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Combating Small Business Fraud in an AI-Powered World · Market Pulse

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Welcome to the Market Pulse podcast from Equifax,
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where we break down the latest economic and credit insights to help you navigate
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today's business landscape.
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Welcome to today's Market Pulse podcast. I'm your host, David Adams.
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I'm the head of commercial product marketing here at Equifax,
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and today's topic is small business fraud.
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I'm also very excited to welcome Jill Molitor, our guest speaker here today.
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She is the Director of Fraud and Credit Administration at Stearns Bank
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and an expert in this particular subject. So, Jill,
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thank you again for joining us today. We really appreciate the time.
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Thank you. Likewise.
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Now, before we kind of jump in today's main conversation,
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we're going to do a quick macroeconomic update from Shandor Whitcher,
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an economist from Moody's Analytics, and we'll be right back with you. Go ahead,
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Shandor.
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The US economy continues to show resilience with our high frequency GDP model,
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expecting the economy to expand nearly 3% in the second quarter.
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Job growth slowed in June with the economy adding 57,000 jobs in the month
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down from 129K in May. Now,
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also notable from the report were downward revisions to prior months.
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This brought the three month moving average to 111,000 monthly gains down
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from 164,000 in the month prior.
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The unemployment rate fell from 4.3% to 4.2% in the month.
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Though this was driven primarily by workers dropping out of the labor market.
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Now, the inflation picture did improve in June.
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Consumer prices fell 0.4% month over month,
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and this was driven by steep declines in energy prices, which fell 5.7%.
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The most encouraging figure from this business report, however,
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was that core CPI,
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which excludes food and energy and tends to provide a better view to underlying
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inflation was flat in June,
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which should quiet concerns of a near term rate hike.
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Now that said with tensions reaccelerating in theStrait of Hormuz,
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the inflation picture is still in flux, so we'll have to keep an eye on it.
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Excellent. Thank you for the update, Shandor. I really appreciate it. Now let's,
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let's get into the meat of the conversation today and small business fraud.
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You know, Jill, I guess it's been a couple, three weeks now,
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you were kind enough to join us for our Market Pulse webinar.
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Got a lot of feedback from folks. You guys covered a lot of topics,
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but there is no question by far,
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fraud stood out more than anything else. And you know, it is interesting.
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You and I have known each other for a few years now,
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and we've talked a lot about speed to market.
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We've talked a lot about process and what was it even before today's call we
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were talking about it. It's like, you know,
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before it used to be automation and strategy. What is it that was, so 2025,
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right?
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Yes. .
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You know, I don't, the interesting thing about it is,
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I can't pinpoint that moment that it shifted from
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like the process and some of those things and automation to now fraud
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being such a hot topic because it was all about let's go, let's go, let's go,
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let's go. And I know your experience is like, we're hearing a lot more of,
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hold on a second. Fraudsters are getting really good. Like,
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how do we make sure that we stay quick while not letting in some of the
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fraudsters and things like that. Like, is there,
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are you guys seeing that push and pull over there at Stearns Bank?
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Like what are the things that you guys are looking at? You know,
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where are you guys going with that? What are you seeing?
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Yeah, so thank you for that question, David.
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And that is such a loaded question because we really need to approach each
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phase of that onboarding process with caution. And it,
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it does open the door to fraud. You talk about synthetic identity,
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which I call the Frankenstein fraud. We talk about identity theft.
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We talk about equipment that is ghost equipment.
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It doesn't even exist. And so
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let's really just take a step back and we'll start with that onboarding process
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because identities can pass those historical check the box types of safeguards
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that worked for years, and we need to be really careful for that.
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So in other words, when we're talking about synthetic identity,
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that is one of the,
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that is one of the really hot topics when we're talking about AI.
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We're talking about DeepFakes.
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It has become easier and easier for bad actors to create these
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Frankenstein identities. And they, the people have a name,
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they have a social security number, a date of birth, physical address,
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phone number, photo identification,
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can be easily purchased on the dark web or even created by the bad actors.
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They get very creative by applying for credit,
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by knowing that they're going to be denied,
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but they're creating a footprint.
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And so then eventually they receive a yes from a creditor with this identity
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that they built. And they're starting to build that credit profile.
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They'll maybe get a credit card and then pretty soon it's an auto and eventually
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they're moving on to some larger purchases and eventually they bust out where
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they stop paying. And you are left as a creditor, high and dry.
