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CRO OF POINT PREDICTIVE DISCUSSES THE FUTURE OF FRAUD PREVENTION DURING FINTECH
MEETUP

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Published on March 15th, 2024 by GDS Link

Live from the Fintech Meetup in Las Vegas, March 3-6, 2024, host Rich Alterman
brings you a captivating Lending Link podcast episode featuring Jeff Hendren,
the Chief Revenue Officer of Point Predictive. Recorded amidst the hustle and
bustle of GDS Link’s booth #1219, this episode dives deep into the
transformative world of fraud prevention, leveraging advanced analytics and AI. 

Jeff Hendren shares an insightful overview of Point Predictive’ s strategic
approach to combating fraud and enhancing loan performance. Through a unique,
proprietary dataset amassed from collaborations with over 50 lenders across the
U.S., Point Predictive has set a new standard in identifying fraud, mitigating
early payment defaults, and streamlining the loan approval process. 

The conversation further explores the company’s evolution from its roots in
automotive financing to its expansion into personal and unsecured lending. This
transition, as Hendren explains, necessitated product enhancements and a nuanced
understanding of the varying needs across different lending sectors. 

Throughout the episode, Rich and Jeff delve into the challenges and
opportunities presented by regulatory environments, the importance of fostering
client education for seamless integration, and the synergistic role of human and
artificial intelligence in spotting and addressing emerging fraud patterns. 

Jeff also hints at upcoming innovations focused on enhancing income verification
processes and broadening the company’s market reach beyond automotive financing.
This forward-thinking vision highlights Point Predictive’ s commitment to
driving the future of fraud prevention and financial inclusivity. 

This episode of Lending Link is a must-listen for anyone keen on understanding
the forefront of fraud prevention strategies, the impactful use of AI in
financial services, and the evolving landscape of fintech innovation. Tune in
here: 

 


WATCH THE EPISODE REPLAY ON YOUTUBE 







OR LISTEN ON SPOTIFY 



Fintech Meetup - Point Predictive

[00:00:00] Rich: Good afternoon. This is Rich Alterman and we are doing our
afternoon session of the GDS Lending Link Podcast broadcasting live from Fintech
Meetup in Las Vegas. I'm pleased to have my first guest of the afternoon, Jeff
Hendren, who is the Chief Revenue Officer of Point Predictive. Jeff is a global
software business leader in emerging and high growth enterprise software,
financial data and information and analytics businesses. How are you doing
today, Jeff? 

[00:00:24] Jeff: Yeah, good, Rich. Thanks for having me. I appreciate it. 

[00:00:27]Rich: Thanks for joining me and we had the opportunity back in May of
2022, to interview Frank McKenna, one of your co-workers and enjoy his podcast,
Frank on Friday. If our listeners haven't signed up for that, I highly recommend
you do that. And if you want to learn about deep fakes, go Google Frank McKenna
does not speak French. Deep fake. And, you'll have a chance to be alarmed on,
How easily, your voice and face can be manipulated. Jeff, why don't we start off
talking about Point Predictive. So why don't you give us a little bit of the
elevator pitch on what Point Predictive is and what type of services it provides
to lenders?

[00:01:06] Jeff: Sure. Well, just a very basic description, right? So Point
Predictive has a unique proprietary data set that's provided by the lenders that
we work with. We work with over 50 plus lenders in the U. S. and we utilize
their data set that they provide to us to basically help lenders reduce fraud on
the first party and third party side, as well as reduce early payment defaults.

[00:01:24] And then on the flip side of that is, we use the same data set to
help people convert more automated loan approvals. Foundations of the company
kind of started in automotive financing and it's moved on to working with a lot
of the personal lender space as well. 

[00:01:38] Rich: So shifting from auto to more non-secured lending, were there
any product enhancements or developments that you had to do to accommodate that?

[00:01:46] Jeff: A little bit. I mean, it was working with our existing clients
to kind of shift how we model things. We worked with a client early on, we have
a history of the loans that they've provided and they've sourced and originated
and the performance on those loans. Then we help them modify our model, to fit
the type of consumer they're going after or the channel that they're delivering
through.

[00:02:06] Rich: Now, I'm trying to recall, are you guys classified or do you
have some products that are considered CRA? And how's the mix on that from an
adoption for your clients? Are most of them using the non FCR products or is it
pretty, even?

[00:02:23] Jeff: When the company was founded six years ago, it was all GLBA
clients. And we shipped it to an FCRA Solutions around 18 months ago. And we've
seen about 30 to 40 percent of our clients start to move over that as of, as of
now, recently, and more of them, more of them entertaining it. It just depends
on their business model and what they're trying to achieve too.

