Dec. 10, 2025
Fraud is a Tax on Everyone w/ Ryan from Bilt
Ryan Hunter (Director of Identity & Fraud Strategy at Built Rewards) joins Eli to unpack how fraud is evolving fast — from synthetic identities to first-party scams and global fraud rings that operate like real businesses. They dig into why legacy ID systems keep failing, where the biggest vulnerabilities live today, and what it will take to actually shift the playing field.
Chapters
(00:00) Ryan’s path into fraud strategy
(05:53) How fraud evolved: fake IDs → synthetic → first-party
(10:21) Why SSNs are a broken identifier
(16:38) First-party fraud, credit washing, and detection gaps
(23:29) Fraud’s hidden cost to every consumer
(25:11) Review decisions: phishing, recovery, mule accounts
(29:47) Why fraud stays interesting + geopolitics of fraud rings
Key takeaways
- Fraud hits everyone through higher rates and product costs
- Synthetic + first-party fraud now outpace classic third-party fraud
- Weak national ID infrastructure still fuels modern attacks
- Mule accounts and phone takeovers are major failure points
- Fraud rings are organized, coordinated, and often state-level
- Stopping fraud would unlock huge UX gains across industries
Follow us on Socials
Ryan's LinkedIn: https://www.linkedin.com/in/ryanrussellhunter/
Eli's LinkedIn: https://www.linkedin.com/in/eliwachs/
Check out Footprint: www.onefootprint.com
00:00 - Ryan’s path into fraud strategy
05:53 - How fraud evolved: fake IDs → synthetic → first-party
10:21 - Why SSNs are a broken identifier
16:38 - First-party fraud, credit washing, and detection gaps
23:29 - Fraud’s hidden cost to every consumer
25:11 - Review decisions: phishing, recovery, mule accounts
29:47 - Why fraud stays interesting + geopolitics of fraud rings
WEBVTT
00:00:00.239 --> 00:00:01.360
Fraud is a tax on everybody.
00:00:01.360 --> 00:00:04.878
Can you explain why it's actually impacting them and why actually raises costs?
00:00:05.679 --> 00:00:13.679
Basically, when people are determining what an APR for a line of credit and their pricing risk, the portfolio is being priced into that as well.
00:00:13.679 --> 00:00:15.839
And so every portfolio will have fraud losses.
00:00:16.480 --> 00:00:18.160
People may say, why do I care about fraud?
00:00:18.160 --> 00:00:19.920
I'm not running a lending company.
00:00:19.920 --> 00:00:21.039
It's not impacting me.
00:00:21.600 --> 00:00:27.199
The cost of things goes up because of how much people have invest to prevent the bad guys from winning.
00:00:27.199 --> 00:00:36.640
If you turn off your fraud rules for sometimes as short seconds, you will get hit with fraud because people are always basically pen testing your system.
00:00:36.640 --> 00:00:40.000
APRs would be lower because delinquency would be lower.
00:00:40.000 --> 00:00:41.520
People could price risk way better.
00:00:41.520 --> 00:00:48.000
If it wasn't for fraud, the user experiences can be so If it wasn't for such prevalent fraud, we can do so many things.
00:00:48.560 --> 00:00:51.039
This week, I'm very excited about our guest.
00:00:51.039 --> 00:00:55.679
Uh Ryan, I've known for some time by now.
00:00:55.679 --> 00:01:01.439
He has uh been kind enough to sit through footprint We've gone on walks in the park.
00:01:01.439 --> 00:01:09.120
I've always said that I think identity is a romantic uh And we've walked around Washington Square Park in different seasons.
00:01:09.120 --> 00:01:22.239
More so he is one of the sharper people, truly, who I've met who has not just been in this space for a long time, but I I've always really appreciated is that I think you're open to meet with new companies.
00:01:22.239 --> 00:01:30.239
And normally you're you're probably at the top of my of people who will put me onto new tools that I had not heard of.
00:01:30.239 --> 00:01:31.840
Normally I feel it to the other way around.
00:01:31.840 --> 00:01:39.519
But you I think you bring like a very healthy skepticism just from many years in this phase of what can actually be done not.
00:01:39.519 --> 00:01:40.799
So we have a lot to cover.
00:01:40.799 --> 00:01:43.680
I'm really excited, but maybe to start off, do you want to yourself?
00:01:44.480 --> 00:01:46.078
Yeah, well, thanks for that that introduction.
