March 26, 2024 • 33 mins
This episode and the following description originally come from Lockstep Consulting's Making Data Better podcast.
A key actor in risk assessment is the data provider. These commercial operations aggregate and analyze the data produced by governments, enterprises, individuals, and even other data providers. All to feed today’s insatiable appetite for understanding who it is we are dealing with online.
In this Making Data Better episode, Steve and George are joined by Cindy Printer, Director, Financial Crime Compliance and Payments, at LexisNexis Risk Solutions. The company is a major data provider to government and enterprise; Cindy focuses her work on financial services firms and their need for regulatory compliance.
We discuss the granular nature of the data LexisNexis Risk Solutions offers its customers and the breadth of sources used to meet their needs. It’s astonishing.
Cindy makes the point, one we heartily agree with at Lockstep, that risk is specific, a concern for each individual entity and that the data required by each entity varies based upon its specific concerns. And that’s why LexisNexis Risk Solutions tunes the data services it provides to the industry segment and individual firm.
Sitting on top of such vast data resources and knowing the complications associated with deriving meaning from it all, LexisNexis Risk Solutions also provides analytical services that saves an enterprise from having to analyze the data itself.
This is a great conversation if you want to understand the data provider role, the scale of its operations, and its priorities. So take a listen.
DISCLAIMER: The information provided in this podcast is for informational purposes only and is not intended to and shall not be used as legal advice. The views and opinions expressed in this podcast are solely those of the speakers and do not necessarily reflect the views or positions of LexisNexis Risk Solutions. LexisNexis Risk Solutions does not warrant that the information provided in this podcast is accurate or error-free.
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
The following episode originally appeared on Lockstep
Consulting's Making Data Betterpodcast.
You can find more episodes atmakingdatabettercom.
A big thank you to GeorgePeabody and Stephen Wilson from
Lockstep Consulting for hostingour own Cindy Prenter, for this
discussion around the role ofdata providers.
Speaker 2 (00:37):
Welcome to Making Data Better, a podcast about
data quality and the impact ithas on how we protect, manage
and use the digital datacritical to our lives.
I'm George Peabody, partner atLockstep Consulting, and thanks
for joining us.
With me is Lockstep founder,steve Wilson.
Speaker 3 (00:54):
How are you, steve?
Fantastic George, good to behere again.
Speaker 2 (00:57):
I need a quick weather report, steve.
What's the temperature downunder the least of your part of
it?
Speaker 3 (01:02):
It's 7 in the morning and it's, let's say, it's 25
Celsius.
So what's that about?
80 degrees, oh wow.
Speaker 2 (01:08):
And Cindy's in Chicago, I'm in Boston.
Yes, yes, cindy, I assumeyou're looking out at snow as
well.
Speaker 4 (01:15):
Cold temps and snow on the ground.
So yeah, we won't see 80 forseveral months here, maybe July.
Speaker 2 (01:23):
Yeah, exactly, exactly, all right, well, let's
get started.
So yes, cindy Prenter is withus Today.
She is the director offinancial crime compliance and
payments at LexisNexisRiskSolutions.
Cindy, thanks for joining usI'd confess, for joining us once
again, which had to re-recordthis since I had a technical
(01:46):
issue want to make this right.
Speaker 4 (01:48):
Happy to be here.
Yeah, thanks for having me back.
Speaker 3 (01:51):
Thank you for Kaspela .
Speaker 2 (01:53):
Yeah, for those of you who'd listened to us before,
steve and I have a pretty hardfocus on data quality and, as
part of that and I know Cindyshares the same perspective that
the risk owner, the partythat's taking on the liability,
taking on transaction risk, who,in the patch while of the
(02:14):
identity industry, is called therelying party.
They're consumers of data thatCindy's organization and many
others provide and, of course,many of these organizations,
many of our client organizations, also gather their own data
sets.
So we're really interested intalking about the breadth of
data that Cindy has at herdisposal to offer her clients at
(02:39):
LexisNexusRisk solutions.
So, but before we dive intothat, cindy, why don't you,
would you tell us a little bitabout what brought you to risk
and fraud as an industry and asa career?
Speaker 4 (02:53):
Yeah, yeah, it's a great question.
I suppose I always likedsolving problems, solving
mysteries, always sort ofenjoyed watching movies or
reading books that were mysteryfocused and not with any intent,
but I really found myself as mycareer in Eval, putting that
toward my career and using thatnow for the good of other people
(03:17):
.
Which really is what LexisNexusdoes, is we help organizations
focus on making better riskdecisions, better decisions
around opportunities, by usingdata and analytics.
So just by chance that I foundmyself using my personal
interest in my career.
Speaker 2 (03:34):
It's nice when those two things line up, isn't it?
It's kind of fun.
So Orient is a little morethoroughly, then, into what
LexisNexus does, and especiallythe risk solutions group that
you're part of.
Speaker 4 (03:47):
LexisNexusRisk solutions in particular, does
help organizations make betterdecisions in two ways both
identifying risks for theirorganizations, but also
identifying opportunities.
And we do that to use some ofyour words, george, from earlier
through our depth and breadthof data that we provide, as well
as our advanced analytics andin the risk solutions group.
(04:11):
I'm part of the financial crimecompliance group in particular.
Speaker 2 (04:15):
Help me understand who are your customers,
particularly in yourorganization.
Speaker 4 (04:21):
Sure, we have a wide variety of customers across
financial institutions, so thatwould be banks, that's credit
unions, that's just some of thefinancial services organizations
that are out there, as well ascorporates.
So we service both financialinstitutions and corporates.
We have a wide coveragegeographically.
(04:43):
We're really in 180 countries,so I think we're a little we're
everywhere, george.
Speaker 2 (04:50):
Before I turn it over to Steve and I bet you, this
question has lots of answers.
You know, I have one vision ofyou as a provider of raw data
that is consumed by yourenterprise, your large customers
, who then take it in andanalyze it themselves, versus
what I would think is asignificant amount of value add
(05:11):
on your part where you're doingthe analytics and saying to your
customers this transaction,this particular interaction,
this identification suggests youshould be careful or go ahead
and approve it.
You know, where is it the rightquestion to ask you?
Is there a balance betweenthose two in terms of what
(05:32):
you're seeing your customersasking you for?
Speaker 4 (05:35):
Yeah, absolutely.
We've made our legacy by way ofdata and so we've been
providing this very rich datafor nearly 40 years at this
point and through evolution ofour own business today and for
many years now.
But the difference between justbeing data provider to now is
(05:57):
we offer solutions as well.
Those solutions are pieces oftechnology, for example,
screening, screening solutionsso watch the screening solutions
that help identify risks fororganization, and then also it's
how we deliver that output fromthat, and that kind of comes to
that analytics piece.
Is what does that mean?
(06:17):
There's data, there's asolution to ingest that data and
then there's output.
So for us, it's that output isgoing to look a little bit
different for every organization.
So we have organizations askingfor only data, but we have
organizations that also look tous, recognize us as a leading
provider of those analytics andalso of those technologies.
