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Shaping What’s Next in Credit Reporting · Market Pulse

0:02
Welcome to the Market Pulse Podcast from Equifax,
0:05
where we break down the latest economic and credit insights to help you navigate
0:09
today's business landscape.
0:14
Welcome to Market Pulse. I'm Clayton Collins, the CEO at HousingWire,
0:18
and I'm genuinely glad to be back in this podcast host seat
0:23
talking about credit reporting. One year ago,
0:25
something very special happened at MBA annual.
0:28
I had the opportunity to sit down with leaders from all three nationwide
0:32
consumer reporting agencies to talk about, candidly,
0:36
what's happening in credit reporting in the mortgage and housing industry.
0:40
The conversation really resonated.
0:42
It kicked off a lot of conversations on the expo hall floor,
0:45
but also a lot of conversations that followed the event,
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conversations about credit data, scores,
0:52
and what these three organizations actually do and how they're designed to
0:56
compete. So today, we're doing it again, podcast 2.0,
0:59
and the timing really couldn't be more relevant.
1:02
It's been a pivotal year for mortgage and housing in the credit space,
1:06
credit cost, score wars,
1:08
and now a renewed push to buy merge via the FHFA that was all
1:13
over the news in the last several weeks and months.
1:16
The role these NCRAs play in helping housing professionals help
1:21
responsibly get borrowers into the,
1:23
into homes and at the closing table has been a really front and center topic.
1:27
And there's a lot for the four of us to unpack today. So,
1:31
please let me introduce today's guest. Joining us from Equifax,
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we have Joel Rickman; from TransUnion, Satyan Merchant; and from Experian,
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Michelle Bodda. Joel, Satyan, Michelle, thank you all so much for joining me.
1:46
Thanks, Clayton. Thanks for having us. It's my pleasure. Good to be here again.
1:49
All.
1:49
Right. So three leaders from three fierce competitors.
1:53
You all have showed up again because we know we add value to the industry and we
1:56
have these conversations. So let's jump right in.
2:00
So when we sat down a year ago for the first version of this conversation,
2:04
we talked about what had changed in the past year.
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So let's revisit that question.
2:09
What has changed in the last year in the credit reporting space?
2:14
And I'd love to kick off with Michelle.
2:16
Thanks, Clayton, and thanks again for having me back. You know,
2:19
when we had this conversation last year,
2:21
the industry was still preparing for the transition to more modern credit
2:25
scoring models and that use more currently available data.
2:30
What's exciting about where we're at now is we're not talking about it anymore.
2:34
Now that current data gets to prove its value through consumption and use in
2:39
those more modern scores, and the data gets to speak for itself.
2:43
Vantagescore 4.0 is available for loans that are securitized through Fannie and
2:46
Freddie, giving lenders greater choice, allowing broader,
2:50
more modern data to play a role in mortgage decisioning.
2:52
And we're seeing the industry embrace these newer scores. Some originators,
2:56
even saying so publicly, sending out press releases. That's exciting,
3:01
moving from policy to practice. So since last year, we've also taken a look,
3:06
Experian, at how consumers are feeling about all that.
3:09
And in a recent survey that we did,
3:11
we learned that people are paying attention and moving to better and more
3:15
inclusive data and more modern scores may very well become the next big
3:19
competitive differentiator for lenders. Just real quick,
3:22
we found that 41% of people are aware that mortgage lenders now have
3:27
access to newer score models that incorporate things like rent and utility
3:31
payments. And one third, 33%,
3:33
would actively seek out a new lender if they learned the current one was relying
3:37
on old approaches.
3:38
People want to be recognized for a more complete financial picture,
3:43
and they deserve to be.
3:44
That makes perfect sense. Satyan,
3:48
we hear people talk about the three credit bureaus often,
3:50
and they've definitely been in the headlines that are housing in our newsroom
3:53
has covered a lot of the press releases this year,
3:56
but there's still some perception out there that three bureaus may mean
4:00
redundancy.
4:01
Can you talk a little bit about how that is true or is not true and the shared
4:05
mission?
4:07
Yeah, sure. Thanks, Clayton.
4:10
I'll probably add a little bit to what Michelle said too.
4:12
I think these go hand in hand. So I think, well, first I'd start with,
4:16
with the way you framed that question is,
4:18
is really interesting because there is a misconception, I think,
4:22
out there that there's an end game for all the bureaus to look the
4:27
same, to have, you know, exact replica of the database. And, and I think the,
4:31
the answer is that it really couldn't be further from the truth.
4:34
The three bureaus are not copies of the same database.
4:37
We compete and our teams compete fiercely every day to develop distinct
4:43
relationships with data furnishers, data sources,
4:46
the analytics and the products that are derived on top of or off of that credit
4:51
data. And so, you know,
4:53
examples could be an auto loan for a consumer might be furnished, you know,
4:57
to the TransUnion bureau while a retail card might be furnished to, you know,
5:02
the Equifax Bureau only and, you know,
5:05
maybe there's a rental payment that's furnished to the Experian Bureau, right?
5:10
And so we are starting to see and while there isn't any sort of comparison of
5:14
the data, because again, it's all proprietary,
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it's our competitive advantage just like Michelle and Joel's
5:21
the more dispersion of data, the more variety, as Michelle mentioned earlier,
5:26
of data sources, I think the more different these bureaus are going to be.
5:30
All that said,
5:31
the shared mission is to help ultimately qualify borrowers reach closing and
5:36
while
5:38
doing that protect their information support a safe and sound
5:43
mortgage and housing finance ecosystem and really help qualify and get as many
5:48
consumers into homes in a responsible way.
