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Market Pulse is a monthly podcast by Equifax, in partnership with Moody’s Analytics. Equifax hosts bring you interviews with industry experts on the latest economic and credit insights that can help drive better business decisions. Whether you’re in financial, mortgage, auto or another service industry, we help make sense of the latest economic conditions that impact you. This podcast series supplements our Market Pulse webinars, which occur on the first Thursday of each month.
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Welcome to the Market
Pulse podcast from Equifax,
where we break down the latest economic
and credit insights to help you navigate
today's business landscape.
Welcome to today's Market Pulse
podcast. I'm your host, David Adams.
I'm the head of commercial
product marketing here at Equifax,
and today's topic is small business fraud.
I'm also very excited to welcome Jill
Molitor, our guest speaker here today.
She is the Director of Fraud and
Credit Administration at Stearns Bank
and an expert in this
particular subject. So, Jill,
thank you again for joining us
today. We really appreciate the time.
Thank you. Likewise.
Now, before we kind of jump
in today's main conversation,
we're going to do a quick macroeconomic
update from Shandor Whitcher,
an economist from Moody's Analytics, and
we'll be right back with you. Go ahead,
Shandor.
The US economy continues
to show resilience with our
high frequency GDP model,
expecting the economy to expand
nearly 3% in the second quarter.
Job growth slowed in June with the
economy adding 57,000 jobs in the month
down from 129K in May. Now,
also notable from the report were
downward revisions to prior months.
This brought the three month moving
average to 111,000 monthly gains down
from 164,000 in the month prior.
The unemployment rate fell
from 4.3% to 4.2% in the month.
Though this was driven primarily by
workers dropping out of the labor market.
Now, the inflation picture
did improve in June.
Consumer prices fell
0.4% month over month,
and this was driven by steep declines
in energy prices, which fell 5.7%.
The most encouraging figure from
this business report, however,
was that core CPI,
which excludes food and energy and tends
to provide a better view to underlying
inflation was flat in June,
which should quiet concerns
of a near term rate hike.
Now that said with tensions
reaccelerating in theStrait of Hormuz,
the inflation picture is still in flux,
so we'll have to keep an eye on it.
Excellent. Thank you for the
update, Shandor. I really
appreciate it. Now let's,
let's get into the meat
of the conversation today
and small business fraud.
You know, Jill, I guess it's
been a couple, three weeks now,
you were kind enough to join us
for our Market Pulse webinar.
Got a lot of feedback from folks.
You guys covered a lot of topics,
but there is no question by far,
fraud stood out more than anything
else. And you know, it is interesting.
You and I have known each
other for a few years now,
and we've talked a lot
about speed to market.
We've talked a lot about process and
what was it even before today's call we
were talking about it.
It's like, you know,
before it used to be automation and
strategy. What is it that was, so 2025,
right?
Yes. .
You know, I don't, the
interesting thing about it is,
I can't pinpoint that
moment that it shifted from
like the process and some of those
things and automation to now fraud
being such a hot topic because it was
all about let's go, let's go, let's go,
let's go. And I know your experience
is like, we're hearing a lot more of,
hold on a second. Fraudsters
are getting really good. Like,
how do we make sure that we stay
quick while not letting in some of the
fraudsters and things
like that. Like, is there,
are you guys seeing that push and
pull over there at Stearns Bank?
Like what are the things that you
guys are looking at? You know,
where are you guys going with
that? What are you seeing?
Yeah, so thank you for
that question, David.
And that is such a loaded question
because we really need to approach each
phase of that onboarding
process with caution. And it,
it does open the door to fraud.
You talk about synthetic identity,
which I call the Frankenstein
fraud. We talk about identity theft.
We talk about equipment
that is ghost equipment.
It doesn't even exist. And so
let's really just take a step back and
we'll start with that onboarding process
because identities can pass
those historical check the
box types of safeguards
that worked for years, and we need
to be really careful for that.
So in other words, when we're
talking about synthetic identity,
that is one of the,
that is one of the really hot
topics when we're talking about AI.
We're talking about DeepFakes.
It has become easier and easier
for bad actors to create these
Frankenstein identities. And
they, the people have a name,
they have a social security number,
a date of birth, physical address,
phone number, photo identification,
can be easily purchased on the dark
web or even created by the bad actors.
