Fraudology Podcast with Karisse Hendrick

This is a survivor’s story. Mohammad Muzahir, also known as Red Bull, is the cybercrime whistleblower in the WIRED reporting that opened a window into how scam compounds work. He escaped one, and he now spends much of his time helping large organizations understand how they operate.

He was studying computer science and looking for a way to fund a bachelor’s degree abroad when a friend of a friend offered him an IT manager job in Laos, in the Golden Triangle region. The job did not exist. What followed is a human trafficking story and a fraud story at the same time. This episode of the Fraudology podcast sits right where the two meet. We cover how the fake job offer worked, what daily life looks like inside a scam compound, and how forced workers are handed scripts, fake personas, and stolen accounts to run romance and crypto investment scams. This is not an easy conversation, and I hope it gives you a fuller picture of the people on both sides of the screen.

This is part one of a two-part conversation.

What you’ll hear in this episode:

• What a scam compound looks like from the inside, including buildings with dozens of floors, long daily shifts, and fines that keep workers in debt
• How a fake job offer and a chain of trusted contacts moved a young IT graduate from India into a scam compound in Laos
• Why many of the people running these scams are forced workers, and why Mohammad calls himself a victim as well
• How the story bible gives each fake persona a childhood, a hometown, a daily schedule, and an answer for every question
• How romance scam victim manipulation turns trust into a crypto investment, including fake gains and small early withdrawals
• Why a scammer may coach a victim to dismiss a bank’s warning before the victim ever walks into the branch
• What Mohammad says about stolen, aged social media accounts and how recovery flows were used against account owners
• What his 4,200 pages of evidence shared with WIRED’s Andy Greenberg add to what investigators know

You should listen to this episode if you:

• Work in fraud, risk, or trust and safety and want a clearer picture of how the people behind these scams are organized
• Support scam victims and want to understand why they trust a persona so deeply
• Train bank or credit union staff who talk with customers who are being coached by a scammer
• Work on account takeover or social media platform integrity and want to hear how stolen accounts are used
• Want a survivor’s perspective on how a fake job offer becomes forced labor

Welcome back to Fraudology.

I’m sharing the presentation I gave to open the Merchant Fraud Alliance conference in Chicago. It was a “where are we right now” look at the fraud attack landscape, and it grew out of a question I heard from merchants all through the conference. What is actually different about fraud today, and how much of it comes down to AI?

If you work in e-commerce fraud, you have probably felt the shift in your own numbers. Generative AI has changed who can attack, how quickly they can do it, and how well the attacks blend in with good customers. I walk through how AI fraud looks from the ten thousand foot view I get from talking with so many merchants, and I pair it with fraud prevention ideas you can take back to your own team and your own leadership.

What you’ll hear in this episode:

• Why fraud has been industrialized, and how AI lowers the cost, skill, and time needed to attack e-commerce at scale
• The headline numbers, including fraud pressure up 33% year over year, account takeover up 78%, card testing up 175%, and BOPIS fraud up 65%
• How the EMV chip shift in 2015 and the rise of account takeover compare with what AI is doing to the fraud attack landscape today
• Why AI-powered account takeover keeps climbing, and how generative AI makes phishing cheap to personalize
• How AI card testing attacks differ from old scripts, including how agents sort cards and adapt to your controls
• How one campaign can move from identity to checkout to customer support to disputes, and why event-based controls miss it
• Why evidence is now cheap to fake, from fake receipts and identity documents to fake damage photos and deepfakes
• What a dark web card marketplace demo shows about how easy it is to buy the pieces of an attack
• How to fight AI with AI fraud detection using session-level data, telemetry, device ID, and behavioral biometrics

You should listen to this episode if you:

• Need to explain fraud increases to leadership and want current numbers to bring with you
• Have seen a sudden rise in account takeover or card testing, or heard from your processor about the Visa enumeration program
• Deal with refund fraud or chargebacks involving photos, receipts, or item not received claims
• Are evaluating fraud prevention tools and wondering why snapshot-based controls keep missing attacks
• Want to hear how merchants are using generative AI to spot AI-written scams
• Couldn’t attend the Merchant Fraud Alliance and want to hear how the conference opened

Welcome back to Fraudology.

I’ve now had a few days to recover from the inaugural Merchant Fraud Alliance conference in Chicago. I helped create this event. Watching two days of conversation among merchants go from a plan to a room full of people was a real highlight for me.