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No recourse because this person and business didn't exist.
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So they're creating synthetic identities and it works for them.
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They're going to continue to find ways to obtain credit and the money are goods
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that go with it.
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Becausethey've already started to create a new identity once they bust out of
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that first one and leave you high and dry. Business perspective,
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they're looking at secretary of state filings that may have
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been active at one point and they're reactivating them. They're,
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they're creating newer identities through those secretary of state filings
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because they're creating a time in business by looking at something that maybe
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needed to be revived.
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They're slipping in some of these created synthetically created personal
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identities and making it look like we have a,
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a business and a person that is operating these,
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they could very easily fabricate a webpage where they're taking
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pictures from other webpage that they have used or that they have
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found that have equipment that they're supposedly selling.
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So they're looking like they're a legitimate company.
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Or they might even take the webpage of an existing company and change
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those details ever so slightly. So it looks like that it,
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it is the bad actor that's, that's that's using that business.
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But then they'll maybe slap in a false phone number and
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webpage to get to redirect people to that falsified webpage.
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So if we look at those couple of pieces,
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like from a traditional know your customer, they have,
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they have a name check, address, check, date of birth, check,
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let's check the boxes. So it's, it's like, let's go,
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let's give them some credit. So the curve ball,
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then we have legitimate person,
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legitimate business of applying for credit.
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A bad actor might use tools to intervene a legitimate
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transaction where maybe they're sitting in somebody's email for a while and
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they're waiting for that moment when wire instructions get passed through and
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the bad actor will then slip in falsified wire instructions that are being
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redirected to an account controlled by them.
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And all of a sudden we've got a legitimate, legitimate dealer,
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legitimate customer, and they're like, well,
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I'm not giving you the equipment because you didn't pay me and the bank's like
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we sent you the funds. So that's, that's another way that,
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that these bad actors in, in today's day and age are,
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are committing some sort of identity theft. So we,
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we really need to make sure that we're vigilant in understanding and using our
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tools to look through and not check the box,
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but really verify that we know who we're doing business with.
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If something changes along the way,
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we need to take the time and have a human look at that and intervene and,
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and ask ourselves, does it make sense?
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So we can to an extent use those traditional verification methods,
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but we also need to make sure that we're
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using those with logic.
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Yeah, no, that's a great point.
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Because you can still stay quick.
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You can still stay efficient if you're keeping an eye on things.
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But to your point,
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a little bit of due diligence really does help kind of solve some of the
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problems. You know, the ironic part about it, I remember, you know,
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even just a couple three years ago that when you heard synthetic ID,
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you're like, oh, that's a consumer. That's an individual. Or you know,
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account takeover, you know, that was. Now it's like,
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it's like fraudsters have just taken what they learned there,
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brought it over into the business world. The thing that that,
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and we've heard this a couple of times too,
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absolutely echoes exactly what you said is with consumers and
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individuals, you know, we have long history sometimes,
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and sometimes it's a little bit harder to create those synthetic IDs and
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whatnot. Companies are so much easier because of the number of startups,
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the number of applications and how they're coming through. So just as you said,
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it's like a business. Okay, it's a legitimate business.
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It's got a legitimate start date. It's got a legitimate address.
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It's got some of those things. But wait, hold on a second.
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David Adams isn't actually David Adams or there's something else going on.
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And looking at both of those pieces together, just some of the basic checks,
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you know, can really make a difference. But, you know,
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and I'll shift on you a little bit because one of the things that we also hear
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is, and we've been, we have been getting this question so many times,
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which is, how are you guys using AI? How is AI going to solve this problem?
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And there seems to be,
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there seems to be this thought that this AI utopia is going to
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exist of, oh, just plug in AI.
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It'll find the fraudsters and everything is great. There's one little twist,
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and that is many of the fraudsters are using AI to commit the
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fraud and go through that. Like there's little, you know,
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it's those little tiny things that figure it out.
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And I think everybody is trying to figure out how to combat that a little bit
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more. I know you've seen some of that.
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Have you seen huge uptick or have you seen the AI component kind of start to
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feed into that?
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Or is that still kind of out there in the ether somewhere and hasn't really
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reared its ugly head yet?
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Yeah, it is. It is. It's out there.
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And as financial institutions we have the responsibility
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to ensure that our customer data remains safe.