They get very creative
by applying for credit,
by knowing that they're
going to be denied,
but they're creating a footprint.
And so then eventually they receive a
yes from a creditor with this identity
that they built. And they're starting
to build that credit profile.
They'll maybe get a credit card and then
pretty soon it's an auto and eventually
they're moving on to some larger purchases
and eventually they bust out where
they stop paying. And you are
left as a creditor, high and dry.
No recourse because this person
and business didn't exist.
So they're creating synthetic
identities and it works for them.
They're going to continue to find ways
to obtain credit and the money are goods
that go with it.
Becausethey've already started to create
a new identity once they bust out of
that first one and leave you high
and dry. Business perspective,
they're looking at secretary
of state filings that may have
been active at one point and
they're reactivating them. They're,
they're creating newer identities
through those secretary of state filings
because they're creating a
time in business by looking
at something that maybe
needed to be revived.
They're slipping in some of these
created synthetically created personal
identities and making
it look like we have a,
a business and a person
that is operating these,
they could very easily fabricate
a webpage where they're taking
pictures from other webpage that
they have used or that they have
found that have equipment that
they're supposedly selling.
So they're looking like
they're a legitimate company.
Or they might even take the webpage
of an existing company and change
those details ever so slightly.
So it looks like that it,
it is the bad actor that's,
that's that's using that business.
But then they'll maybe slap
in a false phone number and
webpage to get to redirect
people to that falsified webpage.
So if we look at those couple of pieces,
like from a traditional know
your customer, they have,
they have a name check, address,
check, date of birth, check,
let's check the boxes. So
it's, it's like, let's go,
let's give them some
credit. So the curve ball,
then we have legitimate person,
legitimate business of
applying for credit.
A bad actor might use tools
to intervene a legitimate
transaction where maybe they're sitting
in somebody's email for a while and
they're waiting for that moment when
wire instructions get passed through and
the bad actor will then slip in falsified
wire instructions that are being
redirected to an account
controlled by them.
And all of a sudden we've got a
legitimate, legitimate dealer,
legitimate customer,
and they're like, well,
I'm not giving you the equipment because
you didn't pay me and the bank's like
we sent you the funds. So
that's, that's another way that,
that these bad actors in,
in today's day and age are,
are committing some sort
of identity theft. So we,
we really need to make sure that we're
vigilant in understanding and using our
tools to look through
and not check the box,
but really verify that we know
who we're doing business with.
If something changes along the way,
we need to take the time and have a
human look at that and intervene and,
and ask ourselves, does it make sense?
So we can to an extent use those
traditional verification methods,
but we also need to make sure that we're
using those with logic.
Yeah, no, that's a great point.
Because you can still stay quick.
You can still stay efficient if
you're keeping an eye on things.
But to your point,
a little bit of due diligence really
does help kind of solve some of the
problems. You know, the ironic part
about it, I remember, you know,
even just a couple three years ago
that when you heard synthetic ID,
you're like, oh, that's a consumer.
That's an individual. Or you know,
account takeover, you know,
that was. Now it's like,
it's like fraudsters have just
taken what they learned there,
brought it over into the business
world. The thing that that,
and we've heard this
a couple of times too,
absolutely echoes exactly what
you said is with consumers and
individuals, you know, we
have long history sometimes,
and sometimes it's a little bit harder
to create those synthetic IDs and
whatnot. Companies are so much easier
because of the number of startups,
the number of applications and how they're
coming through. So just as you said,
it's like a business. Okay,
it's a legitimate business.
It's got a legitimate start date.
It's got a legitimate address.
It's got some of those things.
But wait, hold on a second.
David Adams isn't actually David Adams
or there's something else going on.
And looking at both of those pieces
together, just some of the basic checks,
you know, can really make a
difference. But, you know,
and I'll shift on you a little bit because
one of the things that we also hear
is, and we've been, we have been
getting this question so many times,
which is, how are you guys using AI?
How is AI going to solve this problem?
And there seems to be,
there seems to be this thought
that this AI utopia is going to
exist of, oh, just plug in AI.