(06:39):
So we really have our customersusing any variety of the data,
the analytics and thetechnologies.
Speaker 2 (06:47):
So that you can tune it based on what they're asking
for.
Speaker 4 (06:50):
It's all about the tuning, george, it is yeah.
Speaker 3 (06:52):
So Sydney?
That brings us to sort ofdesign thinking.
A lot of what we're doing inour work is trying to work out
how to make data better.
What are the questions that youneed to answer about data?
You've already used languagearound solutions and the let's
call them information productsthat you have.
Tell us more about the dialogue.
What's keeping your users up atnight and what are they asking
(07:14):
for you or from you in terms ofyour feature set and your
information products?
Speaker 4 (07:19):
Sure.
So at the heart of it, it'svery, it's integral.
It's key that the data ourcustomers use and look to us for
is credible and reliable.
So they're looking to us toensure that what we're
delivering to them is reliabledata, it's coming from a
(07:39):
credible source.
So, regardless of the problemsour customers are having, it all
starts with that credible andreliable data.
But, that said, in the risksolutions space and financial
crime compliance area, morespecifically, keeping our
customers up at night isregulatory compliance.
The regulatory landscape hasalways been dynamic.
(08:01):
It's always been a top concern.
But in recent years, given thechanges due to geopolitical
events, technologicaladvancements, various
innovations, our change, thepace of change, the rate of
change of regular regulations isat its utmost highest, and so
(08:23):
for our customers, it has alwaysbeen a concern.
But it's almost risen back tothe top, as one of the primary
concerns is assuring thatcompliance, and that all starts
with the data, the credibilityand the reliability of the data.
Speaker 2 (08:40):
How do you prove that ?
Casting no aspersions.
But just because it comes fromyour company, you source it from
somebody else.
Clearly, how do you guaranteethat provenance?
How do you guarantee?
The credibility, the currencyof the data.
Speaker 4 (08:54):
We've been sourcing data for 40 years, so our
processes are highly structured,highly standardized.
So, in short, we know what goodlooks like because we've proven
it.
We've proven it to ourselves,we've proven it to the industry.
So we know what good looks like.
So, as we analyze new data,aspects of data, we have a
(09:19):
process for testing that and wehave a process that we don't
vary from.
It's a proven process, and oneof those steps is always
comparing it against what goodlooks like.
We have the benefit of knowingwhat good looks like, and so
we're able to just constantlytest, constantly compare.
(09:40):
We have access to a lot ofsources of data, so we don't
have a problem passing on asource of data.
We deem it to be unreliable,uncredible.
But even if it's reliable andcredible, maybe just not good
enough up to our very, very highstandards.
Speaker 3 (09:58):
We are obviously preoccupied with fakes.
Not to be too negative, wethink that the data industry is
full of good things, but giventhe prevalence of fakes and
given people's concern aboutdeep fakes and generative AI but
consumer level, fraud hasalways been around how do you
know who you're dealing with?
Further up the food chain, witha lot of your sources and a lot
(10:21):
of your clients beingenterprise people, do you see
fakes at that level?
Do you have a lot of issueswith, let's say, fidelity or
genuineness of enterprisecustomers and data sources and
so on?
Speaker 4 (10:34):
Certainly our customers do.
They're the ones that see thefake data.
They are experiencing thosevery overt efforts to disguise
identity, to present an identityperson or an entity as
something other than who theyare.
(10:55):
When someone wants to fake whothey are, there are ways to do
that, and so we have a constantgoal of hoping our customers
sort through that fake data.
And that's a lot about therelevancy of the data, what's
important to organization Aversus financial institution B,
(11:18):
so on and so forth.
And so that comes with thoseanalytic value propositions that
we have, some of thetechnologies that we offer, and
that is a little bit differentfor each organization, but we
absolutely help our customerssee through that fake data
because it's very real.
Speaker 3 (11:38):
And so the dimensions of quality that your customers
are looking for.
It changes from customer tocustomer.
Speaker 4 (11:44):
It absolutely does.
Yeah, each of our customers isserving a different industry,
you know, or differentindustries.
Some look like the other, butwhether your financial
institution coming and operatingin a highly regulated
environment, or a corporate,maybe not as tightly regulated,
(12:04):
however serving a riskiercustomer base, what they're
looking for, what I riskidentifiers and what's important
to those different, you know,organizations based on the
profile of who they're serving,the regulatory environment that
they're operating in meansthey're focused on different
(12:24):
aspects of the data.
So it's our goal and ourobjective not just to provide a
product to a customer, butprovide a solution like get to
the bottom of what, what istheir problem, and then work
towards solving that problem.
Speaker 3 (12:42):
It's so fascinating because we're in one of the
tightest regulated, let's say,financial services.
We're in one of the mosttightly regulated sectors of the
world and you'd think thethings are uniform, and yet we
deal a lot with KYC.
Know your customer.
Everybody does KYC differently,yes, and they've all got their
own concerns, their own riskmanagement habits, their own
settings.
It's fascinating how muchdiversity there is in a tightly
(13:04):
regulated space.
Speaker 4 (13:05):
It absolutely is, and every customer base looks
different from your peerscustomer base and there is
opportunity to leverage,leverage practices, leverage
content.
I've got a lot of talk nowabout data.
Consortia is sharing dataacross organizations that might
be anonymized, however, thereare still indicators that can be
(13:28):
shared.
But the reality is, while thereare practices that can be
shared, the end of the day, it'sreally important that each
organization is focused on whatmatters to them.
It's not only about doing whatyour peers are doing benefit
from that but you must befocused on your own organization
and make policies and processestailored to your organization.
Speaker 2 (13:52):
I love that you're pointing at that, because it
really underscores thehistorical failure of federated
approaches that have hope to,for example, apply standardized
data sets and processes for knowyour customer on board.
Speaker 1 (14:08):
Yep.
Speaker 2 (14:09):
Where you know the risk profile of one bank is very
different from the bank downthe street.
Speaker 4 (14:15):
Yeah, never mind the credit union Right.
Yes, and those standardizedprocesses are our minimums.
They should be looked at asminimums.
This is what you must be doing,but there is more to do now,
based on your risk appetite,your customer base, so on and so
forth, what your particularproblems are that you're looking
to solve.
(14:35):
So it is important to havestandardization, but those are
minimums and should be viewedthat way.
Speaker 2 (14:41):
I can imagine that you have a lot of data that
actually endures for years andyears and years, right?
Someone's address or maybetheir phone number or their
social security number and birthnumber.
But I think that's a good thing, right, those are generally
close to life.
Everyone has them Lifetime,Lifetime thanks.
(15:02):
Just to drill a little bit, isthere data that changes
frequently that you guys have ahard focus on or you think
you're particularly good atmonitoring?
Speaker 4 (15:15):
Data changes all the time.
It all changes, quite frankly,even if someone doesn't.
Someone's social securitynumber is what it is, someone's
birth date is what it is, butpeople move, they take new jobs,
they get married, they getdivorced, they have children.