00:01:46.078 --> 00:01:48.480
Uh I agree, fraud very romantic.
00:01:48.480 --> 00:01:52.239
Uh and uh romance scams are in.
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That's true.
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I love meeting with uh with new vendors uh just to kind of what people are working on, sort of see new approaches.
00:01:58.640 --> 00:02:03.120
And uh, like you said, like you never know what what fraud you're gonna need in the future.
00:02:03.120 --> 00:02:04.718
And so the only way to do that is to take meetings.
00:02:04.718 --> 00:02:07.599
So um yeah, I'll go ahead and introduce myself.
00:02:07.599 --> 00:02:11.840
I'm Ryan Hunter, I'm the director of identity and fraud over here at Built Rewards.
00:02:11.840 --> 00:02:20.800
I've been uh in the fraud and identity space for about 10 Started my career over at Upstart, which was a peer-to-peer lender.
00:02:20.800 --> 00:02:23.598
Now it's uh rebranded as an AI lender.
00:02:23.598 --> 00:02:26.000
They went public in 2020, I believe, 2021.
00:02:26.000 --> 00:02:34.560
Um and and then I joined Deserve in before that, but I joined Deserve uh and I was there for six years.
00:02:34.560 --> 00:02:39.919
Uh Deserve did uh credit cards as a service, and I also ran fraud and and identity over there.
00:02:39.919 --> 00:02:42.318
And now over here at Built, kind of doing the same thing.
00:02:42.318 --> 00:02:55.360
It's a little bit of a different space, kind of doing stuff around onboarding, but kind of uh looking at different sorts of fraud around the loyalty and points and gaming and uh you know login and all kinds of different events.
00:02:55.360 --> 00:03:02.400
So kind of looking at all sorts of different aspects of the of the call fraud life cycle for all the way from but throughout.
00:03:02.400 --> 00:03:08.400
So yeah, it's been it's been a fun career, and I couldn't asked uh for a better career to fall into.
00:03:08.960 --> 00:03:10.080
Now let's talk about that.
00:03:10.080 --> 00:03:13.360
Growing up, were you were you doing escape rooms?
00:03:13.360 --> 00:03:15.438
Uh did you watch Sherlock Holmes?
00:03:15.438 --> 00:03:18.960
You know, uh when did you first start getting interested?
00:03:18.960 --> 00:03:24.960
Like what skills do you think that you you always had up that's like this this could come together pretty nicely?
00:03:24.960 --> 00:03:34.960
But and then when you got to upstart, was it a you're in lending and they're like we need someone to work on or was it I want to join a company that will let me work on fraud?
00:03:35.520 --> 00:03:39.120
I didn't I don't know if anything from my childhood really prepare me for this career.
00:03:39.120 --> 00:03:41.680
What was your first childhood memory, Ryan?
00:03:41.680 --> 00:03:43.280
Oh god, I don't know.
00:03:43.280 --> 00:03:43.919
Good question.
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Great question.
00:03:44.800 --> 00:03:47.520
It was not nothing fraud related, I I don't think.
00:03:47.520 --> 00:03:56.639
But uh yeah, I think that when I was uh when I was at Upstart, I sort of started working there as basically in operations.
00:03:56.639 --> 00:03:59.360
And I was fresh out of college.
00:03:59.360 --> 00:04:02.400
I was just taking sort of like the first job I could get.
00:04:02.400 --> 00:04:08.960
I studied finance in college, but I wanted to, I took off after college to travel and took the first job I I got back.
00:04:08.960 --> 00:04:23.600
And I was working there and I uh was quickly promoted doing a couple different things, and there was I kept I kept recognizing that certain loan applications had fake licenses on them.
00:04:23.600 --> 00:04:29.360
And the way I was sort of recognizing this was because in high school, a lot of my friends had fake IDs.
00:04:29.360 --> 00:04:31.759
Not me, but a lot of my friends did.
00:04:31.759 --> 00:04:42.079
And because of that, I was able to recognize what a fake ID looked like, and I was sort of able to catch fraud that and I quickly was put as one of the early uh members of the fraud team.
00:04:42.079 --> 00:04:43.838
So that's kind of how I fell into it.
00:04:43.838 --> 00:04:48.720
I sort of fell into the lending side of the lending world, then I sort of fell into the fraud world.