It'll find the fraudsters and everything
is great. There's one little twist,
and that is many of the fraudsters
are using AI to commit the
fraud and go through that.
Like there's little, you know,
it's those little tiny
things that figure it out.
And I think everybody is trying to figure
out how to combat that a little bit
more. I know you've seen some of that.
Have you seen huge uptick or have you
seen the AI component kind of start to
feed into that?
Or is that still kind of out there in
the ether somewhere and hasn't really
reared its ugly head yet?
Yeah, it is. It is. It's out there.
And as financial institutions
we have the responsibility
to ensure that our
customer data remains safe.
And so you'll hear ourselves and a lot
of financial institutions that say,
we are implementing AI very strategically
and very carefully where we need
to completely vet to that process.
Now, while we're doing that,
we also need to make
sure that we're aware of,
and that we're vigilant of
fraudsters who are using AI.
And it's there's, there's tools.
There's machine tools that we can
use because AI is, what is it?
It's a machine that's learned it,
it's learned behavior. So it'll,
we can use our existing tools
without even implementing actual AI
to look for velocity checks.
So measuring how quickly
certain events occur,
like if somebody has multiple
failed attempts on the same card,
if the same device, same IP
address, rapid account logins,
out of pattern deposits,
things like that. So if,
or if there's geographical differences
in like where a customer is
located and the dealer's located,
that can flag us where we
need to look deeper into it.
So even without directly
using AI, we can use tools.
We can develop tools too that will
research and understand patterns,
and we can use those patterns that
are pulled forward as pink flags to
further look into those. So we can train,
we can train something to pull forward
those flags and that machine is doing
what it's tr what it's trained to do,
but then we need to be very careful that
these aren't false positives because it
might make sense in today's day
and age. You talk about AI, David,
and what are people doing on the internet?
They're looking for
the best deal possible.
AI doesn't look for that best deal
possible necessarily, but it is.
But you are going to use your computer,
you're going to use open source
searches to look for the best pricing or
the best equipment or the best vehicle.
If you want something
that's extremely customized,
you might buy that from a
dealer that is geographically
far away from you.
And so we can use we can use
our detection to say, Hey,
does this make sense? But it
doesn't mean it's necessarily fraud.
That just means we pull it forward and
our human brains need to look at that and
ask ourselves questions. Does this seem
logical? Does it make sense? It might.
And if it doesn't, and when it doesn't,
then we need to dig deeper and
look for those other patterns of
potential theft identity, true identity
theft, synthetic identity theft.
So the other thing I would say too is
that just asking those logical questions,
even when we are manually
underwriting credit applications or
manually reviewing identities
and comparing the consumer
credit to the business credit.
Does it make sense for a business
to be located in California,
but the owner or owners are operating
maybe in Florida. Maybe there's
one in Florida and one in New York.
Does that make sense? So it's
asking questions like that.
Or if you have an identity that
is pulled forward because that
there's a possibility that it was
synthetically created or something doesn't
seem correct, looking at the date
of birth. Does the date of was,
was that date of birth,
does it make sense in proximity to
how long the company has been around?
Maybe there was an ownership
change. So then it might make sense.
We have to ask questions,
was the social security number
issued in the last five years?
But this is somebody who we would've
expected to have had credit for many years
prior to that that could indicate
that there's digging we need to do.
Those are just some of the things that
we need to be looking for and monitoring.
And a machine can't pick up
those types of questions.
So we need to make sure that
we're looking into those.
Another situation might be
knowledge-based questions.
Going back to that
Frankenstein identity, ,
if they created that identity,
they can answer their knowledge-based
questions because they created themselves.
Yeah.
So we need to be careful in those types
of situations where if a lender or a
sales person or credit person comes
to us and says what they passed their
knowledge-base questions. Yes,
but there's these other pink flags
that we need to dig into. So it's, it,
you can kind of talk yourselves in circles
and that's where we really need to.
You can't look at any one of
these pink flags in the silo.
We need to pull them together and we
need to do it quickly and efficiently
because that customer is
waiting for a decision.
And if we're not speedy but accurate,
that's going to cost
us one way or another.
Yeah, it's a really good point.
