[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.