00:04:48.720 --> 00:04:58.560
And I guess maybe you said what sort of things from my maybe having friends that had fake IDs, that was probably the biggest uh preparation for for a uh a career in fraud.
00:04:59.278 --> 00:05:09.439
Yeah, it I guess that is every not everybody's, but a lot of people's first introduction to uh identity theft as they grow up.
00:05:09.439 --> 00:05:12.480
Uh first party fraud, you could say.
00:05:12.480 --> 00:05:17.120
Um it's become over a decade in the industry.
00:05:17.120 --> 00:05:26.879
Have you seen sophistication evolve from your pop fake ID to get into a college bar to more sophisticated?
00:05:26.879 --> 00:05:33.120
Uh what's that and what's that been like in terms of you know, we talk about arms races.
00:05:33.120 --> 00:05:36.079
Uh we talk about it in let's say sports.
00:05:36.079 --> 00:05:38.959
Of your rival goes out and gets big players, you need to.
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The fraudster versus fraud team one's interesting in that not as open.
00:05:43.360 --> 00:05:51.360
It's the fraudsters are professional and they're spending and they will figure out what they want to do next, and you have to respond very quickly.
00:05:51.360 --> 00:05:53.680
What has that been like over a decade?
00:05:54.720 --> 00:06:00.399
I feel like when I first started, the the fraud schemes have gotten way more sophisticated with time.
00:06:00.399 --> 00:06:09.278
And so you used to see stuff, and some of these like fraud still have similar things where it's like, oh, you know, if you flag the IP for a VPN, that's like high risk.
00:06:09.278 --> 00:06:16.079
It's like, I haven't seen a fraud ring using a VPN that a VPN on an IP address in years, right?
00:06:16.079 --> 00:06:26.079
The fraud rings are all using super sophisticated, you know, residential proxies and and sophisticated networks like So they're hiding their true IPs and and they're you can't tell where they're coming from.
00:06:26.079 --> 00:06:29.040
So I I think that things have gotten way more sophisticated with time.
00:06:29.040 --> 00:06:35.519
Back in the day, it was all third-party fraud when I first very little, you know, synthetic identity fraud.
00:06:35.519 --> 00:06:47.838
And then it sort of shifted when I started maybe that or so, 2017, when I started noticing synthetic identity I'm sure it existed before then, but that's really when noticing it.
00:06:47.838 --> 00:06:52.240
And and then, of course, vendors came into the space to sort of solve that problem.
00:06:52.240 --> 00:06:59.278
And you don't see very much synthetic identity fraud, at being a problem anymore in the lending space, primarily.
00:06:59.278 --> 00:07:05.519
I mean, most people, you know, there's ECBSV, there's really good vendors that can help you, you know, stop synthetic fraud.
00:07:05.519 --> 00:07:24.959
And so then in the last maybe call it four to five years, I definitely think in the lending space it's shifted more first party fraud, which has uh just gone absolutely rampant in the industry because it's extremely hard to detect, very hard to stop, and uh it's it's hard to tell, it's hard to it before it happens.
00:07:24.959 --> 00:07:29.278
It's sort of like a minority report, I think of it like crime, right?
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Where it's like we're trying to, we're trying to, the we're trying to you know figure out who these people are on the way in.
00:07:34.879 --> 00:07:39.439
And it's usually not shown in their credit data because purposely obfuscating it.
00:07:39.439 --> 00:07:41.759
And but yeah, that's sort of I sort of seen this transition.
00:07:41.759 --> 00:07:54.319
And of course, we see the fraud rings today, the third-party fraud rings, are doing extremely sophisticated things with phone numbers, uh, you know, uh, and just sort of like where their devices and things like that.
00:07:54.319 --> 00:07:58.959
So you see a lot of really sophisticated things now that know if they were happening back in the day.
00:07:58.959 --> 00:08:00.560
I certainly wasn't recognizing them.
00:08:00.560 --> 00:08:03.759
Uh now, now I I can recognize them a little more easily.
00:08:03.759 --> 00:08:07.199
But yeah, those that's sort of how I've seen the evolution on.
00:08:08.079 --> 00:08:09.360
Can we go back four years ago?
00:08:09.360 --> 00:08:11.519
You said you noticed a rise of synthetic fraud.
00:08:11.519 --> 00:08:13.278
How do you notice that?
