uh, Claude did it.

You can't say Chad did it.

That won't get job jail.

Ray Ragan: Yeah.

And that, that's exactly it, mark,
is, you know, so long as your policy

has that human accountability,
that's, that's the key part, right.

Because, you know, I, again, I, I like
to think of it as, imagine hiring, a,

a really smart consultant, ultimately
when she comes in and makes these

recommendations, it's ultimately the, the.

Board, or it's the, ultimately
the management that's accepting

that consultant's recommendations.

It's very similar with ai.

I mean, obviously, you know, there's we
talked about some of the confidentiality

and, and you know, some of those other
considerations that come with, um,

using AI and, and I, I would never
advocate putting sensitive member

information into, uh, any one of these
large language models that, that would

be, you know, I, I, I, I think that
would be a, a blatant uh, violation of,

uh, safeguarding member information.

But, can you obfuscate that data?

Can you aggregate it?

Yeah.

I, I, I think you probably could,
but again, this is where you,

the board needs to be informed.

They need to understand how
management plans to use it.

And then ma, you know, management
needs to use it the way that they

said, and this is the thing about,
good governance is, you know, you say

what you do and you do what you say.

And so long as you stay within in
those, those guide, guide rails.

Probably gonna be okay.

Treichel: Yeah, no, that,
that's a excellent point.

Um, agentic ai you did,
I, did I say that right?

Ray Ragan: You did, yeah.

Well, it, it's funny, you know, 'cause
this is one of those, uh, terms that none

of us heard until about six months ago and
you know it when it was first coming out.

And e even as a, as a, as a person
that works in AI and I literally.

Code in AI on, on the
weekends, uh, or code on ai.

I used to say it as AI that
has agency just because it was

such a, a, a turn of phrase.

You know, and you, you, uh, you heard me
allude to it a little bit earlier, right?

And you know what, what's.

What's happening with Ag Agentic ai
and both Google and Amazon now have ag

agentic AI workflows and you prob you
may have seen, uh, I think it was last

week, uh, Wells Fargo announced that they
were actually gonna roll out AG agentic

AI to all their to their workforce.

And so, you know.

We're familiar with chat, GPT
and imagine Chat GPT if it had

the ability to go and do things.

And so, you know, the example I like to
use is, so I am a credit union marketer.

And, um, I create an age agentic
workflow that goes and looks at the

what are the popular themes in Reddit,
Instagram, and TikTok for certain topics.

And actually the age.

Agentic AI then allows me to
pre-program to the large language model.

The personas, the constraints and
the considerations of bringing that

data back from Instagram, TikTok, and
Reddit, making recommendations to to

me as the, the credit union marketer.

Of these are some areas that might
resonate really well for this week,

uh, in, in your credit, in the credit
union's positioning and, this is

all stuff that, would've would've
taken a human to go and, and.

Read all the posts and everything
and, and, uh, come back

with these recommendations.

You know, it's hours of work and now
it can happen almost instantaneously

and it comes back into that large
language model that is actually

looking at the data it gets based on
your rules and the persona set forth.

And actually create outcomes from it.

It could if you allowed it, it
could automatically post to your

credit union's, Facebook, Instagram,
feeds of, of what it came out with.

It could also give you those
insights on market opportunities.

Hey, based on what I saw
there's, there, there are, um.

Opportunities for this kind of product.

You know, stable coin's, the
hot thing right now, right?

And, and so it, it, it could say,
Hey, you know, if you ran a promo

right now highlighting, some kind of
stable coin affiliation it's likely to

get a substantial amount of, of, um.

Virality.

And, you know, this is a very
flat case for agentic ai.

And you know, what I like to say is, you
know, that imagine bringing that into

your credit union's back office and,
you know, having the ability to look at.

Applications and make
decisions based off of it.

And then send off that, that,
uh, information to the next step.

And, and when I say the next step,
the it's the next information system.

So it's the next database, the next
LOS, and the idea is that you're

saving your humans, for those times
when it is important to have that.

Human to care.

And you know, this is one of those
themes that I, I always drive home is

that, you should be caring with your
humans and scaling with technology.

And a lot of, a lot of times in credit
unions, we get that backwards and we

try to scale with our humans and then
care with the technology and that that's

completely opposite of what it should be.

Yeah.

Treichel: Yeah.

That's, and

Ray Ragan: Just genic AI is
gonna really open that up.

Treichel: Uh, yeah, that's a,
that's a powerful statement.