[00:02:42] Which specific products are falling under the SCRA side? Our flagship
product, AutoPass, the automotive solution, is falling under that. And we'll
look to bring some of the consumer solutions under that in the next year or two.
But the consumer solution, personal loan, sorry, personal loan solutions aren't
underneath that umbrella yet.

[00:02:58] Rich: Certainly when we think about fraud type solutions, it's become
a very crowded market. We at GDS, we integrate with about 100 different data
bureaus today and a lot are in the fraud space. So, for our audience, when they,
when they are evaluating headers, yours, what are some of the things that really
should stand out about point predictive versus some of the other companies out
there that do frauds like illusions?

[00:03:20] Jeff: Yeah, sure. I'd say number one is, scale and the unique set of
the data, right? And so we've now seen over 90 million unique. Consumers through
our, through our products and services. And so that's the scale side of things
within there. And just a unique set of data that no other vendors or partners
are providing within the space.

[00:03:38] Right. And so I think that allows us to work well with other fraud
providers. And I think we've, we've also been able to get out of just being
broad. And in the more helping people with automation of approvals, by removing
the kind of income waivers, stipes, et cetera. 

[00:03:52] Rich: You have a protocol income pass, right? And that's getting a
lot of adoption there too. The ecospace, has really gotten proud to understand
that you actually worked in Atomic in the past, before you came over to a Point
Predictive. One of the things that people are always interested in is, what a
product roadmap might look like for 2024. Are there any new developments that
you can share? 

[00:04:16] Jeff: At this point by stating it, I don't think I can, but I can
tell you this. We are investing heavily in that income side, and we'll be coming
out with solutions that are less model-type incomes and more solidified. This is
what an income is.

[00:04:27] Rich: Now you guys operate under get models. That's correct. Does
that ever trade any obstacles as you're looking to bring on new companies? 

[00:04:34] Jeff: Yeah, I'd be remiss to tell you there's some folks out there,
banks, FIs, trade unions, and other clients that don't want to do that. But in
many cases, they've already done it, especially in the credit card space. A lot
of them have already done this in the past. And so there's still going to be
some push, but it's mainly an educational understanding of how we use it and how
everybody kind of benefits in the model. 

[00:04:53] Rich: Yeah. One of the things I've always been curious about is when
a company like yours has a gift to get models and you're talking about fraud in
particular, do you have some criteria that a lender must apply to say that this
actually was a fraud loss versus was it a credit loss? When they're thinking
about how to report the data to you all. 

[00:05:13] Jeff: Yeah. We have specific parameters on how they report the data
from a performance perspective and mainly around default and or fraud in
essence. Yeah. If it's an underperforming loan. 

[00:05:24] Rich: Yeah. But I know sometimes it is a challenge for lenders to
really identify that this was actually fraud.

[00:05:31] Jeff: I mean, if we can catch that it was synthetic, we'll identify
that and even tell them in many cases.

[00:05:40] Rich: Now, one of the things that lenders are often interested in are
any type of triggering or monitoring services. Do you offer anything in that way
where once they apply and that consumer actually comes on, are there any type of
services where you're maybe sending them changes or other updates that you've
gotten on that consumer?

[00:05:57] Jeff: I'd hit two areas here, right? I mean, we not only are applying
machine learning and AI to this, but we also have what we call natural
intelligence, basically a team of analysts, right? They're reviewing
applications and being alerted. And so they'll catch things like fraud rings in
certain areas. And we'll start to alert, we work with dealers and lenders, and
we see a fraud ring in Chicago and his team will basically alert all the clients
and people we have within the Chicago area that we're starting to see this
emerge in a fraud ring. And these are the types of IDs, this is what you're
starting to see come through and the identities that are coming through. 

[00:06:27] Rich: Okay, that's interesting. Yeah. You built that like a workflow
management system for your analyst and basically all day they're turning through
lots of data. 

[00:06:34] Jeff: Yeah. And so we're adding on this kind of human intelligence.
Well, like a very common sense about what they're seeing happening, right? Or is
it just relying on our she? 

[00:06:41] Rich: Right. Now, given that you're using AI as your humans are
identifying things that maybe the AI didn't, are you then somehow incorporating
their human behavior?

Jeff: No, it goes back into the database.

[00:06:56] Rich: That's very cool. That's very cool. What do you see, as a
future as it relates to AI, is that going to become something that. Especially
when I think about deep fakes, this is the conversation I've had. Are there
solutions out there that are really going to help?

