The buy now, pay later industry (BNPL) provides an innovation rail for alternative lending processes and real-time fund access, but it is also opening doors for advanced fraud techniques.

Synthetic fraud poses an especially dangerous risk, Featurespace founder Dave Excell tells Bank Automation News in this episode of “The Buzz” podcast. Featurespace uses machine learning and behavioral analysis to facilitate anti-fraud measures for financial service companies.
BNPL providers see most fraud occur at the onboarding, where identity and documentation must be provided in a process similar to opening a bank or credit card account. Cybercriminals create fake accounts using falsified information, which is then used to access products and services, Excell explains.
“We’ve seen significant growth, especially in the U.S. market, around synthetic fraud,” Excell tells BAN. “So, where you’re using stolen identity information mixed with fake information, almost like a synthetic person, which will clear certain validation checks.”
Listen to glean insights on regulatory compliance and how BNPL providers can manage and mitigate fraud.
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The following is a transcript generated by AI technology that has been lightly edited but still contains errors.
Alijah Poindexter 00:06
Good day, and welcome to the buzz of bank automation news podcast. I’m Associate Editor Elijah Poindexter. Recently I spoke with Dave Excel, founder of machine learning anti fraud company featurespace. I spoke to Mr. Excel about how cybercriminals are leveraging synthetic fraud strategies to attack Buy now pay later providers, along with how the providers can mitigate and manage these issues.
Dave Excell 00:29
Yes, I guess, find out pay later. It’s a bit a bit similar in terms of like almost opening an account with a credit card or a bank. So a lot of that is around for how are you proving your identity to the Buy Now pay later provider. And then it’s, I guess, open them to similar attack vectors that we’ve seen through that. So it could be around falsifying documentation or identity information of an individual are also what we’ve seen significant growth here, especially in the US market around synthetic fraud. So where you’re using a mix of stolen identity information, and mixed with fake information, so Miss crater, like a synthetic person, which will validate on certain, like, validation checks, but but not when it’s all combined, combined together. So I guess they’re some of the ways in which sort of using that data that’s available is ways to sort of be fraud or to put a pretense to them, buy now pay later, and provider and what they’re providing, then they don’t have the correct information to then make a decision on how they’re going to proceed with the transaction.
Alijah Poindexter 01:43
On the contrary, so you know, how are these BNPL firms and anti fraud, you know, FinTech firms? How do they leveraged check to sort of manage the solution to keep this fraud from becoming a big problem?
Dave Excell 01:54
So I guess one of those is to validate the data that actually comes in and making sure that those sources are as accurate as possible. And then it’s using technology like ours in terms of machine learning and adaptive behavioral analytics to look at like, what is where’s the customer coming from? What are they trying to purchase? With the Buy Now pay later product? past behavior of similar customers? If it’s a repeat customer? What are the what’s the activity that they’ve seen, historically, to really then try to differentiate what looks like a traditional sort of good consumer versus something that appears abnormal? Like it could be something around suddenly very basic around philosophy? Are we seeing a large number of attempts to buy something from the same device or the same IP address? And I think one of the things that’s really interesting, as we probably don’t really understand the full extent of how much fraud is taking place, where often potentially that gets categorized as a default against the loan, rather than knowing if it was fraud that was actually taking place in the first place.
Alijah Poindexter 02:58
Could you walk me through just briefly, kind of the tech side of that? So you know, how do you guys leverage those machine learning solutions to kind of do that?
Dave Excell 03:08
So I guess, I guess also, one of the challenges, which has been an opportunity within Buy now pay later is you don’t, there’s not necessarily a large amount of data that’s coming through as part of the application. So one of the first pieces is where what data that is available, it’s then validating that that data is accurate. And it’s consistent with data that’s on record, through know your customer type processes. And then it goes through in terms of, well, let’s look at historic customers. Let’s look at other activity that we can capture, like the device from the customer, potentially looking at the good that’s been purchased. And is there a high potential resale value of that which may indicate a higher progress, something like that, that has less resale value? And that’s combining all of those factors together to then overall make a decision on am I willing to accept that customer expect that loan?
Alijah Poindexter 04:05
So moving into sort of like industry strategy, that kind of space, so will not be NPL firms in you know, fintechs? And financial institutions? Will they in banks, of course are included in this? Do you see them eventually adapting BNPL into their strategy? Or do you see BNPL continuing to become sort of an individual player in the space?
Dave Excell 04:25
Um, no, I see it adapting into that. And I think there’s been quite a few sort of press releases around traditional financial institutions looking to make and buy now pay later offerings available, like either that they’re sort of effectively white labeling them to other providers, or providing them through their own brands, as well.