5:52
I think what's really important to understand is a lot of conversation about
5:56
scores this year as Michelle talked about,
5:58
score modernization driven by the FHFA, which I think was a great move,
6:02
but that score is all driven by the underlining data,
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whether it's a VantageScore, a FICO score, FICO 10T, whatever score it might be,
6:09
it's all about that underlying data and that data,
6:11
the more rich and broad it gets, the more powerful that score becomes.
6:15
Yeah, I'm really happy you brought up the differentiated and in some ways com-
6:19
competitive data. We're going to come back to that in a really deep way,
6:23
but before we move forward into the data conversation, Joel,
6:26
I want to give you a chance to one, say hello,
6:29
but also talk about the role the bureaus play in helping qualified borrowers
6:33
actually get to the closing table, which is the mission of this whole industry,
6:37
help get the right people into the right houses.
6:41
Absolutely. And, and first,
6:42
I just want to reiterate the thank you for everybody getting together and
6:45
spending time here. When I started in this role a few years ago,
6:47
I never thought we would talk so much publicly about the role of credit scores
6:51
and credit bureaus that we do,
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but there's clearly still a lot of opportunity to educate and delighted to be p
6:57
art of that conversation.
6:59
I would build a little bit in regards to looking back in the last year on the
7:05
fact that I just want to amplify what Michelle said in regards to what's been re
7:08
ally exciting to go online and see folks on LinkedIn show the difference between
7:13
the three credit scores or bureaus or the difference between a legacy score
7:18
and a new score that's using more data, and then seeing CEOs from, you know,
7:23
some of the largest lenders out there talking about how that's helping get more
7:27
people qualified and them to win business. That's what it's all about,
7:30
is getting people qualified.
7:32
So our role is working as diligently as possible to make sure that we're
7:37
providing the fullest picture of the consumer because we really have obligations
7:40
to both sides of the equation.
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We have obligations to getting that consumer qualified for the good credit
7:46
history they have, the accurate credit history we have,
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but we also have obligations to the American taxpayer and the financial
7:53
institutions from a safety and soundness perspective to make sure that that full
7:57
view of the customer is readily available.
8:00
But the simple fact of the matter is that not every financial institution does
8:05
want to contribute to all. And, you know,
8:06
two quick real examples that we experienced inside of our own environment and s
8:11
ome of our employees,
8:12
we had one person that was going to get requalified and they had a mortgage
8:16
through a credit union that was only being reported on Michelle's bureau.
8:21
And so if that was left off and they went to apply for a refi somewhere
8:27
it's possible the loan, without all three reports,
8:30
the loan that they were refi-ing wouldn't even show up on a credit report,
8:33
which is a big miss if we left that out.
8:36
Same scenario as we've had folks that have auto trade lines that missed
8:42
payments.
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And so you have one bureau that's got that trade line that the credit score is
8:46
40 or 50 points lower with that credit bureau than the one that's missing or the
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ones that don't have that trade line,
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so you're getting a partial view of that customer.
8:57
So it's all of us working together to try to get that picture.
9:01
And we all wish we had 100% coverage,
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but the reality of the situation is despite trying to do that,
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we haven't been able to get everyone to want to contribute to each.
9:11
But that's just the first part of the story.
9:13
The second part of the story that we are all working diligently to do to help
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get people qualified is the VantageScore opens the door,
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as does FICO 10T, to more data and using that data to make a better decision.
9:26
So each one of us,
9:28
if you were to go back and study press releases from the last year,
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all three of the bureaus are working as hard as we can to get additional data
9:34
resources, additional partnerships,
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and get a broader view of the customer so that that expanded view
9:42
gets used to paint the best picture,
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to provide the best chance of qualification,
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the best chance at the right LLPA table rate structure,
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and provides the safety and soundness for the overall environment.
9:55
I couldn't agree more with what Joel just said. I also think we're,
10:00
none of us are talking about more data for the sake of more data, right?
10:04
You know,
10:05
the mortgage process is complex and innovation should make it more efficient.
10:09
So the question becomes, how can we help people move through the process easier,
10:14
whether it's the consumer or whether it's the loan originator,
10:19
and how do we get the right data into the originations evaluation at the right
10:23
time? That's what all of us are talking about.
10:25
How do we make sure we represent the consumer fairly and help the originator
10:30
underwrite loans that are going to be productive for them long-term?
10:33
That's the problem that we're solving for.
10:36
Yeah, I at the sake of piling, I think it's such a great point, Michelle,
10:40
because there's, as we think about it, there's the core underwriting,
10:43
the risk assessment, which we've been speaking a lot about and, you know,
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the score is the front door into that. But you know,
10:51
the industry actually has been taking advantage of a lot of this breadth we've
10:54
talked about over the years with trended data,
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which all three of the bureaus have supplied in the TriMerge credit report for,
11:01
for now over a decade. But this idea of also the workflow optimization,
11:06
again, I like what you said, Joel,
11:08
just go back and look at the press releases from each of us, right? We're,
11:10
we're not only adding data for depth in terms of trade lines,
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we're adding I would say additional data pieces that may,
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may or may not be used in the underwrite,
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but they're definitely useful in helping an efficient workflow,
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a workflow that would save a lender costs you know,
11:27
by maybe having a preview of some data upfront having a preview of
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alternative data on the credit file.
11:34
So it's far beyond just simply what goes into a score,
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what goes into an underwriting model.
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It's the entire tip-to-tail workflow where the, again,
11:43
the bureaus compete and we are proud of it when we innovate and put a press
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release out,
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but all three of us have been doing that for not just this past year,
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but for many, many years.
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In some cases,
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I feel like I'm the one that's behind at Equifax in the mortgage space because
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other,
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other verticals in auto and consumer finance and loans and all of that have been
12:03
embracing all of this additional data at a much richer level for a number of
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years. So it's not necessarily new,
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it's finally being enabled inside of the mortgage ecosystem.