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And so you'll hear ourselves and a lot of financial institutions that say,
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we are implementing AI very strategically and very carefully where we need
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to completely vet to that process. Now, while we're doing that,
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we also need to make sure that we're aware of,
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and that we're vigilant of fraudsters who are using AI.
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And it's there's, there's tools.
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There's machine tools that we can use because AI is, what is it?
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It's a machine that's learned it, it's learned behavior. So it'll,
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we can use our existing tools without even implementing actual AI
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to look for velocity checks.
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So measuring how quickly certain events occur,
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like if somebody has multiple failed attempts on the same card,
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if the same device, same IP address, rapid account logins,
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out of pattern deposits, things like that. So if,
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or if there's geographical differences in like where a customer is
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located and the dealer's located,
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that can flag us where we need to look deeper into it.
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So even without directly using AI, we can use tools.
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We can develop tools too that will research and understand patterns,
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and we can use those patterns that are pulled forward as pink flags to
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further look into those. So we can train,
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we can train something to pull forward those flags and that machine is doing
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what it's tr what it's trained to do,
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but then we need to be very careful that these aren't false positives because it
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might make sense in today's day and age. You talk about AI, David,
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and what are people doing on the internet?
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They're looking for the best deal possible.
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AI doesn't look for that best deal possible necessarily, but it is.
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But you are going to use your computer,
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you're going to use open source searches to look for the best pricing or
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the best equipment or the best vehicle.
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If you want something that's extremely customized,
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you might buy that from a dealer that is geographically
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far away from you.
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And so we can use we can use our detection to say, Hey,
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does this make sense? But it doesn't mean it's necessarily fraud.
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That just means we pull it forward and our human brains need to look at that and
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ask ourselves questions. Does this seem logical? Does it make sense? It might.
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And if it doesn't, and when it doesn't,
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then we need to dig deeper and look for those other patterns of
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potential theft identity, true identity theft, synthetic identity theft.
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So the other thing I would say too is that just asking those logical questions,
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even when we are manually underwriting credit applications or
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manually reviewing identities and comparing the consumer
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credit to the business credit.
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Does it make sense for a business to be located in California,
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but the owner or owners are operating
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maybe in Florida. Maybe there's one in Florida and one in New York.
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Does that make sense? So it's asking questions like that.
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Or if you have an identity that is pulled forward because that
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there's a possibility that it was synthetically created or something doesn't
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seem correct, looking at the date of birth. Does the date of was,
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was that date of birth,
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does it make sense in proximity to how long the company has been around?
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Maybe there was an ownership change. So then it might make sense.
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We have to ask questions,
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was the social security number issued in the last five years?
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But this is somebody who we would've expected to have had credit for many years
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prior to that that could indicate that there's digging we need to do.
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Those are just some of the things that we need to be looking for and monitoring.
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And a machine can't pick up those types of questions.
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So we need to make sure that we're looking into those.
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Another situation might be knowledge-based questions.
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Going back to that Frankenstein identity, ,
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if they created that identity,
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they can answer their knowledge-based questions because they created themselves.
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Yeah.
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So we need to be careful in those types of situations where if a lender or a
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sales person or credit person comes to us and says what they passed their
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knowledge-base questions. Yes,
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but there's these other pink flags that we need to dig into. So it's, it,
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you can kind of talk yourselves in circles and that's where we really need to.
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You can't look at any one of these pink flags in the silo.
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We need to pull them together and we need to do it quickly and efficiently
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because that customer is waiting for a decision.
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And if we're not speedy but accurate,
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that's going to cost us one way or another.
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Yeah, it's a really good point.
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And it kind of even ties into what we were talking about before is the customers
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are waiting, we've got to be speedy, but we got to be cautious. You know,
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the velocity thing I think is really interesting as well.
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It's almost as you were saying that the first thing that came to mind was like,
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trust but verify. You know, whether it's verify, is it a false positive?
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Is it real? You know, AI can help out a little bit.
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Detect some of those patterns, but there's still no substitute for that person,
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for the individual. Mm-Hmm . You know,
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AI is only going to be able to do so much.
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Like I even heard the other day that that fraudsters or that somebody
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came across an instance where a fraudster was using AI to generate just
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like, sort of like what you said around the knowledge-based questions.