00:08:13.278 --> 00:08:22.399
Because it it is that, you know, you go back and you look the people who committed fraud and you say we reached out for more information, they couldn't provide a driver's license.
00:08:22.399 --> 00:08:32.480
Synthetic fraud for those who don't know, it's essentially me taking some of my actual information and then Ryan's social security number, made up social security number, creating an account somewhere, nurturing that into the bureaus.
00:08:32.480 --> 00:08:36.879
They're now tools like you said that do some pretty math to figure out what's synthetic.
00:08:36.879 --> 00:08:41.600
But how do you, as someone on a team, look at your say, oh, this is synthetic?
00:08:43.038 --> 00:08:43.038
Yeah.
00:08:43.038 --> 00:09:01.678
So a lot of the times, especially when you're thinking SSNs, uh, you can tell the SSN was either issued prior to date of birth, which is an obvious massive tell, um, or that the individual identity has multiple SSNs associated with So those are all easy tells that you can figure it out.
00:09:01.678 --> 00:09:06.320
But of course, in 2011, the SSA started randomizing social numbers.
00:09:06.320 --> 00:09:11.038
So when you start seeing uh randomizing social security it means one of two things.
00:09:11.038 --> 00:09:19.038
It means they either got their SSN issued after 2011, which means they're probably a uh you know, a recent immigrant or something like that.
00:09:19.038 --> 00:09:34.080
Or uh on the flip side, it could be that the individual is this randomized social security number to try and get And so that's really in 2011 is when you saw synthetic really s fraud really start.
00:09:34.080 --> 00:09:39.440
But I didn't notice, I didn't even notice a thing until 2016, 2017.
00:09:39.440 --> 00:09:58.399
And, you know, as of I will call it 2020, it's maybe not a problem, but you know, there's authoritative sources with ECBS via, which is electronic consent-based SSN which is basically where fraud vendors, credit issuers can direct connections with the SSA to sort of validate SSNs.
00:09:58.399 --> 00:10:06.558
And so we've seen that uh I the just the prevalence of social security uh synthetic identity fraud has really gone down.
00:10:06.558 --> 00:10:10.960
They still attempt it, uh, but you don't see it in lending much.
00:10:10.960 --> 00:10:15.759
Uh you know, obviously you still get onesie twosies, but don't see it as as prevalent.
00:10:15.759 --> 00:10:20.480
And that's because of these tools that that exists in the today.
00:10:21.200 --> 00:10:36.720
Now you bring up a pretty interesting point around before you could somewhat guess what an SSN would be associated Can you expand on that a bit more and how I think people may think that the SSA and SSNs are these great sources, but they're almost a bit too predictable from a fraud
00:10:37.600 --> 00:10:37.600
Yeah.
00:10:37.600 --> 00:10:51.918
So SSNs were issued uh basically the first five digits of SSN, you can basically tell where what state and what year was issued within a certain level of tolerance.
00:10:51.918 --> 00:10:56.639
And you know, they're they're basically issued in tranches you can see where it was.
00:10:56.639 --> 00:11:13.120
So like you if you know that you know when I was born and I was born and where my SSN was issued, you can sort of when my SSN of like the first five digits within a reason, It's less, it's harder to work backwards, but if you give me the first five, I can tell you where it was from.
00:11:13.120 --> 00:11:24.480
So basically we have uh, and then in 2011, they stopped doing that because the social security number was never supposed be this sort of private identifier for people.
00:11:24.480 --> 00:11:27.918
The social security number was just supposed to be a so you can collect social security.
00:11:27.918 --> 00:11:32.158
And so no one used to get their SSNs until they got a job.
00:11:32.158 --> 00:11:39.678
So my mom, for instance, didn't get an SSN until she started receiving a paycheck in her whatever late teens or something like that.
00:11:39.678 --> 00:11:42.879
Whereas now there was a change that was made before.
00:11:42.879 --> 00:11:51.360
So the I believe it was you cannot claim uh a deduction for children unless you have an SSN associated with that person.
00:11:51.360 --> 00:11:57.360
And so that was a change that the uh that the tax authority made, I believe.
00:11:57.360 --> 00:12:09.200
And when they did that, millions of children, I'm putting it in quotes, millions of children disappeared because parents were claiming they had all these children uh to claim the deduction and dependence uh on their tax returns.
00:12:09.200 --> 00:12:19.600
And so that is now that's why SSNs are issued right when born, because parents want to get that deduction on their So that is sort of like a little history of the SSN.