And it kind of even ties into what we
were talking about before is the customers
are waiting, we've got to be speedy,
but we got to be cautious. You know,
the velocity thing I think is
really interesting as well.
It's almost as you were saying that the
first thing that came to mind was like,
trust but verify. You know, whether
it's verify, is it a false positive?
Is it real? You know, AI
can help out a little bit.
Detect some of those patterns, but there's
still no substitute for that person,
for the individual. Mm-Hmm
. You know,
AI is only going to be able to do so much.
Like I even heard the other day that
that fraudsters or that somebody
came across an instance where a
fraudster was using AI to generate just
like, sort of like what you said
around the knowledge-based questions.
They can answer that stuff,
but they were using AI to generate
paperwork that would be used in like an
underwriting process and
background and information.
The thing that was really interesting
about it was the thing that popped was
what you said around date of birth and
the alignment, things like that. Like,
it's like it was something,
it was something crazy.
Like the person had to be like nine years
old, you know, when they started it.
But it's crazy. Some that wouldn't be
picked up sometimes by the AI piece. It,
you know, it might only be picked up
by that human thing. Like to me, yes.
I think this applies all the
way around in many ways with AI,
like combining AI and technology
and people really makes
that big difference. Gives a
little bit of that, you know,
the knowledge that we have and the
experience like you have in terms of doing
this stuff for so long.
So yeah, you absolutely can't
replace that institutional knowledge.
You, yeah,
you have to be able to use
logic to anything that a
machine has pulled forward.
Whether it's machine learning,
whether it is information from a
report or whether it is from ai.
Something you said there.
I want to go back to something because
we've been talking a lot about fraud in
terms of, call it third party
fraud people. True fraud.
Somebody that's coming in that's making
stuff up. I've, you know, synthetic ID.
I've made a fake profile, fake company,
all this other stuff. You know,
when we talked about consumer fraud,
you know, there's sometimes there's a,
there's the first person,
and you mentioned it before,
sometimes the bust out fraud.
It's not just a fake identity
and things like that.
It's people that are actually coming
in maybe doing some loan stacking,
you know, trying to take advantage of,
of a situation because maybe they're
a little bit stressed in terms of
their cash flow. And they're like,
look, I don't, you know, I don't,
I'm not trying to commit fraud,
but I'm definitely pushing
the limits or you know,
I'm definitely trying to get more money
because I'm stacking up multiple loans.
One of the things that we get questions
about regularly as well is the
difference between, again,
credit reporting agency,
the difference between score and capacity.
Capacity has become a really hot topic.
It's one thing to have a business
score or even using the consumer score
of, you know, make it up 600.
But there's a big difference between
that somebody that's a 600 that's doing
$200,000 a month in cash flows
and $20,000 a month in cash flows
and what maybe where there's more debt
kind of coming in and going out. I don't,
I guess what I'm trying to figure out,
and the question that we get often is,
is there a way to be able to assess
an individual's capacity to be able
to return?
Like are there things that you're
looking for from a stress standpoint?
Like we see it, we see it in
defaults and delinquencies.
If somebody's in default or a delinquency,
they're stressed out. But how do we,
how do you see that otherwise?
Like are there other things that you're
looking at or is that just part of the
overall picture for you guys?
Yeah, so again, you can't, you can't
look at it in a silo. Where you,
you touched on it a little bit, David,
where you said there might be somebody
that has a 600 credit score and they
might pay as agreed. And they have
a very, there's a very solid reason.
Although a 660, that's a
pretty low score, but it could,
I've also seen people who
maybe have a 750 score and that
$200,000 in debt and they're struggling
or they're on the brink of struggling.
So you really, again, you can't look at
that in a silo. You need to look at the,
you need to take a very holistic review.
And by holistic,
I'm talking about their
financial capacities and
looking at both the consumer
credit and the business credit and
understanding do they have
the capacity to pay their
personal and their business bills?
And so we're looking at their,
we're looking at their personal credit
reports and sometimes those have
business debt that is reported on
them and taking into consideration.
Does that seem to marry up to the size
of business that they state that they
have or to the equipment that they
state they're going to be financing.
So you really need to look
at that holistic report.
And then also do they
have affiliate businesses?
And so what we're really talking
about is global cash flow.