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They can answer that stuff,
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but they were using AI to generate paperwork that would be used in like an
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underwriting process and background and information.
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The thing that was really interesting about it was the thing that popped was
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what you said around date of birth and the alignment, things like that. Like,
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it's like it was something, it was something crazy.
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Like the person had to be like nine years old, you know, when they started it.
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But it's crazy. Some that wouldn't be picked up sometimes by the AI piece. It,
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you know, it might only be picked up by that human thing. Like to me, yes.
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I think this applies all the way around in many ways with AI,
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like combining AI and technology and people really makes
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that big difference. Gives a little bit of that, you know,
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the knowledge that we have and the experience like you have in terms of doing
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this stuff for so long.
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So yeah, you absolutely can't replace that institutional knowledge.
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You, yeah,
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you have to be able to use logic to anything that a
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machine has pulled forward. Whether it's machine learning,
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whether it is information from a report or whether it is from ai.
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Something you said there.
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I want to go back to something because we've been talking a lot about fraud in
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terms of, call it third party fraud people. True fraud.
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Somebody that's coming in that's making stuff up. I've, you know, synthetic ID.
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I've made a fake profile, fake company, all this other stuff. You know,
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when we talked about consumer fraud, you know, there's sometimes there's a,
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there's the first person, and you mentioned it before,
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sometimes the bust out fraud.
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It's not just a fake identity and things like that.
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It's people that are actually coming in maybe doing some loan stacking,
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you know, trying to take advantage of,
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of a situation because maybe they're a little bit stressed in terms of
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their cash flow. And they're like, look, I don't, you know, I don't,
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I'm not trying to commit fraud,
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but I'm definitely pushing the limits or you know,
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I'm definitely trying to get more money because I'm stacking up multiple loans.
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One of the things that we get questions about regularly as well is the
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difference between, again, credit reporting agency,
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the difference between score and capacity.
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Capacity has become a really hot topic.
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It's one thing to have a business score or even using the consumer score
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of, you know, make it up 600.
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But there's a big difference between that somebody that's a 600 that's doing
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$200,000 a month in cash flows and $20,000 a month in cash flows
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and what maybe where there's more debt kind of coming in and going out. I don't,
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I guess what I'm trying to figure out, and the question that we get often is,
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is there a way to be able to assess an individual's capacity to be able
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to return?
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Like are there things that you're looking for from a stress standpoint?
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Like we see it, we see it in defaults and delinquencies.
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If somebody's in default or a delinquency, they're stressed out. But how do we,
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how do you see that otherwise?
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Like are there other things that you're looking at or is that just part of the
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overall picture for you guys?
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Yeah, so again, you can't, you can't look at it in a silo. Where you,
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you touched on it a little bit, David,
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where you said there might be somebody that has a 600 credit score and they
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might pay as agreed. And they have a very, there's a very solid reason.
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Although a 660, that's a pretty low score, but it could,
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I've also seen people who maybe have a 750 score and that
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$200,000 in debt and they're struggling or they're on the brink of struggling.
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So you really, again, you can't look at that in a silo. You need to look at the,
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you need to take a very holistic review.
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And by holistic,
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I'm talking about their financial capacities and looking at both the consumer
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credit and the business credit and
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understanding do they have the capacity to pay their
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personal and their business bills? And so we're looking at their,
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we're looking at their personal credit reports and sometimes those have
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business debt that is reported on them and taking into consideration.
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Does that seem to marry up to the size of business that they state that they
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have or to the equipment that they state they're going to be financing.
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So you really need to look at that holistic report.
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And then also do they have affiliate businesses?
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And so what we're really talking about is global cash flow.
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And even without financial statements, especially in a flow type of a business,
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small ticket types of transactions,
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there's ways to at least deduce whether the
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request makes sense when you blend together. What does their,
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what do the personal guarantors, the owners of the business,
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what does their personal credit look like?
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What does their business credit look like and what does their business credit
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look like for their affiliates?
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And that's also going to help you determine do they have debt stacking?
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Do they have a lot of UCC filings that have been made?
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What does the economic economy look like in their
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particular industry, in their geographical region?
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Does that make sense? So,
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and that can help us determine both on the front end,
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does this seem like a credit risk that's within our risk appetite?
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And from a collections perspective is do
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they show signs of debt stacking there.
