Fraudology Podcast with Karisse Hendrick

Welcome back to Fraudology.
I’m joined this week by Frank McKenna of Frank on Fraud and Point Predictive, because this was one that I needed a second brain to process it all with me.
I was heads-down working on the Merchant Fraud Alliance agenda when my phone would not stop buzzing. It was a group chat with the people I trust to tell me when the big deals are happening versus just internet noise. And this time, it was a big deal. Brian Krebs had uncovered a dark web portal selling real driver’s licenses. Front, back, barcode, and all. For the price of about a hundred dollars each. That’s roughly 60% of the entire US population sitting in a database that almost anyone could buy.
The rest of this episode is really two stories that are more connected than you realize once you start looking deeper. The data breach itself is only half of the story. The other half is understanding the vendor working behind the scenes of a huge number of household-name businesses, handling their identity verification. That’s what makes this breach so much bigger than it looks on the surface. Then we shift to the scam that has been on my mind since Frank first flagged it last year. Digital arrest is a form of psychological captivity scam that’s now officially made the leap from India to the United States, with real victims and real seven-figure losses to prove it.
Buckle up, because the way to start fighting this is to know what we are up against.
What you’ll hear in this episode:
How a supply chain breach at an identity verification vendor exposed 153 million physical driver’s licenses, and why that’s different from a typical retail data breach.
Why forged identification cards used to be the thing fraud detection software looked for, and why that entire approach breaks down when the ID being used is real.
How AI deepfake drivers license techniques let someone swap their face onto a real, stolen license and pass a liveness check.
What digital arrest actually is, and why I was wrong to assume it would stay contained.
Real case studies of US victims, including a woman held under surveillance for two months and who lost $4.2 million.
How digital arrest has evolved into homegrown versions, including police impersonation and FBI impersonation calls, plus jury duty bail scams.
The scam compounds and pig butchering scam infrastructure in Cambodia that’s now being repurposed for new scam types.
Why some of the jury duty and bail scam calls are reportedly coming from contraband cell phones inside US prisons.

You should listen to this episode if you:
Are working in identity verification, KYC, or fraud prevention and need to think what “the ID is real” actually proves.
Are a fraud and risk professional at a bank who may be the first to see a victim of digital arrest scams withdrawing large sums under duress.
Someone who wants to understand how a breach at one vendor can quietly expose customers of dozens of major, recognizable brands.
Are part of a consumer-facing team and may need to train frontline staff to recognize a customer who’s on the phone with a scammer while standing at the counter.

Welcome back to Fraudology.
It’s just me for this episode, but I’ve got two stories to dig into. They are genuinely important for anyone dealing with chargeback disputes. Whether you’re on the merchant side or the banking side.
The first is Uber and the nearly billion dollars in fines for automated account deactivation. The second story is the story that I really want to unpack. Hims and Hers blowing past their chargeback threshold on their weight loss subscription business.
It’s rare that this stuff becomes public, and I think there’s a lot merchants can learn from it. I know a lot of companies leaning on AI right now to cancel buyer or seller accounts. We will walk through the math on chargeback fee per dispute, what’s actually driving these disputes, and what I’d tell these businesses if they were my client.
What you’ll hear:
Why Uber's near-billion-dollar GDPR fine over automated account deactivation AI should matter to any company using AI to cancel buyer or seller accounts, not just ride-share platforms.
How Visa's acquirer monitoring program actually works, including the chargeback threshold merchants need to stay under and the real dollar cost once they don't.
A full breakdown of the Hims and Hers chargeback situation, including the FTC lawsuit, Restore Online Shoppers Confidence Act violations, and real customer complaints pulled from public FOIA records.
How I'd approach chargeback root cause analysis if this were a client, from subscription billing practices to refund policy gaps.
Real examples of merchants using generative AI chargeback response tools, including one who took their chargeback win rate strategies from a 40% to 65% win rate.
Why dispute monitoring program penalties go far beyond the per-chargeback fee, and how they can affect your relationship with your payment processor.
How to calculate the true cost of a chargeback, including fees, fines, operational costs, and the merchant reputation and chargebacks damage that doesn't show up on a balance sheet.
A reminder that subscription chargebacks are almost always a symptom, not the actual disease, and what usually causes them.