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So as each of you pursue more data sources and,
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like, you know, try to fill gaps with trade lines,
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is a game of whack-a-mole where there's so many new entrants and, like,
12:27
data providers coming into the system that, like,
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even with a full force effort at bringing in more data,
12:34
there's still a new provider out there that is going to you know,
12:38
create differentiation between what the respective bureaus are able to provide?
12:43
I think we just look at the financial services market as a whole and think about
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the innovation in the industry over the last, I don't know, decade or so.
12:51
Yeah.
12:53
Fintech lenders, many of the big lenders out there didn't exist 10,
12:56
15 years ago. Right. And, and no matter who this, who that furnisher is,
13:01
they, they start somewhere, and it's, I don't know how common,
13:04
I think it's kind of rare to start off the bat to furnish all three, right?
13:09
Often they furnish to the,
13:12
to the bureau where they have an initial relationship with and it could take y
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ears, if not decades, to kind of disperse that across all three bureaus.
13:20
And again, in the financial services industry,
13:23
there's just so much more innovation on things like buy now, pay later,
13:27
there's further digitization of rental which creates the opportunity of more
13:32
rental trade lines, telco, utility on, so on and so forth. So, so yeah, Clayton,
13:37
I think you're spot on, which is it probably gets more diverse and
13:42
I think whack-a-mole is,
13:44
I use that more on the fraud conversation if we get to that,
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but I think this is more of just, like, the growth of the industry. And it.
13:50
Used to be maybe the credit unions and the banks, and then,
13:53
and the auto lenders,
13:54
and now it's just so many more lenders that could contribute.
13:58
Yeah, I think it's clear how more data and not having a single bureau
14:03
view can benefit the consumer, because they,
14:06
you may be missing key credit lines that would allow them to get approved for a
14:10
mortgage product, but there's also the lender side of the equation.
14:14
How are a multi-bureau view or more data sources
14:19
helping lenders identify and manage risk inside of their lending businesses?
14:25
Well, I would jump in real quick on that and just say part of it is that the
14:28
additional data gives a greater view.
14:30
So if you think about if I am a new consumer and I maybe have two credit cards
14:35
and an auto loan, and that's all I've ever had that's on my credit report,
14:39
I also post - most likely paid rent. And so even as a new consumer,
14:44
I've got rent payments that are flowing in,
14:46
I've been paying utilities that can be captured from a number of different ways
14:51
and potentially I've done buy now, pay later,
14:54
or other transactions that have been talked about here. As you pull that in,
14:58
that now gets added, that gets treated inside of the scoring models,
15:02
and that gets treated as data elements that can make a better decision.
15:07
So what that does is it helps folks have a better view and more
15:12
trade lines and more credit rich. It also makes a big difference,
15:16
and Satyan referred to this earlier about trended data.
15:19
That is not a minor aspect by any means,
15:22
and that makes a really big difference - Right.
15:24
In some folks' credit worthiness, is that it is not uncommon.
15:28
I think we all know of examples where somebody went to apply for a mortgage and
15:32
maybe had some scratches and dents on their credit report, and they were told,
15:36
"Hey, go fix these things, pay off this payment, clear that,
15:40
and come back to us in, in six, seven, eight months, unfortunately." Well,
15:45
trended data is helping getting that picture quicker.
15:47
So people that started that now,
15:49
their scores are improving as they clear those debts and show that r
15:53
esponsibility change,
15:54
and that accelerates the opportunity for them to participate in the housing
15:58
market.
15:59
Yeah,
15:59
one of the things I have certainly come to learn over all of my time in this
16:03
industry is that a thin credit file,
16:05
a thin traditional credit file does not equate to a thin financial life.
16:10
And having access to the data that everybody's talking about here today helps us
16:14
see that more clearly. And like I said earlier, it's not that...
16:17
It's not more data for the sake of more data,
16:19
it's to measure the consumer more accurately,
16:22
to fill in the parts of that financial picture that the traditional file or a
16:26
legacy score may not fully capture. Joel just brought up rent.
16:31
We've done some analysis on that too, and it works on the risk side.
16:36
So not just as a benefit for the consumer, but also for the originator,
16:40
a renter with three or more negative events,
16:42
like a late payment in 24 months is four times more likely to default on their
16:46
mortgage.
16:47
So these types of data can have a real impact for both the consumer in a
16:52
positive way,
16:52
giving them representation of how they're living their lives and everything
16:55
they're paying, and also for the lender not only to approve more loans,
16:58
but to maybe avoid some risky ones.
17:01
That's such a fabulous saying, Michelle,
17:04
a thin file does not necessarily - Yeah. Represent a thin financial life. Right.
17:08
Are underwriters prepared to look at that trended data, understand that, like,
17:12
this new age borrower who may have a thin file but does have a financial history
17:17
and bring that into the underwriting process?
17:19
Yeah, like Satyen said,
17:20
trended data has been used in the mortgage industry for close to 10 years now,
17:24
where we're looking at how people actually manage, let's say,
17:28
their credit card debt, for example.
17:29
So someone who charges just about everything on their credit card to get the
17:34
points or something along those lines, but pays it off every month,
17:37
depending on the reporting cycle into the bureaus,
17:40
it may show a big balance and a smaller, you know, like, debt to limit ratio.
17:44
But through trended data, you can see that they pay it off every month,
17:49
and they're not somebody who's carrying a balance and paying the minimum payment
17:53
and going to be carrying that debt long-term. Without the trended data,
17:56
which the mortgage industry used to have to do,
17:59
and the legacy scores couldn't calculate,
18:01
these new scores can calculate the fact that Michelle's paying off this big
18:06
balance every month. There's no risk there. Instead of someone else who's, like,
18:10
carrying the debt, they used to look the same, but they don't anymore.