The Merchant Fraud Alliance 2026 conference was built around a gap I had heard about for years. Merchants wanted a place to compare notes with peers without feeling like the target of a sales pitch. We kept the ratio at roughly three merchants for every solution provider, invited sponsors based on what their customers say about them, and added merchant only deep dives. I explain why we made those choices, then walk through the AI bootcamp, the deep dives, and the main stage sessions. Topics include fraud as a service, app cloning, account takeover trends, executive scorecards, build versus buy decisions, and the PSP gaps merchants keep running into.

This episode is for everyone who couldn’t be there. Two days of sessions won’t fit into one episode. I’m sharing the moments that stuck with me the most.

What you’ll hear in this episode:

• Why we built the Merchant Fraud Alliance as a merchant first fraud practitioner event, and how invite only sponsorship shaped the room
• How the merchant only deep dives worked, including a fraud center of excellence discussion, a refund fraud ecommerce session for food delivery and retailers, and war room roundtables for retailers and marketplaces
• What came out of the AI fraud bootcamp for merchants, from Slack alerts and chargeback research to governance questions and the rising cost of AI tokens
• Why a fraud as a service session encouraged attendees to treat fraud threat intelligence from Telegram, Discord, and the dark web as an input to decisions at the time of purchase
• What one vendor’s data showed about app clone fraud detection, with chargebacks running roughly four times higher when cloned apps were involved
• Ecommerce fraud trends 2026, including a threefold rise in account takeover over two quarters and agent traffic that one vendor found to be nine times riskier than human traffic
• Why fraud now moves upstream and downstream of the payment, and the merchant PSP fraud gaps that appear when you rely only on processor tools
• Lessons in cross functional fraud collaboration, and how to build a fraud executive scorecard with metrics your executives actually want
• A look at the build vs buy fraud stack panel, a non payment fraud discussion covering loyalty fraud and promo abuse, and research on how cyber signals can improve payment fraud decisions

You should listen to this episode if you:

• Couldn’t attend and want a practical summary of the sessions and the ideas that came out of them
• Are curious what a merchant only fraud conference looks like and whether it’s worth attending next year
• Are exploring generative AI in fraud operations and want to hear what other merchants are doing, and what they’re struggling with
• Struggle to explain fraud to executives and need a better way to present your data
• Are weighing a build or buy decision, or feel limited by the fraud tools your PSP provides
• Deal with refund abuse, promo abuse, or loyalty fraud and want to compare notes with peers

Welcome back to Fraudology.

Welcome back to Fraudology. I'm Karisse Hendrick, and this week I'm talking with someone who has become a regular guest on the show. Matt Vega has joined me across six years and several different employers. He's now the Chief Fraud Strategist at Point Predictive, where he works with Frank McKenna on lending fraud, and his move into that world gave me a good reason to ask what looks different from where he sits.

Lending fraud rarely looks like the fraud most merchants picture. Plenty of losses start with an applicant who stretched the truth about income or cleaned up a credit report through repeated disputes, and those cases sit right next to organized attacks. Matt explains how credit washing fraud and income misrepresentation blur the line between abuse and fraud. He also shows how lending consortium fraud data lets a lender see patterns that a single credit report never will.

Then we get to the part I think deserves the most attention. A super prime synthetic identity can now be assembled quickly, and on paper it can outperform a real customer. Whether you work in lending, fintech, or ecommerce, you'll leave with a clearer sense of where your defenses actually hold up.

What you'll hear in this episode:

• How lending consortium fraud data works, and why one lender's loss can protect an entire network from the same attack
• How credit washing fraud and credit bureau dispute abuse can turn a 580 score into a 790 for a short window, and why lenders sometimes miss it
• Why income misrepresentation and bust out fraud lending sit on a spectrum between friendly first party abuse and deliberate attacks
• How a super prime synthetic identity can be built in roughly 90 days using an authorized user fraud scheme and buy now pay later fraud
• Why a perfect credit profile can be the red flag, and what fraud network intelligence lending teams can see that a credit report cannot
• How jailbroken LLM fraud tools, dark web fraud tools, and a dark web identity marketplace make it easier to produce identities, identity document fraud lending, and matching cards
• Why AVS CVV fraud limitations and PSP fraud tool limitations show up as high declines, false positives, and chargebacks
• How good user behavior mapping makes anomalies easier to spot, and how polymorphic fraud attacks and device farms try to imitate real human behavior
• How behavioral biometrics lending fraud controls, friction strategy fraud prevention, and fraud stack vendor evaluation fit together, including why a design partnership fraud tech opportunity can be worth saying yes to

You should listen to this episode if you:

• Work in lending, auto finance, or fintech and want a current look at lending fraud beyond the standard synthetic identity playbook
• Are responsible for fraud tech stack strategy and want a practical way to think about proven vendors versus newer technology
• Are a merchant relying on a PSP fraud tool and wondering why your declines and your chargebacks are both high
• Need language to explain friction decisions to executives and growth teams using data they already care about
• Want to understand why a fraud consortium lending network matters when attackers are using AI to adapt quickly

Welcome back to Fraudology.

