Whitney McDonald 0:06
Hello and welcome to The Buzz a bank automation news podcast. My name is Whitney McDonald and I’m the editor of bank automation News. Today is December 5 2023. Joining me is Alex Tonello. He is the chief revenue officer of risk intelligence platform trust ball. Prior to joining tres fall, he worked at Experian for over seven years. He is here to discuss how financial institutions can look to artificial intelligence to help fight fraud, as money, mules and synthetic fraud threats grow.
Alex Tonello 0:33
Great, thanks. And thanks for having me today. So, my name is Alex Diallo. I’m the Chief Revenue Officer at trustful I’ve been in data analytics decisioning, for the last like two decades, so I feel a bit old now. But I’ve been at Salesforce as a CRO, since last year in September. And my role is to expand the brands and you know, and help the company grow internationally, across all continents, and across all the key regions. You know, increasing our our clients, relationships, and our partners networks as well. So that’s a little bit about me, who’s trustful. trustful is a risk intelligence platform. And what we do is we analyze hundreds of data signals and data points that come mainly from email, name, email, phone number, device, IP and browser, and we does a wide set of signals that sits underneath those coming from public available sources, we’re able to very quickly, you know, calculate and generate risk scores to help our clients to detect and prevent fraud early in the customer journey. So we’re talking about a solution that is mostly fitting as a pre KYC. So before bank or financial solutions will run traditional sort of onboarding checks, biometric and so on. So we come slightly earlier. And we help our clients to really prevent and detect fraud early in that in that journey. We are an enterprise focus platform. And we obviously, you know, have a very a suite of API’s, as of course, you need to have these days for our products. And our solution is obviously very easy to use, easy to install for our clients. And yeah, that’s a bit about me and the business. Great.
Whitney McDonald 2:38
Well, thank you so much for joining us on The buys. Let’s start here. Bigger Picture, of course, you just mentioned that you’re collecting data, you’re monitoring for fraud. Where do we stand today with fraud? Maybe just tell us where we’re at in the financial services industry with fraud, what you’re watching for? What are those key things that you’re keeping note of? Yeah. So
Alex Tonello 2:59
Yeah, unfortunately for for all of us names through fraud is, is growing and is a complex and challenging issue. The leases can becoming commonplace, but he’s always saying the industry is innovating and technology and people scale skills and experience is driving innovation. And obviously choice as well. But so are the fraudsters. And they’re doing that at a faster rate, that the ones that we are seeing from institutions. So of course, fraud is growing, we are seeing a specific type of frauds, of course, and we are monitoring that we helping our clients with specifically, you know, the detection of money, mules accounts or accounts that are used to recycle money. Even institutional, like Barclays in the UK have seen a an increase year on year of 23%. So obviously, you know, that’s, that’s they specifically younger demographics, you know, surprisingly, as well. But that is something that we’ve seen and the industry is obviously suffering from, and are the source of sorts of types of flows, things like synthetic identities. Fraud is another big and one of the fastest growing form of fraud and financial crime in the United States, for example. And again, those are just a couple of examples. We can quote others, for example, such as authorized push payments, up frauds, again, one other type of fraud. So unfortunately, the the landscape for these is growing a lot. And there’s big challenges for institutions. So that’s where obviously come in, and we will get through to our top clients with
Whitney McDonald 4:51
Yes, those are definitely some trends that we too have been following that you can’t seem to get away from, that you’re watching for within the instance. tuitions. One thing we also can’t ignore right now is AI being used to fight fraud, but also fraudsters taking advantage of AI as well. It would be great if you could talk through both sides of that. How is AI improving the experience to fight fraud? And how has it also advanced fraudsters ability to commit this fraud?