18:14
Now, with these modern scores, again, there's many of them coming to market,
18:17
which is fantastic. That example that Michelle gave where, you know,
18:23
somebody might be a payer, maybe in a trend of paying down their debt,
18:26
it could result in a higher score,
18:27
and the implication there for both the lender and the consumer is possibly a
18:32
lower rate, right, looking at the LLPA tables if their score is higher.
18:35
That's a direct impact, which again, as Michelle said, with the legacy scores,
18:40
not able to capture all that breadth of the data. And again,
18:44
going back to making sure that the comprehensive view,
18:48
so across all three bureaus,
18:50
they get the lender gets the accurate read with the score that is most r
18:55
epresentative of that consumer's credit ends up being a possible way for
19:00
a better impact for borrowers. Joel, you mentioned earlier,
19:04
there's been some public press from some of the biggest lenders out there who
19:06
have reported, since using one of the more modern scores recently,
19:11
that they actually are able to save consumers more money by using those scores.
19:14
So the evidence is already coming out.
19:16
Yeah. And Clayton,
19:17
I would say that there's a couple of things to keep in mind here.
19:19
Remember that the score currently is just used for the final pricing with the
19:24
LOPA tables. There was a couple of articles out that Director Pulte shared
19:29
a couple weeks ago,
19:30
as well as a study by Bank of America that talked about the Fannie score and how
19:35
they look at it. And the fact that it's very,
19:38
very predictive and an extremely quality score, and,
19:41
and I agree fully with that. I won't speak for anyone else in the call today,
19:44
but I personally agree with it because I know that goes into that is all three
19:48
credit bureaus and all of the trended data and income information,
19:53
employment information,
19:54
and equity information about the property that's being purchased. So you know.
19:58
That's right. The asset, you know the credit history,
20:02
you know the income potential,
20:04
and you have that data from a verified sources in each front that now when you
20:09
do a score on that, the accuracy of that goes way up. And that's why, you know,
20:13
Fannie and Freddie have a very complex modeling system to look at
20:18
all of that data before they make a final decision on buying that loan or not.
20:22
Yeah. And,
20:23
and that score is tuned for mortgage delinquencies or the mortgage industry.
20:27
I mean, it's really innovative, frankly,
20:29
in what FHFA and the GSCs have been doing with credit data over the last decade
20:33
or so. I think score is sort of the next leg up, the score modernization.
20:37
But I think clearly the GSCs understand the value of the data.
20:41
Yeah. Joel, I'm glad you brought up the, the Fannie score and Freddie score.
20:45
We've seen Director Pulte tweet about these.
20:47
Can you help me better understand and,
20:49
and practice how those GSE score models compliment or are used
20:54
in relationship to Vantage and, and FICO? Like what role do they play in the,
20:59
in the risk assessment, underwriting, like, credit quality process?
21:04
So I would think about it in a simplistic term. If you think about...
21:07
I refer to it as the black box, and I'm not an expert on all this.
21:10
A lot of people try to teach me every day, and I learn a little bit each time.
21:14
But the black box,
21:15
if you think about putting your three credit reports in there and verified
21:18
information and the property data and all of that,
21:22
they're using all of that data to make a credit decision.
21:25
And so that credit decision,
21:27
they're then applying a score model to all of those components that comes
21:31
together as a score. When you think about a Vantage score or a FICO score,
21:36
it is on the single bureau itself.
21:38
So it's looking at the one bureau and the information in it and what is
21:43
available to it, and that's what's generated.
21:46
And so the difference is the amount of data that's being pulled into the
21:51
black box, I refer to it.
21:53
But the big change here is people don't know what that score really relates to.
21:57
That's why what was so innovative and progressive about Director Pulte's tweet
22:01
was that he's going to now make that transparently available so that investors c
22:05
an start understanding what that score actually looks like compared to the b
22:10
orrower and how that operates. And,
22:12
I think that's going to change things a year from now,
22:15
18 months from now in regards to how the overall ecosystem thinks about
22:20
scores and looks at it from that perspective.
22:23
I mean, that's been one of the,
22:24
the talking points that I've heard from industry executives is like, all right,
22:27
well we can innovate on the origination side,
22:30
but what will capital markets providers credit and that, and,
22:35
and consider valuable? Firstly, I don't...
22:37
Do the Fannie and Freddie score get passed along with a loan as it's sold into
22:41
the capital markets?
22:42
That was the announcement that they want to make that more transparent. Now,
22:46
just to, to kinda close out and then, and please ask a different question.
22:50
But if you think about itto where the ve - the real value of the score d
22:55
oes occur is right now, and, and this varies a lot, but right now,
22:58
Fannie and Freddie make up about 40% of the total loans being originated because
23:03
we've seen a shift - Yeah. To just different.
23:05
And so there is still a great value of having that first credit report
23:10
with a credit score to understand,
23:12
does Joel look like a prime borrower or does Joel look like a subprime borrower?
23:16
And I'm going to need to go through different channels to try to get him ap
23:21
proved with different programs or organizations.
23:23
So there's still a value and will always be a value,
23:26
but when you think about Fannie and Freddie specifically,
23:29
what they're doing from a score perspective is the most inclusive combination
23:34
of data elements.
23:35
That makes sense. Well, I will move us along,
23:37
but I'll keep us on the topic of scoring models.
23:40
So each of your companies has supported greater competition
23:45
in the market than credit scoring with, with VantageScore.
23:48
We just talked about the different models and trade line data,
23:52
but I'd like to go a little bit deeper on differentiation. So Satyan,
23:56
can you lead us in here about kind of what makes your data contribution
24:00
distinct at TransUnion?
