Protecting your Business Against Fraud and Synthetic Identities with Tom Algie from IDology

Rich Alterman  00:04

You're syncing up and tuning in to The Lending Link podcast, powered by GDS Link, with a modern-day lender can dive deeper into the future of data decisioning and credit risk solutions. Welcome to the show everyone. I'm your host Rich Alterman, and today we're syncing up with Tom Algie, National Sales Manager with IDology, a leader in digital ID verification and authentication solutions. On this episode, Tom and I are discussing a wide range of topics including the various types of fraud, the solution to combat them, trends in the market and so much more. But first, please head over to GDS Links LinkedIn and Twitter pages at GDS Link and hit those like and follow buttons and be sure to subscribe to the lending link on Apple podcast, Spotify, or wherever you prefer to listen to your podcast. As I shared I am being joined today by Tom Algie. Tom has been with IDology for over five years and has almost 20 years of experience in the financial services industry, with a focus on consumer lending in some of his prior roles, which included time with longitude partners and DM metrics, time delivered consulting services to consumer lenders across a wide range of operational topics. All right, now let's get synced with GDS Link. Good afternoon, Tom. How are we doing?

 

Tom Algie  01:26

I'm doing fine, Rich. Thank you. It's great to be with you.

 

Rich Alterman  01:30

Where are you joining us from today?

 

Tom Algie  01:31

I am joining you from our IDology headquarters here in Atlanta, Georgia.

 

Rich Alterman  01:37

Great. Well, before we get into business, I always like to start with a couple of personal questions. I understand you are really into golf, tennis and gardening couple of questions for you. A, are you willing to share your handicap? And what is the most challenging course you've ever played on?

 

Tom Algie  01:50

Oh, that's a good one. Yes, I'll share my handicap no lie, I would say I'm an even 16. Toughest course, would probably be Torrey Pines in San Diego, California. And you know, the thing I always tell people about golf is redemption is one swing away Rich.

 

Rich Alterman  02:10

That's great. That's great. I know you really enjoy tennis. But have you had a chance to try out Pickleball it seems to be the new rage.

 

Tom Algie  02:18

Yes. Pickleball is taking control of our neighborhood tennis courts. So yes, I have I tend to stay away from it. But a few folks in my family are totally into it.

 

Rich Alterman  02:27

Right. That's good. So gardening, what are some of your favorite plants to grow in nurture?

 

Tom Algie  02:32

You know, I always say gardening and landscaping are kind of the same thing. I enjoy kind of doing flower beds, different types of flower beds, like tulips or peonies, but I also like the landscaping part. So as Azelas and, and other types of shrub plantings are things that I like to do when the weather and the time is right.

 

Rich Alterman  02:52

So to have you'll need to get yourself over to Holland on day.

 

Tom Algie  02:55

Oh, big time. Yeah, that's the source. Okay,

 

Rich Alterman  02:58

Good, well, I appreciate that. It's always nice to get a little personal insights on our friends on the podcast. So let's get down to business today, perhaps, you know, maybe to do some level setting, maybe provide some backdrop on how we should think about ID verification, authentication and fraud mitigation in different buckets, and then how they're related?

 

Tom Algie  03:17

Oh, great question. So just to break it down. When you think about identity verification, what our perspective is, you're taking data around an individual, and you're looking to prove that that's accurate using some kind of authoritative data source or sources. That's how we would we would frame identity verification, when you go into authentication, what you're really doing is you're proving that that individual is who they say they are, where they say they are, and are they using a device that they own or have in their possession? So really, it's something you have or something, you know, that will be the key to authenticating. And then, you know, with respect to fraud prevention, it's subjective to the organization, right, the risk mitigation equals the accepted minimum for loss. That's what we know as fraud prevention is it's really up to the organization to to make that call, and all of that now together, identity verification, authentication and fraud mitigation. You have to look at this through the lens of balancing friction versus fraud, right, versus the customer experience. It's a tightrope.