00:12:19.600 --> 00:12:37.120
Uh and you know, it basically now that all the SSNs are when they're given out, uh, we don't know where they're they could be anywhere, which is probably a good thing for But it's also makes it so it's harder to tell when SSN was or if this truly belongs to the individual.
00:12:37.120 --> 00:12:43.278
Because before it was like, oh, this person is 35 and the was issued in 1960.
00:12:43.278 --> 00:12:44.639
Well, that's impossible.
00:12:44.639 --> 00:12:49.840
That cannot have happened, which means there's a fraud, something like that.
00:12:49.840 --> 00:12:52.399
Um, so that's all changed with the randomization.
00:12:53.360 --> 00:12:54.399
It's a really interesting history.
00:12:54.399 --> 00:13:02.158
I mean, it it's a good case study of I think the other are intentional where they issue a national ID number, want it to be an identifier.
00:13:02.158 --> 00:13:07.678
The SSM was never supposed to be that, but everybody's tried to force it into that bucket.
00:13:07.678 --> 00:13:08.480
Exactly.
00:13:09.038 --> 00:13:24.000
They and this was all because there was such a pushback assigning numbers to citizens, I believe, which is funny then they're like, okay, we're not gonna give citizens like a number, and then they just made this social security that.
00:13:24.000 --> 00:13:29.278
So it really was like counter counterproductive, and it was never most supposed to be secret.
00:13:29.278 --> 00:13:31.440
They used to send it out on a postcard.
00:13:31.440 --> 00:13:37.440
Uh, they still send it in a really terrible, like little piece of paper, but at least it's in a sealed envelope.
00:13:37.440 --> 00:13:43.200
In the past, they used to send it just on a uh open like uh postcard where anyone could see it.
00:13:43.200 --> 00:13:44.879
The mail carrier could look at it, etc.
00:13:44.879 --> 00:13:47.918
They don't obviously don't do that anymore, but it never meant to be private.
00:13:47.918 --> 00:13:49.840
It's also not a unique number.
00:13:50.639 --> 00:13:52.399
In that there are some people that have the same SSN.
00:13:52.399 --> 00:13:54.879
I think I don't know that to be.
00:13:54.879 --> 00:13:55.519
Is that true?
00:13:55.519 --> 00:13:56.399
I didn't know that.
00:13:56.399 --> 00:13:57.678
I believe I believe that's true.
00:13:57.678 --> 00:14:02.798
They stopped, they they issued we'll have our footprint fact checkers go back and film this.
00:14:03.200 --> 00:14:05.678
I want to be at the bottom if a little disclaimer.
00:14:06.320 --> 00:14:06.960
We'll we'll do a disclaimer.
00:14:06.960 --> 00:14:17.759
I believe that they stopped at some point, they made a where they would no longer do this, but to your point at beginning, it was less of a unique identifier.
00:14:17.759 --> 00:14:20.320
So I believe there are some overlapping SSNs.
00:14:21.278 --> 00:14:24.639
And also something funny about SSNs, they used to just give them sequentially.
00:14:24.639 --> 00:14:32.639
So, like my dad and his siblings have the sequential SSN, that doesn't exist anymore, obviously.
00:14:32.639 --> 00:14:37.278
But uh, I just think it's funny that like back in the day they didn't really care, they would just give sequential SSNs.
00:14:37.278 --> 00:14:42.158
Um Yeah, I have a funny story about social security numbers, actually, from my first job.
00:14:42.158 --> 00:14:50.399
I used to work on the phones in my very first few months and a woman called and she said, I have like sort of this story.
00:14:50.399 --> 00:14:51.759
Looking back, was this a scam?
00:14:51.759 --> 00:14:55.678
I don't know, but let's just let's just suspend disbelief a little bit and just pretend it's real.
00:14:55.678 --> 00:14:57.840
Uh that she wasn't trying to scam me.
00:14:57.840 --> 00:15:01.120
She said, I have been a victim of fraud.
00:15:01.120 --> 00:15:04.240
My social security number was leaked.
00:15:04.240 --> 00:15:07.519
It was leaked all over the the country.
00:15:07.519 --> 00:15:12.080
I had hundreds of credit applications taken out of my name, thousands of attempts.
00:15:12.080 --> 00:15:15.200
She said, I froze my credit, it would get unfrozen.