Gosh, we really took a stepback and thought about all we
each did individually in thelast month, let alone the last
(15:37):
12 months or the last couple ofyears, if you recorded all those
data points and changes in yourlife.
Data is changing constantly andso our organizations are in
you've.
You've come into George, you'vecome into that, and KYC or CDD,
customer due diligence, and, ofcourse, I've mentioned
regulations, sanctions, variouswatch lists.
(16:00):
So that's a lot of what ourcustomers are focused on is what
are the changes in regulationsthemselves?
But then how?
What does that mean for thedata that I now need to be aware
of and track and ensure thatI'm constantly maintaining it to
be accurate enough to date?
Speaker 2 (16:20):
You've got a real breadth of data available to you
.
I mean just a little little.
Look at the acquisition historythat Alan Harris has done
fairly recently and you boughtan email edge and ID analytics
and fly real and others.
Each of those companies havebeen focused on a particular
problem set.
Do you bring that data in andmake it available to other
(16:46):
subsidiaries within yourorganization?
The more data, the better.
Is sort of the defaultsupposition on my part.
Speaker 4 (16:52):
Well, now you've just complicated the question,
george.
Yeah, that a whole new dimensionto the question.
In the first instance, whenwe're analyzing a business for
acquisition, for example, itabsolutely needs to lend to the
question.
It needs to lend to the core ofwhat we do.
That's coming back to improvingthe decision making ability on
risks and opportunities, ifthere is opportunity to share
(17:16):
that data within theorganization in a highly
sensitized way.
We have parts of ourorganization that, for example,
work with governmental agencies.
That's a very secure bubble.
Nothing goes into it.
That isn't very highlymonitored.
(17:36):
So that would be sort of like arandom situation when we say no,
but, for example, I'm in thefinancial crime compliance group
and then we have our fraud andidentity group.
Absolutely, that's a groupthat's tracking a lot of digital
activity or digital identityfootprint, for example, mobile
device, laptop, any kind of appson a phone.
(18:00):
So there is opportunity forfinancial crime compliance to
marry our physical identity, ourdata, physical identity data,
with that very digital identitydata and provide a significant
value to our customers.
And so that's an example ofwhere we do.
(18:23):
We do share information.
It's anonymized in mostinstances, you know, kept in,
maybe like even a data cleanroom, and so access to it might
be tightly monitored.
So it's done in a very secureway.
But where we find opportunityand we're always looking at that
of how can we provide a betteroutcome for our customers then
(18:47):
we will find take thatopportunity to share information
or data within our organization.
Speaker 2 (18:52):
Since this culture of ours is becoming largely
digital, I imagine you anchor afair amount of your analytics.
Going forward is when the kidgets their first mobile phone
Absolutely.
Speaker 4 (19:03):
Absolutely.
I mean, think about what agenow do children?
I have nieces and nephews and Ithink they're like eight and 10
.
They don't have their phonesyet with cell service, but they
have iPads and so they're usingthose you know devices, and they
have a digital footprint.
(19:24):
Someone out there knows they'reonline.
Yeah, they're online and sothat's really an opportunity for
benefit to some of ourcustomers.
And then that's maybe a childand at that age, but even
someone that goes to collegelet's say, bump up the age a
little bit that doesn't have acredit card yet, but they do
have this digital footprint.
(19:44):
Well, how do you provide creditto someone who doesn't have
credit, like it's got to startsomewhere, may not have a parent
to co-sign for them?
This is where some of the datawere able to use and turn that
around and actually provide asituation where customers can
can really provide a greatopportunity for someone young
(20:05):
that only has a mobile digitalpresence.
Speaker 2 (20:08):
I've seen similar kind of decisioning in
developing markets, where themetadata around how a mobile
phone is used, for example, thatactually provides good
information with respect to isthis potential customer Going to
pay their bill on time?
Speaker 4 (20:25):
Yep risky or not risky Yep.
Speaker 2 (20:27):
Right which got even got down to.
Does this person charge theirmobile phone on a regular basis
or does?
Speaker 4 (20:36):
it run out of power and they have to reestablish
them yeah Never charge it to 100percent, George, that dies down
the battery earlier than itneeds to.
Speaker 2 (20:45):
I'm guilty of that.
That just means I just don'tmove around very much.
Speaker 3 (20:50):
So that reminds me of that sort of modern parenting
experience that certainly mostof us had had without teenagers,
like financial literacy, and Iremember encouraging, against my
best instincts, my teenagers tostart thinking about credit
cards and getting into thesystem and having a credit
rating before it's too late, Ihope these days, I mean, some of
(21:10):
that stuff gets creepy, george,but these days there's
certainly got to be better dataavailable to make credit scoring
for teenagers as they grow up,but that you know.
Segue to privacy Cindy, ourprivacy business at Lockstep has
got to do with design thinkingand we ask people to think about
what.
Do you really need to knowabout people to deal with them?
That sort of identity Privacyis like what is the least amount
(21:35):
of information you need to knowabout somebody to still deal
with them.
And these days in your industrythere's so much natural drive
and selection pressures to knowmore and more, so there are some
interesting tensions there.
Can you talk to us all abouthow your business looks at those
(21:55):
tensions?
To minimize data and yetminimize data in the interest of
privacy and yet get the sort ofprecision that your customers
need for risk management?
Speaker 4 (22:04):
It's not about more data, more data that's going to
enable me to make a betterdecision, a more accurate
decision.
It's about looking at the bestdata, the relevant data, and I
used that word earlier.
I don't want more data, I justcut through the noise.
I want to see the data that'simportant to me, whether that's
(22:25):
alias data, someone trying tomaster identity, or whether it's
we use that fake data, orwhether it's the real data.
I do want to see that all.
I want to have the capabilities,the analytics to poke through
what the fake data is, get tothe good data.
But I want to know that fakedata is out there as well,
because now I can identify thatperson as risky.
(22:46):
But too much data and I'm notgoing to know what to do with it
.
For us, we have an approach, wehave a scoring methodology.
It's called the Exposure Indexand it's something that we've
created and it's something thatwe enhance.
Even open source data, like awatch list data, we enhance it
(23:06):
with this exposure index thattells an organization how
relevant is this Steve Wilsonrecord to the Steve Wilson
that's in my database?
Are they the same Kind ofcommon first name, semi-common
last name?
Speaker 1 (23:21):
There's a lot of us.
Speaker 4 (23:23):
Yeah, in even George Peabody.
That might be a common name.
Mine is not so common.
Yeah, it's poking through that.
What's relevant to me?
That's one of the value adsthat we provide, but also really
important for our customers out.
There is something very, veryvaluable to them.
Speaker 3 (23:39):
We know about the enormous burden compliance
burden, reporting burden thatbanks have at the moment.
I've recently heard thatcompliance cost has tripled in
the last 10 years for businessin general and it's worse than
that for financial services andbanking.
This is all about data, isn'tit?
Suspicious matter reporting,AML obligations businesses need
(24:00):
to be really sharp in the waythat they test for suspicious
matters and detect suspiciousmatters.
What's the state of the artfrom your perspective, Cindy,
about the signals and theproducts that help people detect
suspicious matters and so on?