Alex Tonello 5:22
Yeah, absolutely. So AI and machine learning techniques are definitely helping on this challenge. And will will, you know, I will give some examples in a moment about how clients and we seen innovators in institutions are doing this right. But as you said, you know, AI is two sides, and it can be exploited by bad actors. And I think it’s an additive AI is actually becoming a game changer for fraudsters, unfortunately. So we sometimes picture you know, fraudsters and properties worth maybe thinking about for a moment, or what do we mean by fraudsters? Right, so we’ve seen those professional sort of large scale operation rings, those that really have fraud farms that are doing this at scale, and are doing this very effectively. So what AI is doing that is helping these fraudsters to do it even better, faster, and again, at a greater scale. So that is, again, is a worrying trend. But the other things that we have seen is that AI is helping, you know, let’s call it more common people that are taking the bad road, the bad path, and they are really leveraging solutions technologies that are out there, they are there to be to be learned from so we’ve seen this trend where fraud is growing, because it’s both sides disposes professional, but also sort of, you know, individuals that are going down this path, perhaps because they are under more risk, and so on. So that again, it’s it’s a rewarding trend for sure that we’ve seen.
Whitney McDonald 7:02
Now, when it comes to financial institutions ability to monitor this fraud, AI brings a different different, it’s a different player in the game. How should financial institutions really approach this and not underestimate the power of AI that fraudsters are using?
Alex Tonello 7:22
Yeah. Well, this is a very big open, open question, of course, and we could speak for a long time on these, but I guess the key points here are that, you know, institutions are leveraging a combination of in house skills experience technology, to build their defense systems. So you know, we have seen very, you know, lots of innovators, specifically in that sort of new banking and challenges. FinTech space, really building up from from the ground up and doing this at at, you know, really, really well, but of course, do that, well, they still have to leverage external data sources. And, you know, driving feeding these models, these machines with the right level of data is obviously very important. And not taking away of course, the fact that they need to have really great people to do that as well. So, the human side is obviously very, very important. But But equally, you know, we cannot, you know, and they, you know, this is not underestimation here, concerns, you know, issues, because, of course, you know, AI is driving a lot of issues, specifically when we talking about that onboarding journey, where, you know, user’s accounts are being opens, user asking for line of credits, or asking for loans or credit cards or opening just savings account and so on that early stage journey where a user coming and as you mentioned, they have to go through a verification or document checks, and, you know, nowadays, you know, maybe synonymous long ago, they were doing like selfie or video right? And even that, now is a risk have been, have been, as you know, hockenson sites sites are active by fraudsters. So even things we think about liveness checks where you actually have to pick up the handset and during this call, you know, we are seeing fraudsters and AI and, you know, this this trend towards being able to crack even those safest places where the organizations are early to adopt. So I think it’s a combination for what we’ve seen of, you know, getting the right mix of skills in house resources, technology data points externally, and humans and people to help us to coordinate that, but for sure, I don’t think nobody’s really under the belief that they underestimate the issue. everybody’s aware of this So the question becomes how do you? How do you deal with the it’s how do you solve this?
Whitney McDonald 10:06
I know that you’ve started talking through some of the ways that fraudsters are able to even get through the safest of solutions. Can we talk through a little bit more on that red flags to watch for? How do you really monitor this? Maybe it’s on the tech side, maybe it’s on the human side. But how do you watch for these red flags? And what really stands out that should maybe make you hesitate? Yeah.
Alex Tonello 10:31
So again, our narrative here is very much around, you know, dealing with with frauds, before he actually happens. So the idea is to deal with the with the first interaction that’s a banker restriction will have with with a user when they register or request an account or open for our products, open accounts for products, we are really wanting to detect that risk at that early place. Now, for us, you know, a simple call is simple. As soon as a user enter an email and a phone number, a silent check, a tech that can be run in the background, can be run technology allow us to do these in a couple of seconds. And to show some early flags, red flags that tell the organization that declines. Look, this user is more likely to be a risky users. So you need to be really careful. So to give an example, if we were to look at an email address, they have what we call a love velocity, check, which means doesn’t have too many accounts connected, for example, doesn’t have a Google account, or an Amazon or LinkedIn, which is kind of normal these days for your personal email, email address. Or another things could be a phone number that doesn’t have a messaging app, such as a Viber, or a telegram or WhatsApp. So these are pretty common things you’d see, right. So you see, these are individual data points in itself themselves, they don’t really tell a story. But when you put them all together, and when you kind of joined the dots, you start to see some patterns and some correlations that telling you, okay, hold on a second here, which is something not quite right. Therefore, we need to make some adjustments, we need to sort of take some actions and therefore, you know, do better decisioning.