 

Rich Alterman  04:26

So focusing on fraud or fraud, MIT, I mean, there's some pretty astounding statistics out there. I read where a Forbes has said that synthetic fraud, in particular, is projected to cost us businesses about $2.5 billion this year, with research suggesting that they can see numbers as far as $5 billion in 2025. So you know, when we talk about fraud, we hear about first party for odd third party fraud, synthetic fraud, so maybe it helps to do a quick fraud 101 For our audience, I'm sure a lot of them know about it, but certainly it never hurts to a level set again. So just spend a few minutes Tom and really talk about what is that difference between first party third party and synthetic

 

Tom Algie  05:04

So let's talk about synthetic because that's, as we mentioned, that Forbes article, and that's really been a growing issue, especially when we got COVID, and digital onboarding, continue to accelerate, customer not present. So in certain synthetic, I'm creating a fake identity, but I'm using real data. The first question I have is, you know, when you think about solutions that you offer, and competitors as well, what are the methods that are used to really help identify that this identity that's coming over in my application is in fact synthetic? You know, what would be some of the data points that are coming into play there? Fraud is like any other crime, whether it's you know, robbery, or, or whatever the violent or nonviolent, which is that there has to be motivation, there has to be rationalization, there has to be opportunity. So we look at this as a very basic example of a loan, someone's taking a consumer loan, the motivation is they've lost income, or there's something going on with their cash flow. The rationalization is they need money fast to prevent some sort of personal harm. And the opportunity is they can go online and they can get consumer loan and mostly without friction, when we talk about first party, which is like a repayment or a default, that means there was no intent, no intent to pay, right, you know, there's intent to pay or not pay, then you have you know, chargeback which is someone is ordered some kind of good or services, they use a credit card, and now they're going to claim they never got it. So they're gonna go to the bank and or the credit card company and claim chargeback, then you have you know, credit rot washing. So that's first party where someone is intentionally going and using a mechanism like credit repair, they're going to the Bureau's they're claiming identity theft, they're looking to wash that credit record to the point where they can go back out now and do some kind of nefarious activity. And then there's actually something called second party fraud, which has come into play with the advent of prepaid cards. And we know we go back to Patriot Act, any money laundering statutes that came in after 911. And you have money mules, right. So money mule would be, for instance, rich Alterman, it's Tom Algie is data. And now Tom is going to use Rich's data to launder money. So it's almost a second party, a tie for fraud. Yeah, that's something that sometimes people miss. But if we go on to define third party fraud, and that could be account takeover through a man in the middle, or phishing, or some sort of spoofing activity, or you have synthetic identity fraud SIS. Good question Rich, and it's a problem that's not going away. And you know, IDology published a fraud study each year. And some of the stats I'll just call out here to that will backup what you said earlier, our study showed that the SI efforts paddock identity fraud problem is going to yield somewhere between 50 and 250 million a year for the financial institutions that we survey. The other thing is, is there's you know, some Consumer Affairs data out there that show that about two and a half million identities are stolen from deceased individuals. But to answer your question, to spot synthetic identity, you're going to use a few different tools or methods, you're going to use layers of data. So we know that that oftentimes the linchpin data point is Social Security number in terms of financial services. So if that financial service company is looking to spot synthetic, they might look at another digital data point, like an email address or an IP address, or they might look at a device using some biometric tools to to spot fraud or risk. And the other thing, of course, is using diverse sets of data when you're serving industries as diverse as insurance and health care and E commerce, the fraud jumps. So synthetic identities, once they get out there, they can go from banks, to non bank lenders to ecommerce to everything else. So using consortium data will help help spot that in terms of velocity. And then machine learning, use machine learning that's supervised machine learning, meaning you've got trained experts, managing the data that's coming into your machine and watching what comes out of it and making the appropriate adjustments.

 

Rich Alterman  09:33

You know, one of the things I was thinking about was that synthetic fraudsters, you know, they kind of look to maybe getting hold of data from minors, really, that haven't had a credit file established yet. Obviously, age verification is one of the things that you guys offer up is that the primary way to help identify those individuals that might be pulling reports on minors is as a parent, the last thing you want is when they graduate from high school and try to go get a car loan that finds out they have five delinquent trades on a on a credit report. So do you have clients that you know, specifically talk about that as a concern? And do you have any specific ways that you really help combat that?