AI is here, and it’s making every kind of fraud we already know cheaper, faster, and harder to catch. We are going to walk through a handful of stories that show exactly how AI enabled fraud is showing up right now. From card testing at scale to one very messy fraud story.

That story is Polymarket, and I spend some real time on it. It’s rare that we get this much visibility into a company’s actual fraud details. A Wall Street Journal investigation lays out the stolen debit cards linked to thousands of new accounts and a fraudulent deposit rate that hit 80%. I also get into AI-generated deepfake delivery orders, a bank coalition’s new warning about agentic commerce fraud, and a warning about AI voice cloning scams.

We aren’t eliminating fraud, we never can, but we can get the information to be as fast or slightly faster than the fraudsters.

What you’ll hear in this episode:

• Why AI-enabled BIN attack fraud and card testing are up over 75% according to at least one fraud vendor, and how fraudsters are now optimizing payment authorization the same way legitimate payment teams do
• A full breakdown of the Polymarket fraud scandal, including the prediction market fraud risk baked into its business model and the leadership decisions that made things worse
• Why Polymarket scrapped its same-source withdrawal rule to speed up payouts, and how that decision opened the door to real money laundering prevention failures
• A serious account takeover fraud exploit where a new account created with someone's stolen SSN could inherit their existing balance and linked cards, no password needed
• Why compliance staffing fraud programs need people with actual prevention experience, not just investigators brought in after the fact
• How growth at all costs fraud risk plays out when a CEO's response to an 80% fraud rate is reportedly "just keep growing and pay a fine"
• A new bank coalition report on agentic commerce fraud, and why AI shopping agent chargeback liability still has no real framework under current Visa and Mastercard rules
• An AI-generated fake delivery order used to attempt a retail theft at Walmart, and a Georgia police chief's warning
• What a real chargeback monitoring program actually looks like from the inside, and why Polymarket landing a fraud vendor like Riskified says a lot about how serious this problem became

You should listen to this episode if you:

• Work in payments, card fraud, or retail fraud prevention and want a grounded look at how AI is scaling attacks you already know
• Are building or evaluating a chargeback monitoring program and want a real, public example of what happens when fraud rates spiral
• Care about prediction market fraud risk or are watching the regulatory and legal fallout at companies like Polymarket
• Are responsible for compliance staffing fraud programs and want language for why prevention expertise matters as much as investigative expertise
• Are tracking agentic commerce fraud and want to understand the AI shopping agent chargeback liability gap that still hasn't been solved
• Want practical, real-world examples of AI-generated fake delivery orders and AI phishing scams to bring back to your own fraud team

I'm joined today by Sudhir Lanka, Associate Director of Fraud Strategy at GrubHub. Sudhir's team doesn't just cover GrubHub anymore. His scope recently expanded to include Wonder, GrubHub's new parent company, and Blue Apron, which means he's thinking about food delivery fraud across three genuinely different business models at once, and I wanted to dig into how that actually changes his approach.

We get into what makes a three-sided marketplace uniquely exposed to fraud, since GrubHub has to protect diners, restaurants, and drivers all at the same time, and a gap in protection on any one side eventually breaks trust for everyone else. Sudhir walks through the primary fraud vectors his team deals with, account takeover, payment fraud, refund abuse, and promo abuse, and gives some of the most specific, real-world detail I've heard on this podcast about how each one actually plays out, down to the exact excuses customers give to get a refund they're not owed.

What you'll hear in this episode:

• How Sudhir's career path through JP Morgan Chase, Discover, and GrubHub shaped his approach to fraud strategy at each stage of scale
• Why GrubHub, Wonder, and Blue Apron each carry different food delivery fraud risks despite serving the same underlying mission
• The three primary fraud vectors GrubHub tracks, account takeover, payment fraud, and refund and promo abuse, and how they show up differently across business lines
• A detailed walkthrough of restaurant account takeover, including how a compromised owner's email can lead to a redirected ACH payout and an expensive double payment for GrubHub
• Real examples of driver and diner collusion, including self-delivery loops and a surprising exploit tied to minimum wage laws in cities like Seattle and California
• Why refund abuse and first party fraud can't be predicted at the time of transaction, and Sudhir's framework for placing controls at the actual point of irreversibility instead
• How layered fraud controls work across account creation, checkout, and post-order stages, including multifactor authentication, 3DS authentication, CVV validation, and delivery PIN verification
• The difference between soft friction and hard friction, and why Sudhir intentionally reserves harder friction for a very small percentage of customers
• Why Sudhir sees fraud strategy as fundamentally pro-growth, not anti-growth, and how protecting trust across the marketplace translates directly into revenue