24:03
Yeah. And so I would say, Clayton, that it really comes down to,
24:09
you know, the,
24:09
the 300 million American consumers the credit files that are available for those
24:14
consumers and the use case of it. But depending on the use case,
24:18
the consumer file that we have may, again,
24:21
it may include rent utility payments, telco,
24:25
the unique lenders that we talked about earlier,
24:28
whether it's a FinTech or a credit union furnishing to TransUnion or TransUnion
24:33
only. Earlier this year,
24:35
we added to the mortgage credit report our factor trust alternative
24:40
lending data. So these are like short-term loans. And again,
24:43
this becomes a signal that a lender can use in their workflow, right?
24:48
Joel made a great point, right?
24:50
Like an early look to take a look at what's going on with that
24:55
consumer. ,
24:57
Lenders can use that to make decisions on how to maybe you know,
25:02
which loan officer to put a particular borrower in, you know,
25:06
is it a high touch consumer, or is a high touch application? Is it automated?
25:10
So that's like one example of how TransUnion differentiates.
25:15
I'm sure Michelle and Joel can also share many examples from their side.
25:19
I think what's really important though is, you know,
25:22
there's a differentiation of the data,
25:24
and there's also a side here that is around the,
25:28
the stewardship that we all are obligated to,
25:32
and it's kind of the core of each of our companies, right? So I just think of,
25:38
you know, credit score is like the very tip of an iceberg that represents the
25:43
consumer and really what the bureaus do.
25:45
The credit report might be just like the other part of the iceberg that's above
25:48
the surface, but underneath is, again,
25:51
the work that our teams do day in and day out around staying regulated,
25:56
accuracy, security, permissible use cases,
26:01
consumer protection, consumer operations.
26:04
I think that's a big part of the contribution as well that, again,
26:07
each of us do because it's our obligation and we're the stewards of this data
26:11
and stewards of helping consumers get get into a home that makes sense for them
26:16
or get a mortgage at least.
26:18
So that's a lot of what I would say is our contribution.
26:21
Michelle?
26:22
Consumers' financial lives have changed,
26:24
especially over the last several years and
26:28
dramatically post the pandemic era.
26:30
Technology has changed ways for us to be able to permission and analyze data,
26:35
and you see that lenders are wanting that. One of,
26:38
some of the stuff that Experian's done,
26:39
we've definitely made a ton of investment around rental, income, and employment,
26:43
alternative financial services, buy now, pay later, and cash flow.
26:48
We actually just yesterday announced the Experian Cashflow Data Bureau because
26:53
we believe so strongly in the power of credit and cashflow data and what it
26:57
means to the future of lending.
26:59
And we know that the GSEs and the mortgage market are absolutely looking at
27:03
cashflow and how consumers manage their financial lives.
27:07
So when we think about mortgage underwriting specifically,
27:10
some of those things are the new frontier, but what's exciting about it today,
27:13
I'm just going to pull us back in on it a little bit,
27:15
is that these newer models are actually capable of ingesting more of that
27:20
data that we've invested so heavily in,
27:23
and consumers can have more of their financial lives reflected through those
27:26
decisions that matter, both in the tip of the iceberg with the score,
27:30
like Satyan was talking about,
27:32
and in the next level of the top of the iceberg in the actual elements of the
27:36
data.
27:37
The Cashflow Data Bureau is a topic that some lending executives asked me about
27:41
as we prepped for this conversation.
27:43
So can you go a little bit deeper there on that launch?
27:46
I'm coming in the fresh ears.
27:48
Yeah. It's super new for us too.
27:50
We just launched it yesterday and it's really an exciting and advancement in how
27:55
we can use consumer permissioned data,
27:57
their bank data on how they're actually paying bills and all of that.
28:00
We've created an entire set of attributes out of it so that we can see how a
28:05
consumer is managing that kind of daily inflow and outflow of money and
28:10
cash and savings and everything that they're doing.
28:12
So we've created an actual data bureau out of it.
28:15
It's completely permissioned and able to be used and we're super excited about
28:19
it. Yeah.
28:20
So does that, like the data bureau,
28:22
the cashflow data bureau product kind of sit outside of the credit report? It.
28:27
Sits right alongside the credit report is the way I would think about it.
28:30
Like not necessarily in what Experian calls our file one database,
28:33
but inside of a data bureau where we can use it for all of the permissible uses.
28:37
Okay, awesome. Joel, you want to jump in on differentiation?
28:40
Absolutely. Thank you. So Clayton,
28:42
the problem that we started looking at a couple of years ago is in 20 and 21
28:47
when we had the refi boom and everybody was happy in the industry and things
28:52
were going very, very well,
28:53
we were converting as an industry maybe one and two applications,
28:57
some were even doing better than that. In today's world,
28:59
we talk to a lot of lenders that are converting one in 10, one in 12,
29:03
and I swear this quarter very well may be one in 15 or 18 with what we're seeing
29:08
on the data. That causes a problem. I mean, there,
29:11
there's no way to put it any other way.
29:14
The cost of all of that sales and engagement and qualifying a customer
29:19
and understanding their financial wherewithal is expensive and,
29:22
and it's difficult.
29:23
And so where we chose to differentiate was using two other data repositories
29:27
that we have in Equifax.
29:29
One is the work number and the other is a database of cable, telco,
29:33
and pay TV data.
29:34
And so what we've done is provided some elements from the work number to
29:38
basically give the last couple of years of jobs and what their annual income
29:43
was. So you could get a litmus test of whether that individual has the income to
29:48
qualify for a job.