 

Tom Algie  10:15

That is absolutely something that's come into play since COVID, between June 2020, which was right in the heart of the pandemic, through January 2021, we saw a massive spike through our IDology network in terms of under age identity consortium data flowing through will help spot that age identity verification or age verification sometimes, but you know, I think you have to take into account of where age verification is typically used, which is more along the lines of E commerce. So it's really about the data attributes coming through consortium networks and finding where the data will not hold water. I've seen this identity here in one record stream. But I've only seen it there and I've not seen it elsewhere. What does that mean in terms of its footprint? So that's, that's relative to the underage problem with identity.

 

Rich Alterman  11:12

So when you're talking to new prospects, especially in the financial services industries, what do you kind of hear as some of the most common gaps in their current ID verification fraud mitigation processes, where you really kind of lean in and say, Look, guys, this is where we can really help you?

 

Tom Algie  11:29

Probly number one gap is resources, every one that we visit with and my experience is running very tight on resources. So that's number one in the gap. The second is technology, that's probably aligned with the resource issue. But if they don't have the kind of technology platform, like a GDS are some sort of in house system that allows them to make connections out and bring data in quickly. And then they're kind of on the backfoot. And the last is data diversity, they're leaning into one solution, which might be great, it might be very effective. But there could be other solutions out there that give them lift. So it's really a matter of resources, technology, and being smart about what's available in the market.

 

Rich Alterman  12:15

Yeah, you bring up you bring up an interesting point, I've touched on this in a couple of my other podcasts, GDS, our platform, we've linked with over 100 third party data bureaus in the US, and probably about 200, globally. And if we think about the categorization of where we've seen that the most growth in data bureaus, it's really been in the ID fraud mitigation space. And I sometimes hear a reaction when we show a prospect our list, there's kind of like they're overwhelmed. Like, oh, my gosh, I didn't realize there's so many. So you know, when when you think about you and your competitors, what are some and you even just mentioned, you know, not necessarily leaning into only one provider, what are some of the key things maybe that they should be looking for as they're evaluating vendors? Is it you talked about Consortium? Is it about a coverage? What would be some key attributes that you could envision being in an RFP for a fraud solution?

 

Tom Algie  13:12

Yeah, I would say number one is a flexibility of use. And that could mean the ability to integrate that solution into your your platform or a platform of your choosing, number two, having the ability to use that solution providers offering end to end, there's great point solutions out there, will they be able to lever those in a way that fits with the other parts of their workflow? Right. So

 

Rich Alterman  13:41

you know, when you're selling, do you find that you're having discussions about compliance at the same time you're having discussions about risk? And if not, are you having to sell into different people within that organization, those that are, you know, really concerned about compliance and those that are really concerned about risk.

 

Tom Algie  13:59

Typically, I'm speaking with risk people. However, compliance will have a say. That being said, once I onboard an account, often the clients will come back around and now we have a completely different selling or discussion opportunity with the compliance because they've recognized, hey, we're covering this side in terms of Know Your Customer, or CIP for a card issuer. But now we need to make sure that we're meeting AML or meeting TCPA or or CCPA, whatever type of compliance requirements they have.

 

Rich Alterman  14:34

So you know, one of the challenges in the market and I'm sure when you guys are doing studies with your prospects is that ability for lenders to truly identify losses into the buckets of fraud versus credit loss. And I was wondering, Tom, if you guys are able to help lenders maybe retrospectively take a look at their losses and come back and say, Hey, based on The retro you did with us 15% of your losses are actually related to synthetic fraud. And they are not credit losses, are you able to support anything like that, but by any chance, we can rich,

 

Tom Algie  15:11

but I'll caveat that by saying we're not a data warehouse. So a true retro study would be taking Rich Alterman's data on today 11 7 2022. And, and now looking back at what he looked like on 11 7 2021 IDology does not have that kind of set, we have to go get it fresh, we have to go get it in real time. So look, a look back really is limited to what will not skew 30 60 90 days look back. Yeah, we're confident that that our data, and our results will skew for you. Once they've given us the correlated data, we can go back and look at commonalities the commonalities and identity attributes where we could have alerted them or or shown that signal early on, you know, I've done enough of these to know, a credit loss is a credit loss. And there are great, great sources of data out there, the credit bureaus, the banking data providers, the income data providers, you're going to get the strongest signals to spot credit loss through credit, data FCRA data identity data attributes, when we talk to clients, what we're really telling them is, hey, let's look at the identity data attributes that we will find are most effective to just wash this application out as inauthentic, regardless of whether their credit is good or bad, or whatever the credit record is out there. If you don't want to spend money going downstream, and you're underwriting, you can't process it because it's invalid ID.