And even without financial statements,
especially in a flow type of a business,
small ticket types of transactions,
there's ways to at
least deduce whether the
request makes sense when you
blend together. What does their,
what do the personal guarantors,
the owners of the business,
what does their personal credit look like?
What does their business credit look
like and what does their business credit
look like for their affiliates?
And that's also going to help you
determine do they have debt stacking?
Do they have a lot of UCC
filings that have been made?
What does the economic
economy look like in their
particular industry, in
their geographical region?
Does that make sense? So,
and that can help us determine
both on the front end,
does this seem like a credit risk
that's within our risk appetite?
And from a collections perspective is do
they show signs of debt stacking there.
There's tools that you can use that
can help you determine is there debt
stacking or is there shifts
in their business score that
we would want to pull forward to
determine if we need to reach out to that
borrower and talk to
them about seasonality.
Or if there's a payment relief
modification that we would need
to consider to help them.
Geez, I, you know, I
got to tell you it is,
this would be a whole lot better
if this was an easier process.
I'm just telling you. It's,
you know what's funny is, again,
we go back a few years and we think
about fraud. Before, you know,
it was a little bit easier
because fraudsters weren't
nearly as sophisticated m
m-hmm . As they are
today and they're just getting better.
Like your comment on the affiliates too,
especially in a world where serial
entrepreneurship is a common thing,
and you might have people out there
that have two or three or four or five
different businesses that
they're connected to,
so that that stress
component is not shoot,
you could have one company that's stressed
in another company that's a isn't it
would be nice to be able to go and tell
everybody, oh yeah, it's super simple.
This is really easy. But to your point,
like you really do have to look at it all.
Yeah. And it's not that they're trying
to hide anything. Sometimes you might,
I might review a credit application
where there the applying
entity has limited credit.
But once we start to really
understand the holistic
approach and talk to them
about their business,
we find out that well this piece
of our business is a startup,
but we have this to set established
business that is willing to step up to
the plate and do a cross corporate
guarantee and then we can strengthen the
deal. So it's not necessarily bad,
it's these are things that we can look
for because we want to try and help and
find solutions for our borrowers. It's
not necessarily fraud where it can go,
yes, it can go to a, a situation
where it's like, oh my goodness,
we didn't realize that one company
was draining from the other.
But it can actually strengthen
transactions as well. And,
and we can find solutions for what we at,
at the onset or on the forefront or
what's initially presented to us would be
presented as a thinner file. Yeah.
We can find out by talking to them and
having conversations or just doing due
diligence internally that they're
actually a very strong and to,
and a very strong with
strong affiliate companies.
Yeah. So, so you've said a few
times looking at the big picture,
looking at the whole picture,
looking at those things.
As you were saying that one of the
things and you mentioned the the owner a
little bit and it clicked a little
bit in that. Because when you're,
I guess here's, here's the thing.
When you're looking at these accounts,
you're looking at these affiliates,
you're looking at these different
companies I've heard you say as well,
understanding the whole
owner. Understanding the
picture of the owner itself.
So not just the business.
Like to your point,
when you're kind of evaluating these
things, are you looking at the,
are you looking at both the
owners' consumer background as
well as the business background?
Are you using those two together to
kind of understand a little bit and make
more decisions? Mm-Hmm .
I know it's a little bit nuanced.
Like we heard as an example,
one of the things that we continue to
hear and is people have said that a
consumer's credit history or
they tend to bring those same
behaviors into businesses that they run.
So generally good consumer behavior
echoes good business behavior,
bad consumer behavior, sometimes
not always good business behavior.
But I think that's very anecdotal.
Like I I don't think we've been able
to validate is it true? Is it not true?
Like are you looking at those kinds
of things as a part of the mm-hmm
. The evaluation process too?
Yes, absolutely. And to your point, most
times one will mirror the other. Okay.
And it's not always, it's not
always that that is the case.
But in most cases you do
see mirroring of others.
Now when you get to a very large company,
well then it might not make as much
sense to look at that whole owner
perspective where meaning you're
looking at their global cash flow.
It is still a very good idea.
And we would want to encourage
that when we're doing our
underwriting. But sometimes if you
get to like a publicly traded company,
that's just not something
that is realistic.