00:15:15.200 --> 00:15:26.798
People got married under my SSN, they filed taxes with my She said it was so bad that I have petitioned, I think it her senator or something crazy.
00:15:26.798 --> 00:15:35.278
I petitioned my senator, and he had to petition the Social Administration to reissue my SSN to get a brand new SSN.
00:15:35.278 --> 00:15:44.158
So she said, I have credit history associated with this and I have no credit history associated with this new Is it a real story?
00:15:44.158 --> 00:15:44.798
I don't know.
00:15:44.798 --> 00:15:49.519
But I like I think it's a very interesting one where you can get a reissued Social Security number, perhaps.
00:15:50.399 --> 00:15:53.519
Now, and the footprint fact checkers have gone back to me.
00:15:53.519 --> 00:16:01.918
And it so before 1972, they were manually SSNs were issued.
00:16:01.918 --> 00:16:07.200
So there were a couple thousand that were duplicately due to clerical errors.
00:16:07.200 --> 00:16:12.558
Once they went to a computerized system in 1972, they no have this issue.
00:16:12.558 --> 00:16:18.558
But they found that about 0.01% of SSNs issued before 1972 dupes.
00:16:18.558 --> 00:16:21.600
Uh which is yeah, which is a real number.
00:16:22.080 --> 00:16:28.000
That's probably why the sequential SSNs happened, because just going down the list of their whatever, their their block of numbers.
00:16:28.000 --> 00:16:32.558
And if you if you get your kids at the same time, you they give you sequential uh SSNs.
00:16:32.558 --> 00:16:36.399
You would be able to guess the person in line in front of the SSN.
00:16:36.399 --> 00:16:38.080
But kind of exactly.
00:16:38.080 --> 00:16:39.600
Yeah, there you go.
00:16:40.080 --> 00:16:41.840
Now, let's talk about first party fraud.
00:16:41.840 --> 00:16:48.879
Uh you say that that's the biggest thing you're looking at From let's take an outside perspective, somebody may following two things.
00:16:48.879 --> 00:16:49.918
I'm curious how you respond.
00:16:49.918 --> 00:16:56.480
The first is, Well, how are you supposed to know this is This is psychology, there, this is an identity.
00:16:56.480 --> 00:17:06.798
And the second is, well, that's unfortunate, but you can then offboard the person, and you know, if you've conned third at any theft, at least they're only gonna hit you once.
00:17:06.798 --> 00:17:08.480
So that's not ideal, but it's one time.
00:17:08.480 --> 00:17:09.759
How do you respond to those claims?
00:17:10.400 --> 00:17:23.200
I think that so it is very hard to determine who is doing It is, I will say, like geographically concentrated often, to certain high-risk areas.
00:17:23.200 --> 00:17:32.480
It's also you can sometimes get some indication from reports about things that are not credit related, but kind of may indicate that.
00:17:32.480 --> 00:17:37.038
Uh, and also from consortium fraud vendors where you of see some of the stuff as well.
00:17:37.038 --> 00:17:38.798
So I'll say that there are signals.
00:17:38.798 --> 00:17:44.558
I'll say the signals aren't as strong as maybe the fraud signals are today.
00:17:44.558 --> 00:17:52.960
I don't know if that's because it's such a new, relatively type of fraud, uh, or if it's because they're purposely to obfuscate it.
00:17:52.960 --> 00:18:00.160
I mean, all this information is known at the at the bureau level, right?
00:18:00.160 --> 00:18:01.278
Like I'll give you an example.
00:18:01.278 --> 00:18:05.759
Like um, this was a like a credit washing case I had.
00:18:05.759 --> 00:18:08.318
And could you could you explain what credit washing is?
00:18:08.318 --> 00:18:23.440
Yeah, basically, like people were uh this person in uh this is a previous job, was uh basically they had trade lines and they were disputing these trade lines as even though they were almost certainly not.
00:18:23.440 --> 00:18:33.200
And they would, while you dispute a trade line, that trade while it's being researched by the issuer, is deleted credit report.
00:18:33.200 --> 00:18:45.519
So basically, they say you have a 90-day delinquent trade Someone comes in and they dispute that trade line with the The issuer, the credit bureaus will remove it while the is taking place.
00:18:45.519 --> 00:18:54.318
The e-oscar, which is the technology layer between the credit bureaus and the issuer, goes to the issuer and says, like, this person's disputing it for fraud.