Alijah Poindexter 04:46
So in the BNPL space, so what are the vendors and competitors? What are they competing on? What are some of the key opportunity points that they’re that vendors are trying to kind of, you know, outperform each other on what are consumers in the BNPL space? Those, what are they after that vendors are going to have to innovate on and keep adapting to?
Dave Excell 05:05
That’s a great question. I’m not sure I’ve got a great answer for that one. But I guess one for me, is also on acceptance, like you never want to go through that whole process, and then ultimately be a legitimate consumer, and then be sort of turned away at the last minute. So I think that’s ultimately where a lot of our technology comes into play, as well as you don’t want to be declined due to fraud if you are a genuine customer. So having an accurate decision, at that point of time to be able to go through and allowing the the MPL provided to be making a good decision. And I guess, if they’re able to reduce their losses in terms of payments that are made, then ultimately they can make a more competitive product available into the market, and that’s available to their customers.
Alijah Poindexter 05:54
So what are your predictions on, you know, innovations in both the fraud, anti fraud space, and then just a general Buy now pay later space? What do you think is gonna, you know, what’s in the pipeline from your perspective that both vendors and consumers have to be excited about?
Dave Excell 06:09
So I guess one of the biggest things for fraud, I think, is the adaptability that fraud systems need to have in place, I think BNPL has been a really excited like innovation in the market. But then organizations don’t want to, they necessarily have to go and buy new fraud technology all the time. So one of our key things is to make sure that the solutions that we provide are adaptable to different ways in which consumers use financial products, different financial products that financial institutions want to take to market, and also adaptability to trends and changes. So we’ve seen massive changes in terms of how consumers use financial products over the last two years. And having a system in place, which remains fast and accurate. And during that time period, is really important. As well, as they’re making sure that those systems are explainable in terms of what we’re providing in terms of decisions and outcomes from analytics and machine learning. So it becomes much more understandable to a human that’s looking at the output from that.
Alijah Poindexter 07:08
You know, and it’s funny, you know, COVID, I think most people can agree certainly accelerated the FinTech in the sort of Banking and Financial Innovation boom that we’ve seen over the past year and a half, two years. And, you know, as these technologies become more and more widespread, and more widely adopted, do you see this continuing? Do you see that? Do you? Do you see innovation in the way that has been going on for the past two years, continuing? Or do you see it eventually reaching, not necessarily a tipping point, but sort of a high watermark, and then sort of coming back down to, to average regressing to the average or the main,
Dave Excell 07:42
I definitely see it continuing, I think it’s been really exciting to see sort of the uptake of an almost haven’t seen enough of it, like, one went to a restaurant, and they had a QR code printed on the receipt. And that’s how you would then go and pay. And I think the adoption of a lot of that innovation is in some areas, but not necessarily, in all and I think also, the way in which we work today, like I think the general sort of working more from like a hybrid approach, and being at home is definitely going to be here, here to stay for the at least the medium term. And I think that also spurs a lot of innovation in terms of how we then interact with different products and services. Like I think, also the boom that we’ve seen a subscription based services, like I think you can almost buy anything today on subscription. So that’s another trend that I continue to say adopted, but I guess the same same time, start to see things that we’ve historically seen before, like, rental based mechanisms, I remember growing up a lot of the time is that it’d be advertising to rent a TV or to rent a dishwasher and those types of things. So it’s interesting how the business models start to go around in circles sometimes.
Alijah Poindexter 08:53
Are there any regulatory concerns with Buy now pay later? And if you have the liberty to speak about them or speak about what’s happening on the in the regulation space? What are your maybe not concerned with you have any insights on how regulation will move forward with BNPL?
Dave Excell 09:07
Um, yes, I think for me, there’s a good sort of parallel to look at which was in payday lending space in the UK. So there was an organization called Wonga, which grew up grew extremely quickly. But then there, I guess there was that concern about harm to society in terms of where there being loans that were made, that ultimately, people couldn’t then afford to repay. So I think where regulation can really help the industry is that sort of affordability metric and putting potential ceilings in place. So that then I think it helps create a floor for these companies so that they’re not always in terms of where they’re competing. They’re not competing to a place which then generates harm for the end consumer. So, ultimately, that’s I don’t, ultimately that’s where I think regulation can really play strength in terms of helping the industry in terms of the a pace which is then protecting the income customers and not a race to the bottom effectively.
Alijah Poindexter 10:06
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