You should listen to this episode if you:

• Work in fraud strategy at a marketplace, food delivery, or platform business balancing multiple user types
• Are dealing with refund abuse, promo abuse, or first party fraud and want a real framework for controlling it without over-relying on prediction
• Want to understand restaurant account takeover and payment redirection fraud from the platform's side, not just the consumer side
• Are building or refining layered fraud controls and want concrete examples of where soft friction versus hard friction actually belongs
• Need language to make the case internally that fraud strategy is pro-growth, not a blocker to it

Welcome back to Fraudology.

This is a solo episode, and I’ve got two stories for you this week. I wanted to follow-up on the ID scan breach that Frank McKenna and I discussed last week. Where things stand now, whether we should still be worried, and what the driver’s license data breach means for KYC fraud prevention going forward.

And then I wanted to get into one of the biggest fraud stories this week. This one is brand new and I wanted to get it to you as soon as possible. A government email phishing scam hit Revolute using what appeared to be a legitimate .gov email domain. The request was fulfilled. Customer data was released. And it did not require a breach of Revolute at all. It required a spoofed email that looked real enough to pass.

I have been talking to my fraud threat intelligence sources about this, including someone with a background at one of the three-letter government agencies. What he told me changed how I’m thinking about this incident entirely. We are going to get into all of it.

This is a fraud news episode, and I’m going to keep it tight today. Let’s dive in.

What you’ll hear in this episode:

• The ID scan data breach update. Where the 153 million driver’s license database stands now, why the FBI takedown matters, and whether we should still be treating this as an active threat.
• Why the ID scan breach was so dangerous for KYC fraud and identity document fraud detection. And why most verification companies would not have caught it.
• What supply chain data breach risk looks like in practice and the vendor contract language every financial institution should have in place.
• The Revolute government request fraud incident explained. What data was released, what a fraudster can do with it, and why it could be used for identity theft and espionage.
• Why a .gov email domain is harder to spoof than it sounds, what a CAC card is and why it matters for government email security, and what my fraud threat intelligence source actually thinks happened.
• The three most likely explanations for this government email phishing scam. Including the foreign adversary fraud angle that changes the whole picture.
• How to prevent government email phishing at your financial institution, email authentication tools, two factor authentication for sensitive inbox access, and training the team that handles government information requests.
• Why this kind of email domain spooking fraud is going to be attempted again, and what neobank fraud prevention teams specifically need to have in place.

You should listen to this episode if you:

• Work in fraud, compliance, or risk at a bank, neobank, or financial institution and want to understand what the Revolute incident actually means for your team.
• Are responsible for financial institution phishing prevention and want practical recommendations you can bring back this week.
• Want to understand how government impersonation fraud works and why a .gov email does not guarantee legitimacy.
• Are evaluating your vendor contracts for supply chain data breach liability language and want a framework for what to include.
• Work in KYC fraud prevention and want to understand why the ID scan data breach was uniquely dangerous for identity document verification.
• Follow fraud news and want a practitioner's read on what actually happened with Revolute, not just the viral LinkedIn version.

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.

Welcome back to Fraudology.

This week I’m joined by Dave G., who spent years investigating money laundering, wire fraud, and scams before moving into e-commerce and, eventually, directly into crypto. Dave was on the ground floor of Bitcoin back when the white paper first came out, and he brings a rare vantage point on stablecoin fraud risk as someone who has watched a payment technology evolve from a niche curiosity into the backbone of a real conversation about agentic commerce.

We start with a story that sets the tone for the whole conversation. It demonstrates how unpredictable this space has always been, and how easily it is to miss where the real value and the real risk end up landing. From there, we get into the heart of what a stablecoin actually is, and why stablecoin unit economics change the payment fraud conversation entirely. They function less like a new currency and more like an infrastructure upgrade.

That capability sounds abstract until you follow it to its logical endpoint; agentic e-commerce. Everyone wants to talk about AI agents buying jackets, concert tickets, or collectibles, the high-consideration, emotionally driven purchases people actually enjoy shopping for. But Dave argues the real volume, and the real fraud exposure, is going to show up in the boring stuff. Bread, milk, and eggs. The things nobody wants to spend time discovering, just delivered. And when those transactions are worth pennies instead of dollars, low-dollar transaction fraud stops looking like a nuisance and starts looking like a scalable business model for criminals willing to take a cent at a time instead of hundreds of dollars at once.