[00:07:23] Jeff: It's a real problem the industry is going to have. Frank's
obviously picking up on it and saying that. There's companies, I believe, that
are in a strong position to actually provide additional tools around identity.
Apple being one of those but getting all the community to work together is a
hard part. Like Visa, MasterCard doesn't want to work with Apple. Right. It is a
nice partnership. That helps with identity. Right. And so I think there's enough
resources then in tech, so to speak, I could solve this and tackle this problem
together, but the interests don't always align is one of the main problems. 

[00:07:54] Rich: Yeah, I saw a thing on LinkedIn. I’m still scratching my head,
whether it was true or not, where I think it was a company in Hong Kong.  In
fact, I sent it off to Frank where they were having an internal team meeting.
And they did five, five, five million wires. Yeah. The wires, everybody on the
screen was a deep fake. I mean, it's astounding that they could do that so
quickly. 

[00:08:17] Jeff: It's just scary. I mean, obviously there's lots of things that
brings into play from a fraud perspective, it brings efficiency on one side and
the lenders can become more efficient and then more effectively and tailor
products towards people. But I also think it brings the negative side too. 

[00:08:30] Rich: Initially you mentioned first party and third-party fraud, and
then you did mention synthetic. So are your products tapping all three types of
fraud, or is it more first party and third party? 

[00:08:40] Jeff: It's mainly first party, third party, and then synthetic. We
partner for the synthetic, actually. Yeah, so we bring the synthetic in, but
it's more of a partnership on the synthetic ID piece. 

[00:08:48] Rich: Let's not make an assumption. Our whole audience really knows
what the difference is. And you can Explain what, what really is the difference
between?

[00:08:56] Jeff: You may go past my skill set here, but I mean, it's really the
difference between I'm thinking as an individual or I'm lying about what I make
and where I work right now. And who am I as an individual? And am I eligible for
this loan? Really? Where's the difference? Starts a lie, right? 

[00:09:10] Rich: Well, look, it's been a while since I met with Frank, I really
appreciated him back in May 2022, getting together. You haven't been with the
company that long, I understand, but any significant changes that might be of
interest since Frank and us met may that you might want to talk about?

[00:09:29] Jeff: Touched on the income thing, but there will be more news about
that coming in the next year or two. I think from our perspective, it's been
actually just getting out of just being automotive has been a big push for us,
and I think you're seeing it when we come to events like this. It's been a tech
meetup where we would never have been before. And then we think there's a lot
more applications for our unique data set, right? What we're able to do and what
we're able to offer, and we're seeing that as we extend our efforts here. 

[00:09:54] Rich: Okay, great. So, we can't get to this exhibit hall without
passing the gambling tables. Are there any ones that you like to participate in?

[00:10:03] Jeff: I, this is funny. Actually, I came with my wife. We
traditionally play like blackjack and craps and we played Baccarat this time for
the first time. I'm never going to play the other two again. I'm just going to
play Baccarat going forward. The odds are better. You’re basically trying to get
as close to nine as you can, banker or player, but you can bet on the banker.
You're not betting against the dealer per se. You're not betting against the
other people at the table. You're just betting the cards. And do you think the
banker cards will be closer to 9 or the player cards? And the odds on banker are
like 47. 3%. Okay. So if you sit there long enough, you'll lose. But if you find
the right moment in time, you can win. 

[00:10:41] Rich: Yeah. Like I say, we always hear people telling us what they
want.

Jeff: I'm not trying to make a livelihood out of gambling. Oh no. I lost last
time I was here at blackjack.

[00:10:52] Rich:Well, Jeff, I appreciate you joining us this afternoon for a
quick Lending Link Podcast at the FinTech meetup in Vegas. Once again, this is
Rich Alterman. I've been meeting with Jeff Hendry. Chief Revenue Officer for
Point Predictives. Thanks for joining me.

[00:11:07] Jeff: Thanks for having us. Appreciate it. Thank you.




 


ABOUT POINT PREDICTIVE 

Point Predictive is focused on assisting clients in leveraging their data
alongside potent industry fraud consortium data to unearth historical fraud
patterns and construct production-ready models. The expertise lies in predictive
science, enhancing the client’s customer application process through the
adoption of the latest technology and scientific advances to curtail fraud and
diminish the risk of early payment defaults (EPD). With a strong emphasis on
fraud, Point Predictive’s fraud scientists devise predictive scoring solutions,
incorporating machine learning to facilitate the targeting of concealed fraud,
thereby augmenting lending profitability. 





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