Speaker 4 (24:16):
It's a great point because, regardless of what data
you're looking at and reallyyou're going to be able to
identify suspicious data, ifit's entity related Something
specific like that KYC data,that CDD or customer due
diligence data, whether that beon an organization, if I
corporate or a person it'sreally utilizing that entity
(24:39):
data and looking at behaviors ofthat data.
Does this look normal?
What's normal?
I'm a college student.
Am I going to Taco Bell and7-Eleven using my money and
transferring funds digitally, oram I transferring money and
then out of Cuba or something?
Speaker 3 (25:00):
Some of this is just like it's context, isn't it?
It's context, normal variesfrom one customer to another.
Speaker 4 (25:05):
That's exactly right.
It's the data.
It's how you analyze that data.
Again, this comes back to alittle bit of common sense what
makes sense for my customer base?
That's where we see thatvariation from business to
business.
Speaker 2 (25:20):
I've worked on push payments quite a bit in my past
life.
I'm super interested in theproblem of authenticated push
payment fraud.
Of course we're seeing thatlots today with scammers
tricking people to send money tothem.
Yes, Cindy, it really intriguedme to see that you are offering
(25:43):
a service here calledConfirmation of Payee, which I
saw that title, I went.
This is really interestingbecause a lot of these payees
are bogus or they're certainlyscammers who are actually
controlled a legitimate bankaccount.
I'm very curious of what it isthat the Confirmation of Payee
(26:06):
does.
Speaker 4 (26:07):
Yes, what you're referring to is a solution that
focuses on detecting authorizedpush payment fraud.
It's that fraud, of course,being the key part.
You even mentioned it.
It's authorized.
Someone is originating thatpayment and saying I want to
send this payment to GeorgePeabody but, by the way, someone
(26:31):
is posing that they've takenover your name, but it's really
their account number on theother end.
So they've representedthemselves.
That's one situation.
Or they've just representedthemselves as charitable
organization, playing on thegood heart of someone in society
that agrees to send their moneyand turns out it's a scam
(26:52):
that's not going to charitableorganization.
No text write off for you.
We offer a solution that matches.
It basically validates theidentity of the intended
receiver and what it does is itmatches account information.
So this is always.
This solution focuses on bankaccount to bank account, so
(27:12):
account to account.
It doesn't involve its digitalpayments, but not credit card
payments, and it's all aroundverifying who is receiving.
Is that person says who theyare the owner of that bank
account on record, so there's acheck that happens in the
background with our solution.
It's called Validate and SafePayment Verification.
It's an API, it is seamless tothe sender and it's typically a
(27:36):
corporate who would license itand they've installed this API
in their payment process andthey're just running it in the
background.
It happens in less than twoseconds but what it does is it
stops a bad payment before it'sever sent.
The situation of authorizedpush payment it's someone's
authorized it and they're liablefor that money.
(27:57):
Like that poor victim nevergets their money back.
Speaker 2 (28:00):
So your service might .
For example, if a fraudster ora scammer has gotten a hold of
well, they've got a legitimatebank account.
You're checking as to whetherthat bank account matches up to
the name of the scammer?
Speaker 4 (28:13):
Yes, Yep Matches, so owner on record matching with
that bank account and there area couple other checks that are
happening because keep in mindthat the sender is also still
validating identity and oursolution does also that it
validates the bank and theaddress and some of those other
data points that are just reallyimportant to be accurate to
(28:35):
make a payment but also makesure it's going to the intended
recipient.
Speaker 2 (28:39):
I'm curious are any of the customers who are
actually using the service?
Are they giving a thumbs up tothe sender to say is past, our
checks Go ahead?
Speaker 4 (28:50):
No, it's really the opposite.
When it doesn't go through,they're going back to that
sender to say, by the way, wedidn't send this payment, it was
being sent to an unintendedparty.
And it's saving that sendermoney, but also the entity who's
also sending the money not justthe originator, but the
(29:10):
originating entity.
There's a lot of money involvedin these failed payments and
these fraudulent payments, soit's doing a couple of things.
It's not just about savingmoney, it's about stopping fraud
, it's about detecting andpreventing fraud.
Speaker 2 (29:26):
That's fraud today.
Let's wrap up with a littlediscussion about what you're
seeing as the future of fraud,and particularly with respect to
.
I'm always curious how areregulators possibly keeping up
with technology, with theevolution of fraud, now that we
have?
Obviously the poster child ofthis concern is generative AI.
That's got to be high on yourwork schedule.
Speaker 4 (29:50):
It is.
In fact, we recently issued acompany-sponsored statement
couple statements, but acompany-sponsored statement on
our use of AI, and we take itvery seriously.
There is some form of AI thathas been used in some of our
(30:11):
products for years, but thisgenerative AI in terms of a
regulated space is somethingthat must be used in a very,
very measured way.
We realize there are benefitsto it and we realize it's going
to get used in some of oursolutions over time.
I'm not here stating it will orwon't, but it's certainly an
(30:36):
aspect that LNRS recognizes andthat we look into and we're
answering to.
We want to provide the bestsolutions for our customers that
may or may not involve AI overtime.
There are other ways for us toadvance our products.
Things like I mentioned earlier, this consortia, just consortia
of data, that sharing of dataacross businesses and industries
(31:01):
, across customers, across yourpeers.
So there's a very realopportunity there.
That's just another way, not anAI-focused way, but another way
where advancement is happening.
Regulators recognize thebenefit of that, and so it's
very much supported.
Some of these are very muchsupported by regulators, and so
we try to work hand in hand withthem as the industry advances.
Speaker 2 (31:24):
So let me be clear on that LNRS is actually actively
supporting data sharing acrossorganizations and making data
networks available so that theexperience of all of these
parties can be pooled and Iassume your analytics
capabilities can be applied toenrich the overall result.
Speaker 4 (31:45):
Yes, so that is happening in some aspects of our
business, especially in some ofour digital businesses, our
digital data that we provideAnonymizing data, and it's
always anonymized, it's alwaysprotected Data privacy.
Data security is a number oneconcern for us, but there are
(32:06):
some areas of our business whereit is supported by our users.
It's a contributory model, veryaware of the fact that their
peers are contributing data aswell.
It's anonymized and it'sreshared out for the good of
stopping fraud, of preventingfinancial crimes, terrorist
financing, money laundering, andalways for that goal of doing
(32:31):
good.
Speaker 3 (32:32):
That's great to see.
We've always been strugglingwith what's essentially a
tragedy.
The commons haven't we?
With siloed businesses,competitive businesses,
understandably, the naturalthing is to hold that data close
, because data is like the crownjewels.
But more and more willingnessand more and more understanding
of that need to share experienceand know-how and intelligence
(32:54):
that's so fresh it's good tohear, cindy.
Thank you.
Speaker 4 (32:56):
Yeah, things done in a responsible way can do a lot
of good.
It's always focusing on beingresponsible about that use and
sharing of data.
Speaker 2 (33:06):
Well, we'll leave it there, Cindy.
Thank you so much for joiningus on making data better and for
coming back.