29:50
And then we've provided attributes from the other database to help you score w
29:53
hether that individual is current or not and pays their,
29:57
their monthly recurring bills.
29:59
And so We're trying to solve that problem of how do we help in the qualification
30:03
early without having to buy all of the data,
30:05
which Satiad's been talking about this call, and he's absolutely right.
30:08
That's one of the biggest pain points in credit and mortgage today. And,
30:12
and we've chosen to take that approach with providing some of those elements f
30:16
rom our other data repositories with the credit report.
30:19
Honestly,
30:19
the answers on differentiation are way more differentiated than I think a lot of
30:24
the industry professionals would understand.
30:27
Yeah, you know, I think this is, it's worth reminding that you know, we've been,
30:30
obviously this conversation about mortgage. But the mortgage industry, I think,
30:36
you know, gets some drag or some benefit from all of the other industries that
30:41
in different ways push the bureaus based on kind of where their lane is or their
30:46
expertise, right? You know, I think things like cashflow,
30:51
probably other industries are ready, more ready to to take that on. And so,
30:56
you know, what Michelle talked about,
30:57
the mortgage industry will be benefiting from that. The short-term lending data,
31:00
there's personal lenders have already adopted that a bit more, but again,
31:04
mortgage lenders are taking the benefit.
31:05
So it's important to remember that the three companies here,
31:08
we compete in different segments, in different verticals.
31:12
It's not even only lending. It's, you know, we have, each of us each have
31:17
non-financial services business units that telco and other business units like
31:21
that, right? And so it's
31:24
applying the benefit of that data in different workflows,
31:28
in different underwriting, in different scoring models that you know,
31:32
we're moving forward to. And again, I think the FHFA has shown innovation,
31:38
opening the door to modernizing scores opens the door to taking advantage of all
31:41
this data that frankly, the three of us have had for quite some time.
31:45
And every other lending line, right? My other hat is auto. The,
31:49
the conversation is far less about scores and far more about data.
31:54
And many, many auto lenders use data,
31:56
even though there's no mandate from all three of us because they,
31:59
they really want that comprehensive view of what the consumer has.
32:04
I would add onto that, just I think that's a great example, Seiteon.
32:07
And one of the things that we have noticed in the auto vertical specifically is
32:12
the number of lenders coming and saying, "I need two and three euros now." Yes.
32:17
Because, you know, the,
32:18
what's interesting is AI is going to be an amazing tool and it's going to
32:22
provide great benefit to the industry in many, many ways.
32:25
But the group that's really benefiting from it right now is all the fraudsters
32:29
trying to figure out how to fake documents and fraudulently generate a
32:33
financial view that maybe isn't accurate.
32:36
And so by getting data from multiple providers,
32:39
you have a much higher probability of sorting out some of those
32:44
bad actors in the space.
32:46
We understand the regulatory and capital markets complexity of,
32:51
of the housing industry, which is why, you know,
32:53
sometimes things in technology and, and data move a little bit slower.
32:57
But if the other credit categories, I'll keep using auto as an example,
33:01
were ahead of where housing is now and data adoption,
33:05
is there anything we can learn from where those other credit
33:10
categories are today that might guide to where mortgage will be in
33:15
two, two, three or five years? If we're just catching up with where they were,
33:18
now where are they now so we know where we're going?
33:21
I would say there's a couple points. Let's take bank card or retail card.
33:26
And this is public, but Synchrony Financial,
33:28
they're one of the biggest card issuers. They,
33:30
they fully switched to one of the modern scores,
33:32
Vantage Score that includes trended data and they securitize a lot of their
33:37
assets. And so I think there's, you mentioned this, Clayton,
33:41
about the secondary markets, the com- complexity there.
33:44
We've seen evidence in other,
33:46
in other verticals where a switch to a modern score can happen.
33:50
I think we have very efficient markets, capital markets in the United States.
33:54
And so the market, these are very smart people working in the capital markets.
33:58
They will trade on the pricing and on the risk very quickly, will normalize.
34:02
We see the same in auto. Many auto lenders are,
34:05
are also securitizing with alternative scores. And again, this is a small point,
34:08
but interestingly, because there's no mandate in these other verticals,
34:13
a pool of loans will have a variety of scores anyway.
34:16
It could have an older version of FICO, a newer version of FICO, a Vantage.
34:19
And so the capital markets have figured that out already.
34:22
It's not something new that needs to be figured out in mortgage.
34:26
That's one point. I'll let Michelle and Joel jump in as well.
34:28
I'm sure there's a lot of knowledge here.
34:30
I was only going to ... You, you said it. I was going to amplify the fact that,
34:33
yeah, in today's world,
34:35
there may be six credit scores being used in capital markets and auto alone
34:39
right now.
34:40
I think we did account at some point,
34:42
and it's well over 50 scores that are widely used across all of the
34:47
different markets outside of mortgage. Widely used.
34:51
There's way more than that with custom scores and everything else for
34:54
underwriting, but -.
34:55
Let me put on my, like, consumer translator hat for a second. So with 50 scores,
34:58
I know that's not housing, but like there's a lot of scoring models out there.
35:02
Consumers are hearing about tri-merge, hearing about buy merge. As a consumer,
35:07
how do I wrap my head around...
35:08
And I know these aren't necessarily my decisions to make as a consumer,
35:11
but how do I wrap my head around when my, my lender tells me, "Oh, the,
35:14
the credit report you pulled online isn't necessarily reflective of your,
35:18
the picture your underwriter will use because we use a,
35:21
a tri-merge." Like as a consumer, how does that help me?
35:24
How do I understand and what should I understand about the models that lenders
35:29
are, are using from a accuracy and, and consumer protection perspective?
35:34
Michelle, I'll let you jump in first.