That shift exposes a chargeback liability gap that already has real victims. A reminder that new payment technology fraud adoption always follows the same pattern: whatever gets built, someone tries to exploit before the guardrails exist.

What you'll hear in this episode:

• How Dave went from investigating money laundering and wire fraud to working directly in crypto, and the story of accidentally giving away roughly $1.5 million in Bitcoin at industry conferences.
• Why stablecoin fraud risk needs to be understood separately from Bitcoin fraud history, and how stablecoins function more like an infrastructure upgrade than a new currency.
• How stablecoin unit economics make micropayment fraud economics viable at a scale traditional card and ACH rails were never built to support.
• Why the agentic e-commerce conversation has it backwards, focusing on high-consideration purchases like jackets and concert tickets instead of the low-dollar transaction fraud risk hiding in everyday purchases like bread, milk, and eggs.
• A real chargeback liability example where a cardholder admitted an AI agent made the purchase, and the merchant still had no compelling evidence chargeback rules to fight it.
• Why device-based identity verification is a weaker foundation than the industry treats it as, and an early look at an emerging protocol for verifying AI agent identities.
• How consortium fraud data sharing can create real, sometimes irreversible fraud blacklist consortium risk when a label gets attached to the wrong entity.
• A subscription chargebacks story involving an antivirus company that charged customers for software that did nothing at all.
• Why every new payment technology, from ACH to stablecoins, follows the same pattern: fraud arrives before the guardrails do.

You should listen to this episode if you:

• Work in payments, fraud risk, or chargeback management and want to understand where stablecoin fraud risk and agentic commerce actually intersect.
• Are responsible for card network relationships or dispute strategy and want to understand the chargeback liability gap in agent-initiated purchases.
• Are evaluating identity verification strategies and want a real critique of device-based identity as a long-term solution.
• Participate in a fraud consortium and want to better understand the risk and responsibility that comes with labeling data.
• Are trying to get ahead of new payment technology fraud adoption instead of reacting to it after losses show up.
• Want a grounded, practitioner-level conversation about crypto fraud and payment fraud that goes beyond the hype cycle.

In this episode, I'm sitting down with Tal Yeshanov. Someone I've known for a very long time in this industry, and one of the sharpest risk leaders I know. Tal's path into fraud started almost by accident at Google and YouTube. Then took her through building fraud programs at Eventbrite, before its IPO, and Uber during its earliest hockey-stick growth years. Tal has spent her career building holistic fraud detection systems from scratch, in industries where there was no playbook to follow.

For years, fraud teams operated off a snapshot. Device at checkout. IP at checkout. Did the payment information match? Tal walks through why that single-moment view is no longer enough. And why the shift toward an orchestration platform, one that pulls in customer journey risk signals from the moment a user lands on your site rather than just the moment they transact, is where modern fraud programs are actually headed.

The deeper theme of this episode is what happens when you stop treating fraud detection as a scoring exercise, and start treating it as a full picture. Tal shares a personal story about a rule she built early in her career that was, on paper, flawless. It caught the exact triangulation fraud pattern it was designed for. It also caught a company executive, because his girlfriend used his credit card in a different city. That's false positive reduction in fraud detection in its most human form, and it's a direct argument for upstream fraud prevention data collection: pulling in more signals earlier in the journey instead of adding more rules at the transaction point.

What you'll hear in this episode:

• How Tal moved from Google and YouTube into building fraud programs at Eventbrite and Uber with no existing playbook to follow.
• Why holistic fraud detection means tracking customer journey risk signals from first visit to transaction, not just a snapshot at checkout.
• How an orchestration platform unifies device, IP, email, and behavioral data that used to live in separate point solutions.
• A real story about false positive reduction in fraud detection, including a rule that was technically perfect and still failed a legitimate customer.
• How the same holistic approach extends to account takeover detection, including typing cadence, autofill behavior, and device history.
• Why domain expertise vs AI in fraud isn't a competition, and how agentic AI is being used right now to query databases, support customer service teams, and triage escalations.
• A candid conversation about AI replacing fraud jobs, including a real example of a company that prematurely laid off its fraud leadership.
• Why fraud team tribal knowledge doesn't transfer to an AI model, and what companies risk losing when it walks out the door.
• Tal's fraud leadership philosophy, built on transparency and empathy, and why it creates teams that stay in touch for years.
• The ongoing industry shift from risk team vs fraud team naming, and why more companies are choosing the broader term.