Speaker 4 (33:14):
We're really Lovely to be here, John, and then I
hope I get another opportunity.
Speaker 3 (33:18):
Oh you will.
Speaker 2 (33:19):
Yeah, let's do that we look forward to it.
Speaker 4 (33:21):
That'd be great.
Speaker 2 (33:22):
Thanks again.
Speaker 4 (33:23):
Thank you Bunu Robbre you.
The following episode originally appeared on Lockstep
Consulting's Making Data Betterpodcast.
You can find more episodes atmakingdatabettercom.
A big thank you to GeorgePeabody and Stephen Wilson from
Lockstep Consulting for hostingour own Cindy Prenter, for this
discussion around the role ofdata providers.
Speaker 2 (00:37):
Welcome to Making Data Better, a podcast about
data quality and the impact ithas on how we protect, manage
and use the digital datacritical to our lives.
I'm George Peabody, partner atLockstep Consulting, and thanks
for joining us.
With me is Lockstep founder,steve Wilson.
Speaker 3 (00:54):
How are you, steve?
Fantastic George, good to behere again.
Speaker 2 (00:57):
I need a quick weather report, steve.
What's the temperature downunder the least of your part of
it?
Speaker 3 (01:02):
It's 7 in the morning and it's, let's say, it's 25
Celsius.
So what's that about?
80 degrees, oh wow.
Speaker 2 (01:08):
And Cindy's in Chicago, I'm in Boston.
Yes, yes, cindy, I assumeyou're looking out at snow as
well.
Speaker 4 (01:15):
Cold temps and snow on the ground.
So yeah, we won't see 80 forseveral months here, maybe July.
Speaker 2 (01:23):
Yeah, exactly, exactly, all right, well, let's
get started.
So yes, cindy Prenter is withus Today.
She is the director offinancial crime compliance and
payments at LexisNexisRiskSolutions.
Cindy, thanks for joining usI'd confess, for joining us once
again, which had to re-recordthis since I had a technical
(01:46):
issue want to make this right.
Speaker 4 (01:48):
Happy to be here.
Yeah, thanks for having me back.
Speaker 3 (01:51):
Thank you for Kaspela .
Speaker 2 (01:53):
Yeah, for those of you who'd listened to us before,
steve and I have a pretty hardfocus on data quality and, as
part of that and I know Cindyshares the same perspective that
the risk owner, the partythat's taking on the liability,
taking on transaction risk, who,in the patch while of the
(02:14):
identity industry, is called therelying party.
They're consumers of data thatCindy's organization and many
others provide and, of course,many of these organizations,
many of our client organizations, also gather their own data
sets.
So we're really interested intalking about the breadth of
data that Cindy has at herdisposal to offer her clients at
(02:39):
LexisNexusRisk solutions.
So, but before we dive intothat, cindy, why don't you,
would you tell us a little bitabout what brought you to risk
and fraud as an industry and asa career?
Speaker 4 (02:53):
Yeah, yeah, it's a great question.
I suppose I always likedsolving problems, solving
mysteries, always sort ofenjoyed watching movies or
reading books that were mysteryfocused and not with any intent,
but I really found myself as mycareer in Eval, putting that
toward my career and using thatnow for the good of other people
(03:17):
.
Which really is what LexisNexusdoes, is we help organizations
focus on making better riskdecisions, better decisions
around opportunities, by usingdata and analytics.
So just by chance that I foundmyself using my personal
interest in my career.
Speaker 2 (03:34):
It's nice when those two things line up, isn't it?
It's kind of fun.
So Orient is a little morethoroughly, then, into what
LexisNexus does, and especiallythe risk solutions group that
you're part of.
Speaker 4 (03:47):
LexisNexusRisk solutions in particular, does
help organizations make betterdecisions in two ways both
identifying risks for theirorganizations, but also
identifying opportunities.
And we do that to use some ofyour words, george, from earlier
through our depth and breadthof data that we provide, as well
as our advanced analytics andin the risk solutions group.
(04:11):
I'm part of the financial crimecompliance group in particular.
Speaker 2 (04:15):
Help me understand who are your customers,
particularly in yourorganization.
Speaker 4 (04:21):
Sure, we have a wide variety of customers across
financial institutions, so thatwould be banks, that's credit
unions, that's just some of thefinancial services organizations
that are out there, as well ascorporates.
So we service both financialinstitutions and corporates.
We have a wide coveragegeographically.
(04:43):
We're really in 180 countries,so I think we're a little we're
everywhere, george.
Speaker 2 (04:50):
Before I turn it over to Steve and I bet you, this
question has lots of answers.
You know, I have one vision ofyou as a provider of raw data
that is consumed by yourenterprise, your large customers
, who then take it in andanalyze it themselves, versus
what I would think is asignificant amount of value add
(05:11):
on your part where you're doingthe analytics and saying to your
customers this transaction,this particular interaction,
this identification suggests youshould be careful or go ahead
and approve it.
You know, where is it the rightquestion to ask you?
Is there a balance betweenthose two in terms of what
(05:32):
you're seeing your customersasking you for?
Speaker 4 (05:35):
Yeah, absolutely.
We've made our legacy by way ofdata and so we've been
providing this very rich datafor nearly 40 years at this
point and through evolution ofour own business today and for
many years now.
But the difference between justbeing data provider to now is
(05:57):
we offer solutions as well.
Those solutions are pieces oftechnology, for example,
screening, screening solutionsso watch the screening solutions
that help identify risks fororganization, and then also it's
how we deliver that output fromthat, and that kind of comes to
that analytics piece.
Is what does that mean?
(06:17):
There's data, there's asolution to ingest that data and
then there's output.
So for us, it's that output isgoing to look a little bit
different for every organization.
So we have organizations askingfor only data, but we have
organizations that also look tous, recognize us as a leading
provider of those analytics andalso of those technologies.
(06:39):
So we really have our customersusing any variety of the data,
the analytics and thetechnologies.
Speaker 2 (06:47):
So that you can tune it based on what they're asking
for.
Speaker 4 (06:50):
It's all about the tuning, george, it is yeah.
Speaker 3 (06:52):
So Sydney?
That brings us to sort ofdesign thinking.
A lot of what we're doing inour work is trying to work out
how to make data better.
What are the questions that youneed to answer about data?
You've already used languagearound solutions and the let's
call them information productsthat you have.
Tell us more about the dialogue.
What's keeping your users up atnight and what are they asking
(07:14):
for you or from you in terms ofyour feature set and your
information products?
Speaker 4 (07:19):
Sure.
So at the heart of it, it'svery, it's integral.
It's key that the data ourcustomers use and look to us for
is credible and reliable.
So they're looking to us toensure that what we're
delivering to them is reliabledata, it's coming from a
(07:39):
credible source.
So, regardless of the problemsour customers are having, it all
starts with that credible andreliable data.
But, that said, in the risksolutions space and financial
crime compliance area, morespecifically, keeping our
customers up at night isregulatory compliance.
The regulatory landscape hasalways been dynamic.
(08:01):
It's always been a top concern.
