Thanks to AI, Fraud Has Never Been Easier

**Johnny Ayers** (0:00)
People should assume, fortunately, like assume something's gonna go wrong and kind of work backwards from there, right? You know, assume that some emergency, be like, okay, thank you very much, I'm gonna call you back, right? Your bank will never call you, right? Your bank calls you, hang up the phone, look in the back of your card, call the bank, right?
If you think something's a scam, it probably is.

**Laurie Segall** (0:29)
I'm Laurie Segall, and you're listening to Mostly Human, a tech podcast through a human lens.
So Johnny, I'm so happy to have you here today. We had a brief conversation in Davos about the future of identity and fraud, and it terrified me, but you are the person that people go to to talk about this, and you know a lot about the bad guys on the internet and what they're doing and how they're doing it. So without sounding like major CEO elevator pitch, explain to us, common folk, what it is that you do.

**Johnny Ayers** (1:04)
So Socure has built the largest identity verification fraud prediction in any money laundering platform in the world. So we build AI on offense to enable enterprises, fintech, financial services, insurance, gaming, governments, marketplaces, really give them the tools and algorithms to be able to combat folks that are committing scams, stealing identities, perpetuating different types of fraud.
And so we've been at it for a little over 10 years. So we've been studying these problems pretty intimately and have, I think, a pretty unique seat at the table at onboarding, at login and account takeover, at money movement, call center, digital, in branch. We see all different types of attacks. And yeah, I'm excited to be with you today and share a little bit about what we see in the world.

**Laurie Segall** (1:58)
I am curious. You mentioned kind of AI for offense.
We hear over and over a lot of tech folks say, okay, AI is going to counter AI. AI can do a lot of bad, but it can also do a lot of good. So talk about how you're using machine learning and artificial intelligence to catch, to prevent fraud, and why that's a lot better than the old-fashioned methods. Can you explain it to us, I would say, like we are not in the room at the company?

**Johnny Ayers** (2:24)
We have a lot of security, so we know you're not in the room at the company.

**Laurie Segall** (2:30)
I guess I imagine you would have a lot of security, given what it is that you do.

**Johnny Ayers** (2:35)
I was a little tardy to start here, because I was on the phone with our CSAIL going through some more recent changes that we're making. But yeah, it's pretty locked down.

**Laurie Segall** (2:45)
Are people trying to, I mean, because of what you do, and I promise I won't go on this whole thing, but it is fascinating, because you literally protect some of the largest companies from fraud and all sorts of attacks. Like, do people try to attack you guys?

**Johnny Ayers** (3:01)
I think most companies that have something of value are under attack all the time. Fraudsters, we often say, they're like water. They find the path of least resistance. They're very good at testing weak points. They just will perpetually test and test and test, try to find policy, policy gaps, try to find weak employees. It's unbelievable. Laurie, if you were to start at Socure tomorrow, you'd immediately be attacked with gift cards, CEO requests, inbounds, phishing attacks. They're very, very, very good at saying, okay, we see someone on how to LinkedIn change, boom, attack.
It's remarkable. But anyway, so on the solution set, so we do lots of red teaming. So part of the AI on offense is, we create a lot of synthetic identities ourselves. We mature a lot of synthetic identities ourselves. We're trying to build and create synthetic identities that permeate throughout the ecosystem.
We are creating deep fakes ourselves. We are running injection attacks. We have an internal red team that basically is perpetually trying to figure out how to beat our own systems. We also use multiple external teams to simulate similar types of attacks. They go create fake identities, manipulated identities. They go hire forgers to go actual, go build these fake documents themselves. It's a perpetual game of trying to take two or three or four steps ahead in terms of how we are kind of dogfooding our own capabilities.

**Laurie Segall** (4:34)
It has to be every day is kind of its own little adventure where you are.
Could you take us into your day to day? Who, what types of customers do you have? And what kind of threats are are you seeing? Obviously, they've evolved over the last years, but but take us behind these very locked and secured doors and tell us what you're seeing.