35:36
I mean, I've been doing this for a while and this is one of my passion areas is
35:42
how do we work to more fully educate consumers about exactly what's being
35:46
evaluated and what they can do to manage their lives? This,
35:50
that's a grassroots effort, by the way. I mean,
35:53
we all three of our companies have massive consumer education plays that we
35:58
facilitate all the time.
35:59
Experian has relationships with 90 million consumers through our direct to
36:04
consumer business,
36:05
and we are interacting with them and helping them understand all of these things
36:09
all day, every day. There is still more work to be done.
36:13
But the cool thing about that research that I was talking about earlier is that
36:17
people are paying attention, and especially Gen Z
36:21
which I was really happy to hear. I have two
36:26
16-year-olds that are about to go to college in a couple of years,
36:29
definitely on the younger end of Gen Z.
36:31
But on the older end of that generation, they are paying attention.
36:36
Those are the ones that are saying, "I expect a lender that I work with,
36:40
and I will switch with one who's not to use the full
36:45
representation of how I manage my life." They're paying attention.
36:48
We've facilitated that as an industry, lenders, originators, regulators,
36:52
the bureaus, everybody else over the last 20 years.
36:56
But there's still more work to do.
36:59
You know, one of the thing that's interesting,
37:00
and you hit on it at the early part of this, Michelle, and just again,
37:04
want to amplify it, is the consumers are learning.
37:07
And it's both directions right now.
37:10
So there was a public post not too long ago by Bat Ishba that he talked about
37:14
the emails he got on a daily basis about loans that were being won because there
37:19
was now a different view of that consumer that portrayed a,
37:22
a more holistic view of the consumer and they were able to win loans because of
37:27
that. Now, on the flip side, we've had lenders come to us that say,
37:30
"We lost loans because this other company was,
37:34
was able to use a different score and a different process than we're using t
37:38
oday." And if anybody really was keeping track,
37:41
and it's very possible that only the three of us on this panel were keeping
37:45
track on a daily basis, Clayton,
37:46
but of what was going on with the adoption of Vantage through Fannie and
37:50
Freddie, it started with a very small select group,
37:54
but lenders were fighting at the door because of that example that I'm giving
37:58
where they were starting to lose loans to people that had the right to be in
38:02
that program and use the new scores. Yeah.
38:05
And so it is starting to get that grassroots where people are talking about it.
38:09
You go onto social media and see where brokers advertise their own services,
38:14
you're starting to see now accepting Vantage, which is interesting to me.
38:18
How that bubbles up as well.
38:19
I mean, lenders are bragging about it.
38:21
We're seeing the headlines and press releases from - Yeah, from UWM,
38:25
from Rocket, like this is like the the early adopters are proud of the work.
38:30
AI came up a little bit in this, in this last point. And I think it's clear.
38:34
We've talked about on the stages at HousingWire events that consumers are
38:37
turning to AI for better understanding financial education and
38:42
they're, they're probably asking questions about their,
38:44
their own credit score and not sure what they're, they're getting back on that.
38:47
Is AI and that, like,
38:50
that ChatGPT as resource number one changing the way consumers are
38:55
asking their lenders about, about credit?
38:57
Just going a little bit deeper into consumer education here.
39:00
I mean, I can jump in here. I think it's some of, some of my answers, you know,
39:04
hypothesis driven, right?
39:06
But it's probably a build off of what Michelle and Joel said,
39:08
which is the consistent effort. And again, I would say being the stewards,
39:13
I've used that that word before of credit education for consumers.
39:17
We kind of experimented a little bit. I've asked my team to simulate that of,
39:22
you know, what does,
39:23
what happens when you go into one of the AI models and ask about cons - about
39:29
credit and how does that, is it accurate? Is it not accurate? Does it differ,
39:33
you know,
39:33
from what might be on our website or one of the other two NCRA websites?
39:37
And I think what it leads to is consumers now have at their
39:43
fingertips or even voice at this point, somebody to quickly answer the question.
39:47
So it's,
39:48
so it continues to be imperative for us in the industry lenders as
39:53
well to not resist it, but really enable it, right? Enable that education,
39:57
enable the access to our tools and services.
40:00
I think all of us provide things like score simulators.
40:03
I know we all have mobile applications that, again,
40:06
most people now use to interact.
40:08
We all probably have some sort of AI strategy to ensure that they do
40:13
interact with the AI platforms and applications.
40:16
But doubt that that is how consumers are going to continue to interact and b-
40:21
become educated. And I think we just have to keep doing what we're doing,
40:23
which is provide the data, provide transparency, things like free scores,
40:28
free credit scores that reports that we all provide.
40:31
And so to really continue this theme of transparency, I do want to tie it to,
40:35
again, where FHFA has gone over this last year, right,
40:38
of transparency in the data and the scores.
40:40
I think that the bureaus will continue to do the same.
40:43
Okay. So we're all about to go get on planes and head to Chicago for MBA annual.
40:48
I know credit data, credit reporting will be big topics at, at this event.
40:52
So we all probably have some preparation to do to get ready for this blitz that
40:56
is our annual pilgrimage to, to MBA.
40:59
So I want to close this out with a couple rapid fire questions and I, I,
41:02
I usually like to, you know, end on a positive note,
41:04
but I want to push you guys a little bit on this first question, not rapid fire.
41:07
Had a lot of challenges in housing in 2026.
41:10
What's your biggest disappointment from our industry? Joel, kick us off.
41:15
I would love to see a more concerted effort in talking about true affordability.
41:20
Like the big rocks that really are going to change affordability for the
41:24
consumer and how we can be looking at that and working together because it's,
41:28
it's no secret that shoppers are way down from the real estate
41:33
indexes and tours and applications are way down.