Whitney McDonald 12:22
Yes, looking at all of that data in a in a bigger picture format, right, not just the one offs that are happening. So that kind of brings me into my next question of who really uses trust fall? Have you seen demand grow as fraud to has increased? Maybe talk me through who it is that is leveraging this technology? And how it’s working? Yeah.
Alex Tonello 12:51
Obviously, you know, the, the results of a bigger landscape of fraud means as organizations will definitely need to look for more and more technologies. And that’s, for us. Absolutely, we’ve seen a much higher demand for our solutions. And a lot of organizations wanted to test and learn and, and find ways to really better fight this. Absolutely. So we we really cover a wide a wide array of organizations and financial space. So from traditional banking groups, to to more sort of neobanks intelligent boxes, I mentioned organization that potentially might have already, you know, built things from the ground up, but they need to add additional security measures down to for example, other FinTech digital lending is very big, buy now pay later, again, another sector that we see a lot of demands, because again, those quick decisions that you have to do or the point of someone saying, I want to pay for these goods in in many installments, allow you really to say actually, okay, I want to go further with this with this, this user, this person, rather than actually don’t don’t progress. But again, maybe going back to a bank example, again, to you know, what we’ve seen these days, and I mentioned the beginning, a type of fraud that we see a lot of requests from specifically the money, mewling example, where, you know, we’ve done activities, for example, now we just, we just completed, you know, all series of testing with a large bank is about to be announced and being signed up with us, because we managed to sort of spots over 90% of accuracy of our models in spotting the money mule accounts being created. Again, these are accounts that will be created from so called synthetic identities to obviously commit that sort of money recycling. So again, these are the landscape that are obviously lateral industries we also serve, but in the financial space, that’s where which the biggest demands for for obvious reasons. And that’s where we, I think we’ll definitely continue to see the trend going up. New Year.
Whitney McDonald 15:08
Yes, well, just based on this conversation and what we know from from following fraud within the industry, it would be great if you could provide the audience with a takeaway here, what can they be doing to protect themselves from fraudsters? And I’ll let you take that however direction you want, but what would be something that you can do to really put yourself in a better position to fight fraud?
Alex Tonello 15:35
So I love I love to sell to say here, there’s a silver bullet in all these, as usual, and there is one single solution, but reality is that nobody really should believe you, if you say that. So the reality is that organizations have to use a combination of tools and technologies and data sources to to prevent fraud. So, we are not sitting here saying yes, that is one single thing, but that is, you know, our solution, we always say this is very complementary to many other checks that are run, even in that later phase, the journey, which is obviously KYC, documents, X biometric and so on. So doing these alongside and, of course, we know from our perspective, running these further checks, complimentary is, is extremely important. And, of course, you know, running these, you know, in, in doing these in two ways, because there’s the option of again, taking a solution off the shelves and running it and relying on the scores, and the risk scores degenerate. Or, of course, for more sophisticated clients, using this vast amount of data, feeding into existing models, again, this depends on sophistication declines, but we see both sides happening in with our clients, you know, conversations. And, and for us, again, it’s, you know, the takeaways, of course, use test and explore new solutions. And, and always stay in the game, because because these, as we talked about earlier, the innovation is not going to stop, I mean, other things that we know is already happening, and we already have sort of solutions and things that we’re building is to, you know, for example, dealing with what we call super synthetic identities, which are fraudsters that are really understand the game and standard solutions that are able to stop them, therefore, they are actually advancing their things to mass themselves. So technology has to advance and that’s always going to be the case for providers, but also organizations and alongside having the right people skills, having the right you know, human intervention that we know is super important. That will be my few key points if I were to list them out.
Whitney McDonald 18:00
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Transcribed by https://otter.ai
Transcribed by https://otter.ai