 

Rich Alterman  16:45

So by any chance, do you have any anecdotes where a lender without mentioning any names might have come back after they work with you guys and said, gosh, we didn't realize the you know, this many number of fraudsters had actually gotten through our pipe and now have represented losses.

 

Tom Algie  17:03

Absolutely one that's coming to mind recently, a lot of fraud coming from overseas. So what we did is we looked at the geolocation data, the IP address, the lender passed to us. And we saw right away these are IPS coming from out of country out of the United States, which is the authentic identity fraud or third party fraud, someone's you know, making an attack from a foreign country. And once they saw that they immediately, you know, recognized Hey, we could use this type of data from IDology to spot these early and stop these from getting through into the funnel and, and being originated.

 

Rich Alterman  17:38

I just had a flashback. I was at Bank of Boston from 85 to 88. And one of the departments that was in charge was was fraud. We were in the credit card space. And I can remember I won't mention the country, but fraudsters would dot their eyes and slant their their letters a certain way on the application. So we were actually trained, we would go to seminars and learn how to look at those applications and identify certain writing patterns. I know today there are solutions out there and not sure if you offer one Tom, but where like a JavaScript gets placed onto the front end application screen. And it looks for things behavioral analysis, like cadence, how quickly Am I filling out that application? Because it's relying on are looking for that long term memory, right? Name, address, social security number, date of birth should flow off my hands without even thinking twice. But if I'm a fraudster sitting there with an Excel file with 1000 names, and I'm looking back and forth, you're not going to pick up that behavior. Do you guys offer any solutions that kind of attack that problem?

 

Tom Algie  18:37

We don't. But I am aware that there are great solutions out there that are doing that kind of biometric analysis watching that customer or that potential fraudster go through the process and looking for those types of signals looking for those types of behaviors. So it's behavioral biometric. Yeah, it's really for LASIK. Yeah, there are lots of great solutions out there in the market. One of our partners is

 

Rich Alterman  18:59

a chemical neuro ID. And they do that it's neat when they do the demo, you actually get to see how the thing is recognizing these these patterns. We talked about coverage earlier. And with the acquisition by GvG. And you mentioned in one of the answers, you talked a little bit about thin, like thin no credit files, can you talk about how your you know, solutions, like yours really can help round out, you know, a consumer profile when I pull their credit report, and they only have you know, maybe one inquiry and one trade, you know, I may be more likely to climb them because of that, but maybe giving them a you know, a lower loan amount, because I'm so confident that they are who they say they are. Did you deal with that a lot clients looking to you to help round out where they have a thin No, no file type condition.

 

Tom Algie  19:46

Absolutely. And so that goes back to what do those folks have in common? They may be thin, violent in respect to a credit report, but they've got mobile phone or phone data out there on them. They've got email data out there on them, they created some sort of footprint. And so the diversity of data comes back in and not just looking into to one, whether it's large or narrow dataset, you're looking across multiple datasets. And now we can start to find a points on the graph that make up enough confidence to say, Yeah, this is this is an identity that will hold water and you know, go ahead and take them through the credit underwriting process and origination.

 

Rich Alterman  20:25

Okay, so let's kind of like now kind of go back and focus for a second on digital. So we know a lot of digital lenders are evaluating leads from third party lead companies in the traditional lending space, you're talking maybe credit, karma, credit, sesame, short term lending space, dot 818, lead Sherpa other companies, so when you're selling into those groups that are buying leads versus having their own, also having their own organic website for capturing traffic? What are some of the maybe challenges that that you have to help them solve where they are buying leads, and that lead provider is not necessarily doing some of these projects, right, because they want to push as much traffic through as they can. So is there a some nuances in the type of services or products that you would sell, to help evaluate a lead coming from a third party lead provider affiliate versus their own organic website,