Or when you look at a company
that is oh that where it is
a parent and that parent
has multiple umbrellas,
then it might not make as large
of an impact on a very small,
a very small loan in comparison
to their size.
But it is something that for most small
businesses we would be looking at.
Yeah, I could see it. I
was going to say it's,
I hope nobody's looking at my personal
credit when they're evaluating Equifax as
a business business that the standpoint
that might be a little bit awkward,
but yeah.
Well, yes, and you're not, I I don't
think you're an owner of Equifax,
so if you're not the, if you're
not the owner, no, even the owner,
it might be a little bit
different because of the size.
I got some shares. It's fine. No, ,
you're absolutely right. Well, it's,
you know, it is, this to me is
a really interesting subject.
Because when we talk
about fraud, you know,
fraud is sometimes very it's negative.
It's people are looking at it,
people truly people, bad
people doing bad things.
But this is a really, like,
I'm always interested in this,
this component as it relates to the owner.
Because if, you said it even earlier.
If you're looking at the
owner, their background,
like a change of ownership and the
business mm-hmm itself,
like being able to do those little things,
I think can help determine the
difference between fraud and legitimacy.
And if you can help
delineate between those two,
then you kind of go down to that next
layer of, okay, we think this is good.
We think that people are good. What can
they really do? What's the capacity?
You know, how do we look
at these things? You know,
together we do see a lot of those
trends with our consumer data and our
commercial data together. So it seems to
be lining up and you know, it's, again,
it's look.
So I know we're coming a little bit
towards the end and so I want to,
I think we might be able
to close some things out.
because The and I think we kind of
alluded to it a little bit, is, you know,
maybe we could look forward
a little bit, you know,
break out our crystal ball because ai,
future fraud, all those things
that go along with it. Like,
is there, if somebody, if you were,
if somebody were to come to you
and ask you and say, hey Jill,
I need to do something
today that's going to make,
that's going to help me in six
months, 12 months, 24 months from now,
what would you recommend to them?
Is there a true future proof option?
Is there something that somebody could
be doing that solves that problem?
Or is it just an ever evolving world
that we're living in? Right. Oh.
It's always going to be ever evolving.
And my crystal ball broke years
and years ago. .
Fair enough.
I keep trying to fix it, but some of
the things that I would say is we,
we need to use to conversations
that we've had both today and in
prior conversations,
we need to continue to use our
resources and rely on each other and
use both machine learning as well as human
intervention and apply
logic to them. You can't,
you can't look at either one in a silo.
And we need to continue
to develop and to train
internally and externally.
We rely a lot on training because
I only know that something seems
fraudulent or that from a credit
perspective that something seems risky if
somebody brings it forward,
if it's not a transaction
that I'm working on myself.
So we need to make sure it's
great if I have the knowledge,
but we need to educate internally,
our team members and externally within
our communities so that they understand
what, what trends to look
for. What to monitor.
How to bring those forward and to feel
comfortable bringing those forward.
We'll kind of close things
out. I just want to, again,
thank you so much for joining
us. I really appreciate it.
If anybody has questions. If
anybody wants to reach out to you,
what's the best way for them to
be able to get a hold of you?
Yeah, absolutely. You can reach
out to me on LinkedIn. It's,
Jill Molitor and you'll find me under
the Sterns Bank header. Perfect.
So absolutely would love to connect.
Very good people. Again, it's
been a pleasure knowing you these,
these past several years
and I look forward to,
to doing a lot more with you in
the future. So thank you, thank.
You.
And to our audience out there
if you guys enjoyed the topic,
if you have more questions,
anything like that,
please listen to our other
podcasts. Visit our market insights.
If you have questions, reach
out to our advisors@equifax.com.
We would also love to talk to you.
We'd love to have a deeper conversation
with you around fraud and verification
and all the things in each other,
challenges that you might be facing
in your commercial portfolios.
In the meantime, thank
you again for joining us.
The information and opinions provided
in this podcast are intended as general
guidance only, and are subject
to change without notice.
The views presented during the podcast
are those of the presenter as of the
date.
This podcaster recorded and do not
necessarily reflect official positions of
Equifax.
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using the contact desk box on the
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