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There's tools that you can use that can help you determine is there debt
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stacking or is there shifts in their business score that
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we would want to pull forward to determine if we need to reach out to that
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borrower and talk to them about seasonality.
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Or if there's a payment relief modification that we would need
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to consider to help them.
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Geez, I, you know, I got to tell you it is,
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this would be a whole lot better if this was an easier process.
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I'm just telling you. It's, you know what's funny is, again,
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we go back a few years and we think about fraud. Before, you know,
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it was a little bit easier because fraudsters weren't nearly as sophisticated m
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m-hmm . As they are today and they're just getting better.
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Like your comment on the affiliates too,
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especially in a world where serial entrepreneurship is a common thing,
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and you might have people out there that have two or three or four or five
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different businesses that they're connected to,
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so that that stress component is not shoot,
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you could have one company that's stressed in another company that's a isn't it
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would be nice to be able to go and tell everybody, oh yeah, it's super simple.
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This is really easy. But to your point,
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like you really do have to look at it all.
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Yeah. And it's not that they're trying to hide anything. Sometimes you might,
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I might review a credit application where there the applying
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entity has limited credit.
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But once we start to really understand the holistic
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approach and talk to them about their business,
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we find out that well this piece of our business is a startup,
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but we have this to set established business that is willing to step up to
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the plate and do a cross corporate guarantee and then we can strengthen the
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deal. So it's not necessarily bad,
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it's these are things that we can look for because we want to try and help and
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find solutions for our borrowers. It's not necessarily fraud where it can go,
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yes, it can go to a, a situation where it's like, oh my goodness,
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we didn't realize that one company was draining from the other.
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But it can actually strengthen transactions as well. And,
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and we can find solutions for what we at,
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at the onset or on the forefront or what's initially presented to us would be
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presented as a thinner file. Yeah.
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We can find out by talking to them and having conversations or just doing due
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diligence internally that they're actually a very strong and to,
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and a very strong with strong affiliate companies.
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Yeah. So, so you've said a few times looking at the big picture,
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looking at the whole picture, looking at those things.
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As you were saying that one of the things and you mentioned the the owner a
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little bit and it clicked a little bit in that. Because when you're,
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I guess here's, here's the thing. When you're looking at these accounts,
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you're looking at these affiliates,
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you're looking at these different companies I've heard you say as well,
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understanding the whole owner. Understanding the picture of the owner itself.
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So not just the business. Like to your point,
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when you're kind of evaluating these things, are you looking at the,
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are you looking at both the owners' consumer background as
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well as the business background?
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Are you using those two together to kind of understand a little bit and make
24:46
more decisions? Mm-Hmm . I know it's a little bit nuanced.
24:48
Like we heard as an example,
24:50
one of the things that we continue to hear and is people have said that a
24:55
consumer's credit history or they tend to bring those same
25:00
behaviors into businesses that they run.
25:02
So generally good consumer behavior echoes good business behavior,
25:09
bad consumer behavior, sometimes not always good business behavior.
25:13
But I think that's very anecdotal.
25:14
Like I I don't think we've been able to validate is it true? Is it not true?
25:18
Like are you looking at those kinds of things as a part of the mm-hmm
25:20
. The evaluation process too?
25:22
Yes, absolutely. And to your point, most times one will mirror the other. Okay.
25:27
And it's not always, it's not always that that is the case.
25:31
But in most cases you do see mirroring of others.
25:34
Now when you get to a very large company,
25:36
well then it might not make as much sense to look at that whole owner
25:40
perspective where meaning you're looking at their global cash flow.
25:43
It is still a very good idea.
25:47
And we would want to encourage that when we're doing our
25:51
underwriting. But sometimes if you get to like a publicly traded company,
25:54
that's just not something that is realistic.
25:57
Or when you look at a company that is oh that where it is
26:02
a parent and that parent has multiple umbrellas,
26:05
then it might not make as large of an impact on a very small,
26:10
a very small loan in comparison
26:15
to their size.
26:16
But it is something that for most small businesses we would be looking at.
26:21
Yeah, I could see it. I was going to say it's,
26:23
I hope nobody's looking at my personal credit when they're evaluating Equifax as
26:26
a business business that the standpoint that might be a little bit awkward,
26:30
but yeah.
26:31
Well, yes, and you're not, I I don't think you're an owner of Equifax,
26:34
so if you're not the, if you're not the owner, no, even the owner,
26:38
it might be a little bit different because of the size.