You should listen to this episode if you:

• Work in fraud detection, risk operations, account security, or trust and safety.
• Are evaluating an orchestration platform or trying to move your fraud program beyond point-in-time scoring.
• Want a practical, non-hypothetical look at where agentic AI actually fits in fraud operations today.
• Are a fraud leader worried about AI replacing fraud jobs on your team, or trying to make the case for why domain expertise still matters.
• Care about fraud leadership philosophy and want to build a team that stays loyal and stays sharp.

Welcome back to Fraudology.

Every once in a while I come across a report that makes me stop what I was planning to talk about because it's just that important. This is one of those weeks.

Matt Vega recently released his quarterly fraud report 2026, and after reading it, I knew we needed to break it down together. Not because it's predicting what might happen next year. Because every attack covered in this report is already happening.

One of the biggest themes running through the report is that fraud is simply moving faster than fraud teams are. AI is making sophisticated attacks cheaper, easier to launch, and much harder to detect. Criminals no longer need deep technical skills when they can rent the tools they need through fraud as a service marketplaces.

So what does that actually look like?

In this episode, I walk through several of the newest fraud tactics already showing up in the wild, including malware hidden behind fake unsubscribe links, banking malware that can piggyback on a customer's trusted device, and session hijacking techniques that make account takeover fraud incredibly difficult to detect using traditional fraud detection models.

At first glance, many of these attacks look legitimate. That's exactly the problem.

We also talk about why fraud teams can't rely on individual trust signals like device intelligence, IP reputation, or even behavioral analytics by themselves anymore. Criminals have learned how to blend into legitimate customer behavior, which means our approach to fraud detection and fraud prevention has to evolve too.

This is where things get interesting.

One of my biggest takeaways from Matt's report is that the answer isn't one new tool or one new model. It's layering better fraud intelligence, sharing information across consortium networks, and understanding the broader cybercrime trends happening outside your own organization. Because by the time a new attack reaches your queue, there's a good chance another company has already seen it.

I also spend a few minutes sharing an update on the Merchant Fraud Alliance conference, including our new AI bootcamp that's focused on practical ways fraud teams can start using AI in fraud prevention today. Not because AI replaces fraud analysts, but because it helps good teams move faster.

If you work in merchant fraud, banking fraud, online fraud, or e-commerce fraud, this is one of those episodes that will help you recognize the patterns before they become your next incident.

What you'll hear in this episode:

• Why Matt Vega's quarterly fraud report 2026 is one of the most valuable fraud intelligence resources released this year.
• How AI is accelerating modern cybercrime and making sophisticated attacks accessible through fraud-as-a-service platforms.
• A breakdown of several emerging fraud tactics already targeting merchants and financial institutions.
• How unsubscribe phishing campaigns are being used to install malware and keyloggers.
• Why banking malware is becoming increasingly effective at bypassing traditional fraud controls.
• How session hijacking enables criminals to perform account takeover fraud from trusted customer devices.
• Why device intelligence, IP reputation, and behavioral analytics all need to be evaluated together instead of independently.
• How fraud intelligence sharing and consortium data help organizations identify emerging threats faster.
• An update on the Merchant Fraud Alliance conference and its new AI in fraud prevention bootcamp.

You should listen to this episode if you:

• Lead fraud, risk, or trust & safety teams.
• Manage fraud prevention for an ecommerce merchant, fintech, bank, or payments company.
• Want to stay ahead of the latest cybercrime trends instead of reacting after attacks become widespread.
• Are evaluating how AI is changing both fraud prevention and AI fraud.
• Build or manage fraud detection models.
• Investigate account takeover, synthetic identity, or online fraud.
• Want practical fraud prevention best practices instead of theoretical discussions.
• Rely on device intelligence, IP reputation, or behavioral analytics as part of your fraud strategy.

Welcome back to Fraudology.

In this episode, I’m sitting down with Mark Porteous, a longtime leader in e-commerce fraud and trust who knows two-sided marketplaces from the inside out. Mark spent nine years at StockX, where he started in customer support before building the company’s fraud program from the ground up. And honestly, that is one of the best ways to learn marketplace fraud prevention because you see the customer complaints, the chargebacks, the seller issues, the account problems, and the policy gaps before anybody has officially decided they belong to a fraud team.

We get into what makes online marketplace fraud so different from traditional e-commerce. In a standard retail transaction, the merchant controls the inventory, the pricing, and fulfillment. In a two-sided marketplace, the platform may be trusting one person to sell, another person to buy, and sometimes a third person to deliver the product. Every additional participant creates another identity question, another fraud vector, and another opportunity for marketplace scams or policy abuse.