You should listen to this episode if you:
Are a merchant, especially recurring or subscription-based businesses, currently on or worried about landing on Visa's acquirer monitoring program.
Are a fraud, risk, or payments professionals who want a practitioner's breakdown of what actually drives chargeback disputes.
Are using or considering AI to automate account decisions, cancellations, or chargeback responses.
Are a banking professional curious about the ecommerce and merchant side of dispute management.
Are a business leader weighing whether an aggressive subscription or cancellation policy is actually saving money, or just deferring a bigger cost.

Welcome back to Fraudology.
Since I’ve been back from SardineCon, I’ve thought about how much faster and cheaper AI is making fraud. That thread runs through basically everything I’m covering today. I’m digging into a new report from Inscribe showing a 4X increase in AI generated documents. I’ll walk through the difference between a document that’s built entirely by AI and one that’s a real document with AI alterations. Because they are not the same problem.
Then we will go deep on a digital arrest scam, and this is the one I really want you to sit with. Frank McKenna has been predicting digital arrests would hit the US for almost a year. I found a first person account from a woman who got a call claiming to be from her local sheriff’s department. What happened to her over the next several hours is genuinely hard to listen to. I think this is one every fraud fighter needs to be able to explain to the people in their own life who aren’t in this industry.
Along the way, I’m covering a case out of Spain where a man was arrested for using deepfakes to get past identify verification checks, a new report on Grok deepfakes, and a study out of UMass on zombie credit cards. Which is a real NFC fraud loophole. It’s a lot but stick with me.
What you’ll hear:
A quick recap of SardineCon 2026 and why AI was the theme of nearly every conversation I had there
Inscribe's new fraud report showing a 4X increase in AI generated documents, and why bank statement fraud, fake invoices, and fake pay stubs make up more than half of what they're catching
The difference between AI generated documents and AI altered ones, and why the altered ones are actually harder to catch
How synthetic identity fraud and first party fraud both show up in lending fraud, even when the person applying is real
A case out of Spain where deepfakes almost got a man through identity verification, until a one second glitch gave him away
A new report on Grok deepfakes and what it means that one platform is tied to the majority of tracked incidents
A UMass study on zombie credit cards and the NFC fraud loophole that can bring expired cards back to life
The full, first person story of a digital arrest scam, including the jury duty scam call, someone impersonating law enforcement, a bond scam demand, and a PayPal fraud payment that couldn't be undone

You should listen to this episode if you:
Want to understand what a digital arrest scam actually sounds like from the inside
Are in lending, underwriting, or KYC and need to know how bank statement fraud and fake pay stubs are evolving
Want to know the real difference between synthetic identity fraud and first party fraud
Have family members who don’t work in fraud and need a real example to help them recognize a jury duty scam or someone impersonating law enforcement
Are tracking deepfakes and want to know where Grok deepfakes fit into the bigger picture
Process card not present or in person transactions and haven't heard about the zombie credit card loophole yet

Welcome back to Fraudology.
I have to tell you I’m genuinely excited about this one. Today’s guest was highly recommended by Matt Vega, someone whose opinion I trust completely in this industry. By the time we finally hit record, we’d already been talking for 45 minutes off air. That’s a pretty good sign this episode is going to deliver.
Cy Khormaee spent years at Google, building out what eventually became the company’s user protection platform and the technology that now runs quietly in the background protecting billions of devices worldwide from phishing and malware. He took that experience and eventually founded Aegis.AI, and he just got back from Black Hat, which means he is walking into this conversation with a front-row view of exactly where adversarial AI is heading next.
What I wasn’t fully prepared for was how far he was willing to take the demonstration. Cy didn’t just tell me adversarial AI is a growing thread, he showed me, live. Using nothing more than ChatGPT and information freely available online. It’s the kind of moment that changes how you think about a threat you thought you already understood.
We cover a lot of ground in this one. And if you work in fraud, trust and safety, or security in any capacity, this is one you’ll want to sit with.
What you’ll hear in this episode:
Cy's path from Google's user protection platform, home of reCAPTCHA and Safe Browsing, to founding Aegis.AI, and how credential stuffing defense evolved into a hundred-million-dollar business.
A live ChatGPT phishing demo where Cy used open source intelligence to research himself and generate a convincing, contextualized phishing email and matching fake conference website in minutes.
Why AI phishing attacks have moved from theoretical to fully operational, with real-world state actor phishing tactics now automatable at near-zero cost.
The staggering AI phishing email bypass rate statistics: over 50% of emails now slip past existing security email filter bypass controls.
Why AI red team fraud thinking, treating AI as a gardener to nurture rather than a carpenter to micromanage, changes how fraud and security teams should actually deploy these tools.
How the real Robinhood phishing attack shows why login fraud detection signals and upstream fraud detection AI matter more than ever.
Why fraud and cybersecurity convergence isn't optional anymore, and how fraud data sharing across teams closes gaps that adversaries are actively exploiting.
How automated sandboxing fraud detection can catch attacks before a user ever clicks, and why carding attack prevention and account takeover detection increasingly rely on the same signals as cybersecurity teams.