26:41
I got some shares. It's fine. No, , you're absolutely right. Well, it's,
26:46
you know, it is, this to me is a really interesting subject.
26:50
Because when we talk about fraud, you know,
26:52
fraud is sometimes very it's negative. It's people are looking at it,
26:57
people truly people, bad people doing bad things.
27:02
But this is a really, like, I'm always interested in this,
27:04
this component as it relates to the owner. Because if, you said it even earlier.
27:09
If you're looking at the owner, their background,
27:11
like a change of ownership and the business mm-hmm itself,
27:14
like being able to do those little things,
27:16
I think can help determine the difference between fraud and legitimacy.
27:22
And if you can help delineate between those two,
27:24
then you kind of go down to that next layer of, okay, we think this is good.
27:29
We think that people are good. What can they really do? What's the capacity?
27:32
You know, how do we look at these things? You know,
27:35
together we do see a lot of those trends with our consumer data and our
27:39
commercial data together. So it seems to be lining up and you know, it's, again,
27:44
it's look.
27:45
So I know we're coming a little bit towards the end and so I want to,
27:49
I think we might be able to close some things out.
27:51
because The and I think we kind of alluded to it a little bit, is, you know,
27:54
maybe we could look forward a little bit, you know,
27:57
break out our crystal ball because ai,
28:02
future fraud, all those things that go along with it. Like,
28:07
is there, if somebody, if you were,
28:10
if somebody were to come to you and ask you and say, hey Jill,
28:13
I need to do something today that's going to make,
28:16
that's going to help me in six months, 12 months, 24 months from now,
28:20
what would you recommend to them? Is there a true future proof option?
28:24
Is there something that somebody could be doing that solves that problem?
28:28
Or is it just an ever evolving world that we're living in? Right. Oh.
28:30
It's always going to be ever evolving.
28:33
And my crystal ball broke years and years ago. .
28:37
Fair enough.
28:37
I keep trying to fix it, but some of the things that I would say is we,
28:41
we need to use to conversations that we've had both today and in
28:46
prior conversations,
28:47
we need to continue to use our resources and rely on each other and
28:53
use both machine learning as well as human
28:58
intervention and apply logic to them. You can't,
29:01
you can't look at either one in a silo.
29:04
And we need to continue to develop and to train
29:09
internally and externally.
29:10
We rely a lot on training because I only know that something seems
29:16
fraudulent or that from a credit perspective that something seems risky if
29:21
somebody brings it forward,
29:22
if it's not a transaction that I'm working on myself.
29:26
So we need to make sure it's great if I have the knowledge,
29:28
but we need to educate internally,
29:31
our team members and externally within our communities so that they understand
29:36
what, what trends to look for. What to monitor.
29:39
How to bring those forward and to feel comfortable bringing those forward.
29:43
We'll kind of close things out. I just want to, again,
29:45
thank you so much for joining us. I really appreciate it.
29:49
If anybody has questions. If anybody wants to reach out to you,
29:51
what's the best way for them to be able to get a hold of you?
29:54
Yeah, absolutely. You can reach out to me on LinkedIn. It's,
29:57
Jill Molitor and you'll find me under the Sterns Bank header. Perfect.
30:01
So absolutely would love to connect.
30:03
Very good people. Again, it's been a pleasure knowing you these,
30:07
these past several years and I look forward to,
30:09
to doing a lot more with you in the future. So thank you, thank.
30:12
You.
30:13
And to our audience out there if you guys enjoyed the topic,
30:17
if you have more questions, anything like that,
30:19
please listen to our other podcasts. Visit our market insights.
30:24
If you have questions, reach out to our advisors@equifax.com.
30:29
We would also love to talk to you.
30:30
We'd love to have a deeper conversation with you around fraud and verification
30:35
and all the things in each other,
30:36
challenges that you might be facing in your commercial portfolios.
30:39
In the meantime, thank you again for joining us.
30:43
The information and opinions provided in this podcast are intended as general
30:47
guidance only, and are subject to change without notice.
30:50
The views presented during the podcast are those of the presenter as of the
30:53
date.
30:53
This podcaster recorded and do not necessarily reflect official positions of
30:58
Equifax.
31:00
Investor analysts should direct inquiries using the contact desk box on the
31:04
investor relations section@equifax.com.