But in recent years, given thechanges due to geopolitical
events, technologicaladvancements, various
innovations, our change, thepace of change, the rate of
change of regular regulations isat its utmost highest, and so
(08:23):
for our customers, it has alwaysbeen a concern.
But it's almost risen back tothe top, as one of the primary
concerns is assuring thatcompliance, and that all starts
with the data, the credibilityand the reliability of the data.
Speaker 2 (08:40):
How do you prove that ?
Casting no aspersions.
But just because it comes fromyour company, you source it from
somebody else.
Clearly, how do you guaranteethat provenance?
How do you guarantee?
The credibility, the currencyof the data.
Speaker 4 (08:54):
We've been sourcing data for 40 years, so our
processes are highly structured,highly standardized.
So, in short, we know what goodlooks like because we've proven
it.
We've proven it to ourselves,we've proven it to the industry.
So we know what good looks like.
So, as we analyze new data,aspects of data, we have a
(09:19):
process for testing that and wehave a process that we don't
vary from.
It's a proven process, and oneof those steps is always
comparing it against what goodlooks like.
We have the benefit of knowingwhat good looks like, and so
we're able to just constantlytest, constantly compare.
(09:40):
We have access to a lot ofsources of data, so we don't
have a problem passing on asource of data.
We deem it to be unreliable,uncredible.
But even if it's reliable andcredible, maybe just not good
enough up to our very, very highstandards.
Speaker 3 (09:58):
We are obviously preoccupied with fakes.
Not to be too negative, wethink that the data industry is
full of good things, but giventhe prevalence of fakes and
given people's concern aboutdeep fakes and generative AI but
consumer level, fraud hasalways been around how do you
know who you're dealing with?
Further up the food chain, witha lot of your sources and a lot
(10:21):
of your clients beingenterprise people, do you see
fakes at that level?
Do you have a lot of issueswith, let's say, fidelity or
genuineness of enterprisecustomers and data sources and
so on?
Speaker 4 (10:34):
Certainly our customers do.
They're the ones that see thefake data.
They are experiencing thosevery overt efforts to disguise
identity, to present an identityperson or an entity as
something other than who theyare.
(10:55):
When someone wants to fake whothey are, there are ways to do
that, and so we have a constantgoal of hoping our customers
sort through that fake data.
And that's a lot about therelevancy of the data, what's
important to organization Aversus financial institution B,
(11:18):
so on and so forth.
And so that comes with thoseanalytic value propositions that
we have, some of thetechnologies that we offer, and
that is a little bit differentfor each organization, but we
absolutely help our customerssee through that fake data
because it's very real.
Speaker 3 (11:38):
And so the dimensions of quality that your customers
are looking for.
It changes from customer tocustomer.
Speaker 4 (11:44):
It absolutely does.
Yeah, each of our customers isserving a different industry,
you know, or differentindustries.
Some look like the other, butwhether your financial
institution coming and operatingin a highly regulated
environment, or a corporate,maybe not as tightly regulated,
(12:04):
however serving a riskiercustomer base, what they're
looking for, what I riskidentifiers and what's important
to those different, you know,organizations based on the
profile of who they're serving,the regulatory environment that
they're operating in meansthey're focused on different
(12:24):
aspects of the data.
So it's our goal and ourobjective not just to provide a
product to a customer, butprovide a solution like get to
the bottom of what, what istheir problem, and then work
towards solving that problem.
Speaker 3 (12:42):
It's so fascinating because we're in one of the
tightest regulated, let's say,financial services.
We're in one of the mosttightly regulated sectors of the
world and you'd think thethings are uniform, and yet we
deal a lot with KYC.
Know your customer.
Everybody does KYC differently,yes, and they've all got their
own concerns, their own riskmanagement habits, their own
settings.
It's fascinating how muchdiversity there is in a tightly
(13:04):
regulated space.
Speaker 4 (13:05):
It absolutely is, and every customer base looks
different from your peerscustomer base and there is
opportunity to leverage,leverage practices, leverage
content.
I've got a lot of talk nowabout data.
Consortia is sharing dataacross organizations that might
be anonymized, however, thereare still indicators that can be
(13:28):
shared.
But the reality is, while thereare practices that can be
shared, the end of the day, it'sreally important that each
organization is focused on whatmatters to them.
It's not only about doing whatyour peers are doing benefit
from that but you must befocused on your own organization
and make policies and processestailored to your organization.
Speaker 2 (13:52):
I love that you're pointing at that, because it
really underscores thehistorical failure of federated
approaches that have hope to,for example, apply standardized
data sets and processes for knowyour customer on board.
Speaker 1 (14:08):
Yep.
Speaker 2 (14:09):
Where you know the risk profile of one bank is very
different from the bank downthe street.
Speaker 4 (14:15):
Yeah, never mind the credit union Right.
Yes, and those standardizedprocesses are our minimums.
They should be looked at asminimums.
This is what you must be doing,but there is more to do now,
based on your risk appetite,your customer base, so on and so
forth, what your particularproblems are that you're looking
to solve.
(14:35):
So it is important to havestandardization, but those are
minimums and should be viewedthat way.
Speaker 2 (14:41):
I can imagine that you have a lot of data that
actually endures for years andyears and years, right?
Someone's address or maybetheir phone number or their
social security number and birthnumber.
But I think that's a good thing, right, those are generally
close to life.
Everyone has them Lifetime,Lifetime thanks.
(15:02):
Just to drill a little bit, isthere data that changes
frequently that you guys have ahard focus on or you think
you're particularly good atmonitoring?
Speaker 4 (15:15):
Data changes all the time.
It all changes, quite frankly,even if someone doesn't.
Someone's social securitynumber is what it is, someone's
birth date is what it is, butpeople move, they take new jobs,
they get married, they getdivorced, they have children.
Gosh, we really took a stepback and thought about all we
each did individually in thelast month, let alone the last
(15:37):
12 months or the last couple ofyears, if you recorded all those
data points and changes in yourlife.
Data is changing constantly andso our organizations are in
you've.
You've come into George, you'vecome into that, and KYC or CDD,
customer due diligence, and, ofcourse, I've mentioned
regulations, sanctions, variouswatch lists.
(16:00):
So that's a lot of what ourcustomers are focused on is what
are the changes in regulationsthemselves?
But then how?
What does that mean for thedata that I now need to be aware
of and track and ensure thatI'm constantly maintaining it to
be accurate enough to date?
Speaker 2 (16:20):
You've got a real breadth of data available to you
.
I mean just a little little.
Look at the acquisition historythat Alan Harris has done
fairly recently and you boughtan email edge and ID analytics
and fly real and others.
Each of those companies havebeen focused on a particular
problem set.
Do you bring that data in andmake it available to other
(16:46):
subsidiaries within yourorganization?
The more data, the better.
Is sort of the defaultsupposition on my part.
Speaker 4 (16:52):
Well, now you've just complicated the question,
george.
Yeah, that a whole new dimensionto the question.
In the first instance, whenwe're analyzing a business for
acquisition, for example, itabsolutely needs to lend to the
question.