You should listen to this episode if you:
Work in fraud, trust and safety, or security and want to understand how adversarial AI is changing social engineering and phishing attacks.
Are responsible for account takeover detection, credential stuffing detection, or synthetic identity risk at a bank, fintech, or merchant.
Assumed business email compromise had been mostly solved and need a reality check.
Are evaluating AI fraud investigation automation tools and want a clearer sense of what can realistically be automated today.
Are trying to build the case internally for fraud and cybersecurity convergence and fraud data sharing across teams.

Welcome back to Fraudology.
Today's a solo episode built around a study that puts a real number on something fraud leaders have been debating for years: does organizational convergence for fraud actually move the needle on performance, or is it just an org chart trend?
For years, we've all benchmarked ourselves the same way. Approval rate here, chargeback rate there, maybe a manual review rate if we're being thorough. But the problem I've seen play out in company after company is this: optimize your approval rate, and your chargeback rate quietly creeps up. Optimize your chargeback rate by blocking more, and your approval rate takes the hit. You're never seeing the whole picture, just one lever moving at the expense of the other.
The Precise Yes metric is the headline finding from a new Liminal and Accertify study, but the study itself is much bigger than one metric. It surveyed 250 senior fraud, security, and risk leaders across five industry verticals specifically to test the thesis of organizational convergence for fraud and cybersecurity. I walk through what the data says, what forms of convergence actually improve fraud performance, and which ones don't move the needle at all.
This is a data-heavy episode, and I mean that as a compliment to the study. If you've ever needed a fraud KPI for CFO reporting that actually captures the full tradeoff between approvals and fraud loss, this is the one to bring back to your team.
What you'll hear in this episode:
How the Precise Yes metric is calculated, and why approval rate vs chargeback rate alone can hide the real story of your fraud program
Why organizational convergence for fraud and cybersecurity is being driven by operational necessity, not executive mandates, and what that means for how teams are actually changing
Why login has become the new fraud control point, with account takeover, credential stuffing, and bot attacks all converging at that stage
Why 63.6% of organizations still cannot distinguish a cyber attack from a fraud attack in real time, and what that costs them operationally
How CISO fraud ownership is showing up earlier in the vendor decision process, and why board level fraud reporting is becoming a real governance topic
Why partial integration is the highest-performing model for organizational convergence for fraud, and why pushing to full structural integration can actually erode the domain expertise that makes teams effective
Why sharing just two or more use cases between fraud and cyber teams is the real performance tipping point, delivering a 1.5x improvement in fraud performance scores
Why separate budgets between fraud and cyber teams actually outperform unified ones, contradicting one of the most common assumptions about convergence
How fraud metrics by industry vertical vary, including why ecommerce and retail lead the pack while marketplaces lag significantly behind
What the study found on agentic commerce fraud controls and synthetic identity fraud in ecommerce specifically

Who should listen:
Fraud leaders looking for a fraud KPI for CFO reporting that captures the real tradeoff between approvals and fraud loss.
Anyone building a business case for fraud and cybersecurity convergence and needing real data to support it.
CISOs and security leaders increasingly involved in fraud tool evaluation and vendor decisions.
Fraud teams trying to figure out where to start with shared fraud and cyber use cases without a full reorg.
Ecommerce and marketplace fraud professionals wanting an ecommerce fraud benchmarking study to compare their own performance against.
Anyone responsible for board level fraud reporting or making the case for fraud visibility at the executive level.