41:37
And so how do we get people to be confident they can afford a house and help
41:41
make that affordable for them?
41:45
Yeah.
41:45
I think one of my disappointments is like in getting maybe the
41:50
louder voices or,
41:52
or discussion around overcoming the innovation story,
41:56
which I think has been a good one here in this industry,
41:59
has been a lot of things framed as and like or choices, right?
42:04
It's an or choice on a score. It's an or choice on a report.
42:07
It's an or choice on, you know, anything. And the,
42:09
the fact of the matter is we see this in all industries is when you open the d
42:13
oors to innovation,
42:14
the market really chooses and ends up deciding like what's the best.
42:17
And let's take AI, for instance. I'll speak for myself,
42:20
this is probably true with everyone. I use all of them. I use all of,
42:23
I use all of the different platforms because they all can provide benefit to me.
42:27
So I think that can still be true in credit, right? You can use multiple scores,
42:32
you can use multiple bureaus. So I think, you know,
42:35
sometimes framing competition as an or a zero-sum game is not really the right
42:39
way to go.
42:40
It's why don't we just open the door and continue to like highlight the
42:43
innovation and highlight the deverything we've talked about for this last hour
42:47
of, you know, the more the expansive data,
42:49
the new things that each of us have and will announce over the course of the
42:53
next week and then kind of keep going down that path.
42:57
Ooh, things to announce in the course of the next week.
42:59
Sounds like we might have some things coming up your sleeve there. Michelle,
43:02
the biggest disappointment.
43:03
Look, I agree with what my counterparts just said.
43:07
The affordability thing is a real, real problem.
43:09
I'll go ahead and say the thing that no one else has said yet.
43:12
One disappointment that I have is the continued misunderstanding of the role
43:17
that we all play in this ecosystem.
43:20
We are three independent companies. We compete vigorously every single day,
43:25
whether it's for clients or data through innovation,
43:28
through the products and the services that we bring to market.
43:31
And at that same time,
43:32
we operate in a really highly regulated ecosystem where things
43:37
like accuracy and security and consumer pro- protection
43:42
are the fundamental responsibilities.
43:44
And I think sometimes those two things get conflated.
43:49
Operating within the same regulatory framework and competing,
43:53
we absolutely do.
43:54
And healthy competition and continued innovation ultimately benefits both
43:59
lenders and consumers.
44:00
Okay. So let's, let's take a little more upbeat. Most exciting thing in 2026.
44:05
Michelle, we'll go the opposite direction this time. You start us off.
44:07
I'm glad we're letting the data and the scores speak for themselves now.
44:10
The thing I'm most excited about is that we're not talking about it anymore.
44:14
We're actually using it in real decisions. This more modern, more current data,
44:19
more modern scores are getting to be tested and proven and show the value they
44:23
can bring.
44:25
Yeah. I,
44:25
I'm very similar though here on the excitement of the mortgage industry
44:30
starting to embrace, you know, one,
44:32
the doors being open to everything Michelle just mentioned, going from idea to,
44:36
to practice. And then you know, I'm excited about,
44:39
and I think we'll see this here at MBA is the,
44:41
the lenders themselves talking about and sharing how they are using the new
44:46
scores, the alternative data, the new workflows. I mean, again,
44:51
we saw some press releases from two of the biggest lenders sharing with the
44:54
industry, like, this is,
44:56
this is how we are serving the borrowers better. And
45:01
I really like to see that because I think, again, back to your first question,
45:04
Clayton, the shared mission is to get consumers to the,
45:08
to the closing table in a safe and responsible way. And I think we and the,
45:13
you know, the three of us are coming together,
45:14
but I think the lending industry is going to come together and continues to
45:17
voice that more.
45:18
And I would say I fully agree with my two counterparts here.
45:22
So I would add to it that I feel like we are past the talking phase in many
45:27
areas of, of the scoring world. Where I think we're still in the talking phase,
45:31
but people are taking it really serious is how do we get more efficient in the
45:36
lending cycle? And part of that is the whole combination of AI, automation,
45:40
and all of that,
45:42
and better data at the right times to automate those solutions and add,
45:47
you know, a lot more capacity to to everyone's origination shop.
45:52
That I'm, I'm really excited about.
45:54
I'm also really excited about mergers and acquisitions can be interesting,
45:59
but we're seeing a change with a lot of organizations that really value their c
46:03
ustomer and their servicing book and want to understand that customer through
46:07
the entire life cycle.
46:09
And that's something that is a little new and is exciting for us to,
46:13
to see that mindset in the mortgage space.
46:16
I think M&A and consolidation is a whole nother hour conversation,
46:20
so we'll have to save that for next time. A- as we come to a wrap here as a,
46:24
as a host,
46:24
I can't help but start thinking about how do you title and market the episode.
46:29
And I'm,
46:29
I'm somewhere between thin file does not equal thin financial life or
46:36
we're past the talking phase. We're actually taking action. Some,
46:39
something in there is going to be how we market this episode. And I,
46:41
and I hope that those are the points that our guests take away from this conv
46:46
ersation and hopefully, you know,
46:47
talk to us about when we're in Chicago next week. Joel, Michelle, Satyen,
46:51
thank y'all so much for this conversation.
46:53
I learned a lot and I hope our listeners learned as much as I did.
46:57
So I appreciate you. Thank you.
46:59
Thank you. Thank.
47:00
You.
47:02
The information and opinions provided in this podcast are intended as general
47:06
guidance only and are subject to change without notice.
47:09
The views presented during the podcast are those of the presenter as of the date
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this podcast was recorded and do not necessarily reflect official positions of
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Equifax.
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Investor analysts should direct inquiries using the contact us box on the
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investor relations section at equifax.com.