It needs to lend to the core ofwhat we do.
That's coming back to improvingthe decision making ability on
risks and opportunities, ifthere is opportunity to share
(17:16):
that data within theorganization in a highly
sensitized way.
We have parts of ourorganization that, for example,
work with governmental agencies.
That's a very secure bubble.
Nothing goes into it.
That isn't very highlymonitored.
(17:36):
So that would be sort of like arandom situation when we say no,
but, for example, I'm in thefinancial crime compliance group
and then we have our fraud andidentity group.
Absolutely, that's a groupthat's tracking a lot of digital
activity or digital identityfootprint, for example, mobile
device, laptop, any kind of appson a phone.
(18:00):
So there is opportunity forfinancial crime compliance to
marry our physical identity, ourdata, physical identity data,
with that very digital identitydata and provide a significant
value to our customers.
And so that's an example ofwhere we do.
(18:23):
We do share information.
It's anonymized in mostinstances, you know, kept in,
maybe like even a data cleanroom, and so access to it might
be tightly monitored.
So it's done in a very secureway.
But where we find opportunityand we're always looking at that
of how can we provide a betteroutcome for our customers then
(18:47):
we will find take thatopportunity to share information
or data within our organization.
Speaker 2 (18:52):
Since this culture of ours is becoming largely
digital, I imagine you anchor afair amount of your analytics.
Going forward is when the kidgets their first mobile phone
Absolutely.
Speaker 4 (19:03):
Absolutely.
I mean, think about what agenow do children?
I have nieces and nephews and Ithink they're like eight and 10
.
They don't have their phonesyet with cell service, but they
have iPads and so they're usingthose you know devices, and they
have a digital footprint.
(19:24):
Someone out there knows they'reonline.
Yeah, they're online and sothat's really an opportunity for
benefit to some of ourcustomers.
And then that's maybe a childand at that age, but even
someone that goes to collegelet's say, bump up the age a
little bit that doesn't have acredit card yet, but they do
have this digital footprint.
(19:44):
Well, how do you provide creditto someone who doesn't have
credit, like it's got to startsomewhere, may not have a parent
to co-sign for them?
This is where some of the datawere able to use and turn that
around and actually provide asituation where customers can
can really provide a greatopportunity for someone young
(20:05):
that only has a mobile digitalpresence.
Speaker 2 (20:08):
I've seen similar kind of decisioning in
developing markets, where themetadata around how a mobile
phone is used, for example, thatactually provides good
information with respect to isthis potential customer Going to
pay their bill on time?
Speaker 4 (20:25):
Yep risky or not risky Yep.
Speaker 2 (20:27):
Right which got even got down to.
Does this person charge theirmobile phone on a regular basis
or does?
Speaker 4 (20:36):
it run out of power and they have to reestablish
them yeah Never charge it to 100percent, George, that dies down
the battery earlier than itneeds to.
Speaker 2 (20:45):
I'm guilty of that.
That just means I just don'tmove around very much.
Speaker 3 (20:50):
So that reminds me of that sort of modern parenting
experience that certainly mostof us had had without teenagers,
like financial literacy, and Iremember encouraging, against my
best instincts, my teenagers tostart thinking about credit
cards and getting into thesystem and having a credit
rating before it's too late, Ihope these days, I mean, some of
(21:10):
that stuff gets creepy, george,but these days there's
certainly got to be better dataavailable to make credit scoring
for teenagers as they grow up,but that you know.
Segue to privacy Cindy, ourprivacy business at Lockstep has
got to do with design thinkingand we ask people to think about
what.
Do you really need to knowabout people to deal with them?
That sort of identity Privacyis like what is the least amount
(21:35):
of information you need to knowabout somebody to still deal
with them.
And these days in your industrythere's so much natural drive
and selection pressures to knowmore and more, so there are some
interesting tensions there.
Can you talk to us all abouthow your business looks at those
(21:55):
tensions?
To minimize data and yetminimize data in the interest of
privacy and yet get the sort ofprecision that your customers
need for risk management?
Speaker 4 (22:04):
It's not about more data, more data that's going to
enable me to make a betterdecision, a more accurate
decision.
It's about looking at the bestdata, the relevant data, and I
used that word earlier.
I don't want more data, I justcut through the noise.
I want to see the data that'simportant to me, whether that's
(22:25):
alias data, someone trying tomaster identity, or whether it's
we use that fake data, orwhether it's the real data.
I do want to see that all.
I want to have the capabilities,the analytics to poke through
what the fake data is, get tothe good data.
But I want to know that fakedata is out there as well,
because now I can identify thatperson as risky.
(22:46):
But too much data and I'm notgoing to know what to do with it
.
For us, we have an approach, wehave a scoring methodology.
It's called the Exposure Indexand it's something that we've
created and it's something thatwe enhance.
Even open source data, like awatch list data, we enhance it
(23:06):
with this exposure index thattells an organization how
relevant is this Steve Wilsonrecord to the Steve Wilson
that's in my database?
Are they the same Kind ofcommon first name, semi-common
last name?
Speaker 1 (23:21):
There's a lot of us.
Speaker 4 (23:23):
Yeah, in even George Peabody.
That might be a common name.
Mine is not so common.
Yeah, it's poking through that.
What's relevant to me?
That's one of the value adsthat we provide, but also really
important for our customers out.
There is something very, veryvaluable to them.
Speaker 3 (23:39):
We know about the enormous burden compliance
burden, reporting burden thatbanks have at the moment.
I've recently heard thatcompliance cost has tripled in
the last 10 years for businessin general and it's worse than
that for financial services andbanking.
This is all about data, isn'tit?
Suspicious matter reporting,AML obligations businesses need
(24:00):
to be really sharp in the waythat they test for suspicious
matters and detect suspiciousmatters.
What's the state of the artfrom your perspective, Cindy,
about the signals and theproducts that help people detect
suspicious matters and so on?
Speaker 4 (24:16):
It's a great point because, regardless of what data
you're looking at and reallyyou're going to be able to
identify suspicious data, ifit's entity related Something
specific like that KYC data,that CDD or customer due
diligence data, whether that beon an organization, if I
corporate or a person it'sreally utilizing that entity
(24:39):
data and looking at behaviors ofthat data.
Does this look normal?
What's normal?
I'm a college student.
Am I going to Taco Bell and7-Eleven using my money and
transferring funds digitally, oram I transferring money and
then out of Cuba or something?
Speaker 3 (25:00):
Some of this is just like it's context, isn't it?
It's context, normal variesfrom one customer to another.
Speaker 4 (25:05):
That's exactly right.
It's the data.
It's how you analyze that data.
Again, this comes back to alittle bit of common sense what
makes sense for my customer base?
That's where we see thatvariation from business to
business.
Speaker 2 (25:20):
I've worked on push payments quite a bit in my past
life.
I'm super interested in theproblem of authenticated push
payment fraud.
Of course we're seeing thatlots today with scammers
tricking people to send money tothem.
Yes, Cindy, it really intriguedme to see that you are offering
(25:43):
a service here calledConfirmation of Payee, which I
saw that title, I went.