You know, and you've you've
touched on the agentic ai.

You, you've, uh, touched on the,
the classes that you offer and

what you in include in that.

What else should have I asked you
here, uh, today about all of this

that, that we wanna make sure
the listeners can hear today?

Ray Ragan: Yeah.

So Mark, again I'm very
grateful for this time.

I deeply respect, uh, the
content you cover on this show.

And, and I, I appreciate what you're
doing in terms of helping us credit

union management understand the, the, the
murky waters that, that are our industry.

You know, and, and really what
it comes down to is that hey.

We underst, we, as the
collective industry.

And, and forgive me for for using that
understand that the guidance we're getting

from our regulators is not super clear.

And.

We really do need to embrace ai.

You heard me talk about Wells
Fargo rolling out Agentic AI

to their entire workforce.

We're already trying to compete
at that level and that those

margins are gonna continue to be
compressed and it's gonna continue.

We're gonna continue to find
difficulty in scaling and staying.

Competitive with the market leaders.

And certainly, you know, go ahead, mark.

Treichel: Well, and I, and I was just
gonna say, you know, uh, an earlier thing

you said kind of ties back to this is.

The, the youth, uh, of our, of
our planet are gonna be more

comfortable today with ai.

Uh, and the leaders of every industry
needs to be embracing it because

because that it is the future.

And oh, by the way, credit unions
sometimes can be challenged by

bringing the young folks into,
uh, the credit union, right?

So it's perfectly aligned with, you
know, when you talk Reddit and you talk.

Instagram and, and Facebook and,
and all those social medias and

ai, and being able to utilize that.

To make it a, to, to enhance your
marketing to be able to bring

a, a, a better mix of members.

You, one of your articles talks
about, about, um, AI and, and merger

and maybe we should talk a little
bit about, about, about that, that

soundbite and what that means to you.

Sure.

So

Ray Ragan: Mark, you know, um, I, I.

I led off my, the last article with,
yes, AI will merge your Credit Union.

And you know, while that sounds
very inflammatory, it's true.

And what I mean by that is that there are
going to be the financial institutions

that do figure out how to embrace ai,
how to operationalize it, how to offer

hyper personalized journeys, and, and.

Discussions with their members
that they truly feel valued and

return to that credit union.

And then they're gonna be the credit
unions that don't figure it out

and continue to try to compete just
entirely off the goodwill and spirit

of the people in the credit union.

And while that's a very noble
way to uh, continue the fight.

It's not a sustainable one.

And so that's, that's really the
thesis of the, of that article is that

I either, you figure out AI and you.

You're able to compete or AI is
going to be used by the credit

union that you're merging with.

Treichel: Sure.

And avoiding the challenge of the
regulator, not having clear guidance

avoids that challenge with the regulator.

Meanwhile, you're getting further
behind on, where the future is heading.

Ray Ragan: I, it, it, it absolutely is.

And this is where and, and this is
why, you know, I'm offering these

free workshops is because you know, I
understand the fear, uncertainty, and

doubt that we have in, in our industry.

But we can't wait for guidance.

So we need to take the best guidance
we have, which is, you know, follow

the NIST AI risk management framework.

And learn through doing of what
AI can do for your membership.

Treichel: Great point.

And so on the, on, on the opportunities
to sign up for these classes, if someone,

uh, wants to take you up on that or they
just wanna chat with you about this topic

in general, what's the best way for them?

To connect with you, Ray.

Ray Ragan: Yeah thank you, mark.

Um, so they're free to email me
at rayReagan@securityplusfcu.org,

or they're welcome to
connect with me on LinkedIn.

Both ways work.

Again, I'm offering this as free.

I, I, because I, I really believe
that it's critical for credit

unions to, to embrace this now
not three years from now, but now.

Treichel: No.

Makes total sense.

This is, uh, you, you learned,
you, you taught me a few things.

Uh, I, I think some of my listeners
probably learned a few things.

And, and, uh, I encourage you to
reach out to Ray if this sounds like

something, uh, that you'd want to
educate your board with, educate

your management team with Ray.

I wanna thank you for,
um, reaching out to me.

We've been connected on, on
LinkedIn for a while, but, um,

I, I appreciate you reaching out.

So that we could, uh, talk about
this important topic today.

Ray Ragan: Thank you so much, mark.

I really appreciate the opportunity.

Treichel: You got it.

And listeners, I want to thank
you as always for listening.

I hope you will listen again soon.

This is Mark Tril signing
off with flying colors.