00:18:54.318 --> 00:18:55.920
Do you accept or not?
00:18:55.920 --> 00:18:59.920
And between the time, that falls off your credit report.
00:18:59.920 --> 00:19:03.200
So what people are doing is basically disputing all their and then applying for credit.
00:19:03.200 --> 00:19:08.798
Their credit looks really good because they have no and then they go through and they go through with like a 700 FICO.
00:19:08.798 --> 00:19:14.160
Then when those things get added back in, the next month have a 550 FICO, which is probably their true risk.
00:19:14.160 --> 00:19:27.440
And I had this case where I had this person, I forget exactly the details of it, but I had never seen a credit report like it where they had like a FICO exclusion score, like a 9003 or something like that.
00:19:27.440 --> 00:19:31.759
But they had like indications that they previously had had trade lines.
00:19:31.759 --> 00:19:39.358
And I called a friend at a credit bureau, and the cred guy at the credit bureau is like, look, I'm gonna tell you off the record.
00:19:39.358 --> 00:19:42.480
Uh I'm not gonna say which credit bureau this was, uh, friend.
00:19:42.480 --> 00:19:44.160
Uh I'm gonna tell you something off the record.
00:19:44.160 --> 00:19:46.160
You should not lend to this guy.
00:19:46.160 --> 00:19:51.680
He's he has disputed every single trade line he's ever given over the last twenty five years.
00:19:51.680 --> 00:19:56.318
He's never kept a trade line, none of it's fraud, they're falling off.
00:19:56.318 --> 00:19:59.598
He's probably litigious, you know, like suing the issuer or whatever.
00:19:59.598 --> 00:20:01.200
Or threaten, threatening whatever.
00:20:01.200 --> 00:20:03.278
He's like, you should definitely not lend to this guy.
00:20:03.278 --> 00:20:06.640
Uh won't say what we did with that one, but that was a job.
00:20:06.640 --> 00:20:16.880
Uh and that was just kind of an interesting case where uh the bureaus can see all the disputed and deleted trades, but the issuers cannot.
00:20:16.880 --> 00:20:17.920
For good reason, right?
00:20:17.920 --> 00:20:24.880
Like that's a that's a protection for customers where, if someone steals your identity, you can delete that and it won't impact you on a go for it.
00:20:24.880 --> 00:20:25.838
It's it's meant to be good.
00:20:25.838 --> 00:20:34.798
But it's being abused by first-party fraudsters to delete and manipulate their credit profiles to sort of uh you know get access to credit.
00:20:34.798 --> 00:20:43.759
And on a larger scale, it's like, you know, these bust-out rings where they're just have no and they're just getting with no intent to pay back.
00:20:43.759 --> 00:20:55.759
Uh and you know, they have very strong credit profiles, typically will get very high credit lines, and then they'll just uh first party uh or first payment default without ever even looking to pay the line.
00:20:55.759 --> 00:20:56.960
So we see that a lot.
00:20:56.960 --> 00:20:58.160
Uh I used to see that a lot.
00:20:58.160 --> 00:21:06.880
And again, like we don't do that kind of stuff is you know, we have a co-brand with with Wells, it's not really my neck of the woods anymore, but that's what I used to see a lot of when at my last job.
00:21:07.440 --> 00:21:12.000
Now you bring up an interesting point around things that well-intentioned and taken advantage of five fraudsters.
00:21:12.000 --> 00:21:16.480
I feel as a result, we often, when we think about building have to do the inverse.
00:21:16.480 --> 00:21:22.960
One example I'll give is we often have customers say, hey, are fat fingering an SSN.
00:21:22.960 --> 00:21:26.000
Why can't you tell them that like it's off by one?
00:21:26.000 --> 00:21:40.160
And we say, well, there are definitely a lot of people that that's good for, but like there are people who would abuse and figure out, oh, I'm one away from getting somebody's So we have to build a worse experience for fraudsters as a How how much do you think about those trade-offs?
00:21:41.038 --> 00:21:41.440
Oh my god.
00:21:41.440 --> 00:21:47.680
I was just laughing about how I would just use that to or some, you know, you could enumerate someone's SSZ pretty easily.
00:21:47.680 --> 00:22:00.078
But yeah, the uh it's it's all it's very unfortunate if there wasn't fraud, uh lending credit APRs would be lower because delinquency would be lower, people could price risk way better.
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