 

Tom Algie  21:22

there are and we go back to orchestrating the data, or waterfowling the data with a channel like a third party affiliate, lead generator, there's only a few seconds, that a credit originator has to make a decision before that lead passes over and goes into, you know, another part of the bid system or the pain tree? Sometimes it's been called that in the past. So within a few seconds, they have to figure out one is this data authentic. And if it is great, I'm gonna move it through into the funnel and do the rest of my underwriting checks like credit and banking, income, verification. So what we're doing there is, is we're helping them orchestrate and we build tools to help them do that, if this is a phone number that's disconnected Is this a lead, you really want to evaluate for credit, maybe, maybe not, you've got that appetite. If it's coming organically, they're kind of on their own time. Now, you know that that person maybe has landed to them from a direct mail campaign, or they found them through web search, could be a returning customer as well, they've got more time to be a little bit more thorough, they can run a more of a holistic identity check and really look for a full picture of that identity before they take them through the rest of the journey or the customer experience. That's the nuance that we see. And our services can be mixed and matched accordingly. Based on your your marketing channel.

 

Rich Alterman  22:44

You mentioned friction a couple of times. And we know lenders also very much focused on time to fund how quickly can you put that money in the consumers hands? And what type of correlations Could you share with the audience as far as that you know, the fraud tools and the amount of the loan, ie you as that loan amount increases, we talk talk about doing a $50,000 unsecured loan versus a $2,000 12 month installment loan, you see the uptake of the number of solutions that a lender will use from companies like yourself increasing as that amount of loan is increasing.

 

Tom Algie  23:19

Yeah, that that's a prime example of where friction gets introduced is the risk in terms of loss. So the higher the dollar the loan, the more the company is going to do a holistic look at the identity and then introduce friction. Now sometimes that introduction or friction is at the very end, because they also run credit and all sorts of other potential background checks on that borrower. But yeah, that's very common is the more risk you have with the loan, the more friction that lender is going to potentially introduce.

 

Rich Alterman  23:51

Right? I mean, you certainly want to have some touchpoint, maybe with a customer when you're doing a $50,000, unsecured loan, you can't really afford to do that when someone's borrowing $2,000. We talked a lot about consumers, consumers, consumers. So let's talk for a second about opportunities for small business lending. Is it the same type of products that they are leveraging in the fraud myth space? And do you have any things that are specific to a business versus the owners of the business?

 

Tom Algie  24:17

We learned that when  PPP loans were just streaming through the system during COVID, that our business lender clients, whether they were small business lenders or merchant cash advance whatever they were doing, whatever type of credit, business model they were running, they were losing time and money on the underwriting of the business data that is, is this business located in a secretary of state? What do the Articles of Organization really look like? And are they tied to the primary beneficiary or the individual who's going to guarantee the loan on behalf of the business? So we've created solution and we're very happy with it and we're getting a lot of reasons, you know, really good response and what we're doing As we're taking data on the business, we're taking data on the individual, the primary beneficiary. And we're able to run that data together in a real time, look up through our data set and get back a response that will allow our client or our business lender client, the ability to check that box and move on through the rest of their process. So what we've heard is we're saving people a lot of time and money with our solution.

 

Rich Alterman  25:25

Well, Tom, this has been this has been really good. Let me ask you one personal question. So you played a lottery, you win that $2 billion, how would you spend your first million?

 

Tom Algie  25:34

And gosh, I think probably just put it away into something that's safe and secure. Yeah. And take a vacation. Nice one, maybe somewhere that my phone won't work. How's that?

 

Rich Alterman  25:46

Okay. Maybe you could go to Holland and look at this tool. It's a maybe

 

Tom Algie  25:49

show, but I think my son might work there. So I'm thinking more like Tahiti or somewhere on remote island.

 

Rich Alterman  25:56

Okay. There's some places around Georgia. We could find that too, I think. Yeah. I'm Rich Ultraman, your host at the Linnaean link and we've been chatting with Tom algae national sales manager with audiology. We hope you enjoyed the podcast and are able to apply some of our talking points to your business as you evaluate solutions and processes to improve your ID verification strategies and work to mitigate fraud losses. Thanks and make it a great day.