The deeper theme of this episode is market volatility fraud. Prices can change dramatically because of a sports result, a viral release, an artist announcement, a celebrity event, or a sudden shortage. When that happens, legitimate customer behavior changes, but fraudulent behavior changes too. Sellers may decide not to fulfill because they can resell the item for more. Buyers may file friendly fraud chargebacks after prices fall. Stolen cards may be used to purchase high-demand inventory. And risk models that normally work well can suddenly start treating good customers like fraudsters.

Marketplace fraud prevention cannot stay on autopilot. Fraud leaders need strong policies, clear escalation playbooks, reliable fraud intelligence, and real collaboration with vendors, data teams, customer support, marketing, product, and leadership.

What you’ll hear in this episode:

• How Mark moved from customer support into building a marketplace fraud program from the ground up.
• Why two-sided and multi-sided marketplaces create more complex identity, payment, seller, and fulfillment risks.
• How marketplace pricing volatility can trigger seller non-fulfillment, policy abuse, first-party fraud, and chargebacks.
• Why World Cup ticket pricing is a real-time example of market volatility fraud.
• How friendly fraud can increase when buyers repurchase the same item after its secondary-market price drops.
• Why fraud models and vendor strategies cannot be left on autopilot during high-demand events.
• How approval rates, false declines, fraud risk scoring, and card-not-present fraud need to be balanced during sudden demand spikes.
• Why fraud leaders need playbooks, policy enforcement, and clear escalation paths before an unexpected event happens.
• How to work with marketing, product, growth, finance, customer support, and security without becoming the team that says no to everything.
• Why strong fraud storytelling must always be supported by fraud analytics and measurable business impact.

You should listen to this episode if you:

• Work in marketplace fraud prevention, trust and safety, fraud operations, or fraud risk management.
• Manage buyer fraud, seller fraud, marketplace chargebacks, seller verification, or payment fraud prevention.
• Work for a ticketing, sneaker, collectible, resale, delivery, or multi-sided marketplace.
• Need a better approach to fraud monitoring during major events, product releases, shortages, or pricing spikes.
• Want to improve collaboration between fraud, customer support, product, growth, marketing, finance, and security teams.

In this episode of Fraudology, Karisse Hendrick provides a comprehensive debrief following the massive shifts in the world of artificial intelligence and digital fraud. Karisse shares her highlights and lowlights from navigating the industry's rapid evolution, cutting through the future-looking hype to provide practical insights for fraud and payments professionals.

The conversation explores the evolving mechanics of Agentic AI in commerce, detailing how partnerships like the new Visa and OpenAI infrastructure are designed to make AI commerce a reality. Karisse provides an inside look at why OpenAI previously shelved its original agentic project to avoid becoming a merchant of record, shifting away from full structural ownership due to complex liability rules and low initial adoption.

We also explore the "hot topics" dominating the fraud landscape today:

• The VAMP Threshold "Cliff": How Visa’s monitoring programs offer no "trial" or "safety" months anymore, immediately penalizing merchants the first month they cross thresholds with instant fines and fees per chargeback and TC40.
• The Complexity of Agentic Chargebacks: Real-world examples of "authorized" AI purchases where merchants are automatically losing disputes because card brands do not yet have established "compelling evidence" rules or representment provisions for AI agents.
• The Hook of Cloned Sites: Why AI models looking purely for the "best deal" are inadvertently leading consumers straight into elaborate, cloned phishing websites that harvest credit card data and trigger fraudulent descriptors.

Additionally, Karisse dives into the latest real-world data from a recent UK case study highlighting how fraudulent duplicate sites for household brands appeared directly inside ChatGPT's search indexes. We break down the immediate operational steps fraud leaders must take to protect their customer service channels and coordinate with IT to combat malicious DNS activity before these automated losses skyrocket.

In this episode of Fraudology, Karisse Hendrick sits down with Hailey Windham, Community Lead for Banking at Sardine and host of the Fraud Forward podcast. Hailey shares her journey transitioning from a practitioner at community financial institutions to the vendor side, focusing entirely on providing practical, operational insights for credit unions and community bank fraud teams.

The conversation explores the highly anticipated June 2026 updated guidance from FinCEN regarding Section 314B of the USA Patriot Act. Hailey breaks down how this new fact sheet eliminates long-standing confusion and regulatory fears, giving financial institutions a robust legal framework to safely share information and collaborate under a liability safe harbor.