This is really interestingbecause a lot of these payees
are bogus or they're certainlyscammers who are actually
controlled a legitimate bankaccount.
I'm very curious of what it isthat the Confirmation of Payee
(26:06):
does.
Speaker 4 (26:07):
Yes, what you're referring to is a solution that
focuses on detecting authorizedpush payment fraud.
It's that fraud, of course,being the key part.
You even mentioned it.
It's authorized.
Someone is originating thatpayment and saying I want to
send this payment to GeorgePeabody but, by the way, someone
(26:31):
is posing that they've takenover your name, but it's really
their account number on theother end.
So they've representedthemselves.
That's one situation.
Or they've just representedthemselves as charitable
organization, playing on thegood heart of someone in society
that agrees to send their moneyand turns out it's a scam
(26:52):
that's not going to charitableorganization.
No text write off for you.
We offer a solution that matches.
It basically validates theidentity of the intended
receiver and what it does is itmatches account information.
So this is always.
This solution focuses on bankaccount to bank account, so
(27:12):
account to account.
It doesn't involve its digitalpayments, but not credit card
payments, and it's all aroundverifying who is receiving.
Is that person says who theyare the owner of that bank
account on record, so there's acheck that happens in the
background with our solution.
It's called Validate and SafePayment Verification.
It's an API, it is seamless tothe sender and it's typically a
(27:36):
corporate who would license itand they've installed this API
in their payment process andthey're just running it in the
background.
It happens in less than twoseconds but what it does is it
stops a bad payment before it'sever sent.
The situation of authorizedpush payment it's someone's
authorized it and they're liablefor that money.
(27:57):
Like that poor victim nevergets their money back.
Speaker 2 (28:00):
So your service might .
For example, if a fraudster ora scammer has gotten a hold of
well, they've got a legitimatebank account.
You're checking as to whetherthat bank account matches up to
the name of the scammer?
Speaker 4 (28:13):
Yes, Yep Matches, so owner on record matching with
that bank account and there area couple other checks that are
happening because keep in mindthat the sender is also still
validating identity and oursolution does also that it
validates the bank and theaddress and some of those other
data points that are just reallyimportant to be accurate to
(28:35):
make a payment but also makesure it's going to the intended
recipient.
Speaker 2 (28:39):
I'm curious are any of the customers who are
actually using the service?
Are they giving a thumbs up tothe sender to say is past, our
checks Go ahead?
Speaker 4 (28:50):
No, it's really the opposite.
When it doesn't go through,they're going back to that
sender to say, by the way, wedidn't send this payment, it was
being sent to an unintendedparty.
And it's saving that sendermoney, but also the entity who's
also sending the money not justthe originator, but the
(29:10):
originating entity.
There's a lot of money involvedin these failed payments and
these fraudulent payments, soit's doing a couple of things.
It's not just about savingmoney, it's about stopping fraud
, it's about detecting andpreventing fraud.
Speaker 2 (29:26):
That's fraud today.
Let's wrap up with a littlediscussion about what you're
seeing as the future of fraud,and particularly with respect to
.
I'm always curious how areregulators possibly keeping up
with technology, with theevolution of fraud, now that we
have?
Obviously the poster child ofthis concern is generative AI.
That's got to be high on yourwork schedule.
Speaker 4 (29:50):
It is.
In fact, we recently issued acompany-sponsored statement
couple statements, but acompany-sponsored statement on
our use of AI, and we take itvery seriously.
There is some form of AI thathas been used in some of our
(30:11):
products for years, but thisgenerative AI in terms of a
regulated space is somethingthat must be used in a very,
very measured way.
We realize there are benefitsto it and we realize it's going
to get used in some of oursolutions over time.
I'm not here stating it will orwon't, but it's certainly an
(30:36):
aspect that LNRS recognizes andthat we look into and we're
answering to.
We want to provide the bestsolutions for our customers that
may or may not involve AI overtime.
There are other ways for us toadvance our products.
Things like I mentioned earlier, this consortia, just consortia
of data, that sharing of dataacross businesses and industries
(31:01):
, across customers, across yourpeers.
So there's a very realopportunity there.
That's just another way, not anAI-focused way, but another way
where advancement is happening.
Regulators recognize thebenefit of that, and so it's
very much supported.
Some of these are very muchsupported by regulators, and so
we try to work hand in hand withthem as the industry advances.
Speaker 2 (31:24):
So let me be clear on that LNRS is actually actively
supporting data sharing acrossorganizations and making data
networks available so that theexperience of all of these
parties can be pooled and Iassume your analytics
capabilities can be applied toenrich the overall result.
Speaker 4 (31:45):
Yes, so that is happening in some aspects of our
business, especially in some ofour digital businesses, our
digital data that we provideAnonymizing data, and it's
always anonymized, it's alwaysprotected Data privacy.
Data security is a number oneconcern for us, but there are
(32:06):
some areas of our business whereit is supported by our users.
It's a contributory model, veryaware of the fact that their
peers are contributing data aswell.
It's anonymized and it'sreshared out for the good of
stopping fraud, of preventingfinancial crimes, terrorist
financing, money laundering, andalways for that goal of doing
(32:31):
good.
Speaker 3 (32:32):
That's great to see.
We've always been strugglingwith what's essentially a
tragedy.
The commons haven't we?
With siloed businesses,competitive businesses,
understandably, the naturalthing is to hold that data close
, because data is like the crownjewels.
But more and more willingnessand more and more understanding
of that need to share experienceand know-how and intelligence
(32:54):
that's so fresh it's good tohear, cindy.
Thank you.
Speaker 4 (32:56):
Yeah, things done in a responsible way can do a lot
of good.
It's always focusing on beingresponsible about that use and
sharing of data.
Speaker 2 (33:06):
Well, we'll leave it there, Cindy.
Thank you so much for joiningus on making data better and for
coming back.
Speaker 4 (33:14):
We're really Lovely to be here, John, and then I
hope I get another opportunity.
Speaker 3 (33:18):
Oh you will.
Speaker 2 (33:19):
Yeah, let's do that we look forward to it.
Speaker 4 (33:21):
That'd be great.
Speaker 2 (33:22):
Thanks again.
Speaker 4 (33:23):
Thank you Bunu Robbre you.
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
24/7 News: The Latest
The latest news in 4 minutes updated every hour, every day.
Crime Junkie
Does hearing about a true crime case always leave you scouring the internet for the truth behind the story? Dive into your next mystery with Crime Junkie. Every Monday, join your host Ashley Flowers as she unravels all the details of infamous and underreported true crime cases with her best friend Brit Prawat. From cold cases to missing persons and heroes in our community who seek justice, Crime Junkie is your destination for theories and stories you won’t hear anywhere else. Whether you're a seasoned true crime enthusiast or new to the genre, you'll find yourself on the edge of your seat awaiting a new episode every Monday. If you can never get enough true crime... Congratulations, you’ve found your people. Follow to join a community of Crime Junkies! Crime Junkie is presented by audiochuck Media Company.