We also explore the "hot topics" dominating the banking fraud landscape today:

• Fraud Explicitly in Scope: How the new guidance expands past narrow money laundering limits to formally include computer fraud and scams targeting individuals, organizations, or governments.
• Reasonable Suspicion vs. Certainty: Why teams no longer need a fully completed investigation or total certainty before sharing data, making early sharing on incomplete information permissible.
• Real-Time Data Transmission: The protocol shifts allowing financial institutions to share verbal alerts, video surveillance footage, IP addresses, and attempted transactions in real time.
• The Power of Associations: How non-bank compliance providers and industry trade groups can now legally organize 314B associations so smaller institutions don't have to fight fraud alone.

Additionally, Hailey dives into the preliminary insights from the industry’s first practitioner-built benchmarking survey for community banks and credit unions under $10 billion. We examine the systemic issues keeping banking fraud fighters up at night, from rigid legacy core system limitations to the immense pressures of tackling an exponential surge in social engineering scams with limited resources.

Connect with Hailey Windham

In this episode of Fraudology, host Karisse Hendrick cuts through the AI noise to analyze the critical fraud news and regulatory developments impacting the industry today. Inspired by conversations with Merchant Fraud Alliance ambassadors, Karisse separates empty hype from actionable intelligence to help fraud and payments professionals better protect their organizations.

The episode kicks off with a personal story: a real-time "fraud phone a friend" scenario involving a reissued credit card that suffered an enumeration attack (card testing) before it was even activated. Karisse breaks down the mechanics of modern BIN enumeration attacks. How fraudsters leverage AI to narrow down math equations like the Luhn algorithm, test numbers across friction-free merchant checkouts, and navigate the lack of activation requirements or AVS letter checks in payments. She also touches on Visa's merchant enumeration thresholds and the lesser-known Visa Issuer Monitoring Program (VIMP).

Karisse then reviews four major industry news stories driving the future of cyber fraud:

• The Fall of Frontier Models & The KYC Paradox: Following the U.S. government forcing AI models Mythos 5 and Fable 5 offline over cybersecurity concerns, frontier AI labs are pushing for strict, bank-grade Know Your Customer (KYC) identity verification. Karisse reads an insightful debrief explaining why traditional KYC will fail to stop threat actors due to pre-verified accounts, session-hijacking infostealer logs, and deepfake biometrics.
• Meta's $16 Billion Scam Economy: New statistics and internal documents reveal that Meta platforms (Facebook, Instagram, WhatsApp) account for up to 68% of fraud reported by major institutions like Lloyds Bank—and that Meta actively profits off fraudulent ad traffic rather than taking it down.
• The Reality of the UK's Mandatory APP Fraud Reimbursement: A 15-month progress check on the UK's 50/50 mandatory scam reimbursement policy for banks. While 89% of in-scope losses were returned to victims, APP fraud losses actually climbed year-over-year due to high-value, long-gestation social engineering scams.
• Google's RICO Lawsuit Against "Outsider": Google files a lawsuit targeting "Outsider," a massive PhaaS (Phishing-as-a-Service) Telegram network responsible for 9,000 fake sites, $1.9 billion stolen, and over 3.87 million compromised credit cards using AI-assisted smishing templates.

In this episode of Fraudology, Karisse Hendrick provides a comprehensive debrief following her long-awaited conversation with Kathy Stokes, the Senior Director of Fraud Prevention for AARP. Kathy shares her highlights and lowlights from leading the Fraud Watch Network, cutting through the general consumer education hype to provide practical insights for fraud and payments professionals.

The conversation explores the evolving mechanics of fraud victimization in commerce, detailing how organizations like AARP are now identifying systemic vulnerabilities by monitoring "invisible" behaviors, such as how society naturally blames the victims of these crimes. Kathy provides an inside look at why the industry must move away from simply playing "whack-a-mole" at the point of transaction, moving toward shared intelligence to avoid the massive financial and emotional liabilities of sophisticated networks.

We also explore the "hot topics" dominating the fraud landscape today:

• The Private-Public Intelligence Threshold: How the National Elder Fraud Coordination Center (NEFCC/NEFSI) is bringing enterprise giants like Amazon, Google, and Walmart together with law enforcement to drastically turn isolated smaller incidents into massive, prosecutable organized crime cases.
• The Complexity of Systemic Collaboration: Real-world examples of how major financial institutions are shifting their legal stances from hiding data due to perceived risk, to realizing that there is a far greater risk if they do not share fraud data across networks.
• The Human Element vs. The Script: Why senior fraud leadership and empathetic human support systems cannot be replaced by generic checklists, as the critical domain expertise and emotional recovery required to turn victims into survivors is found only through dedicated peer communities.

Additionally, Kathy dives into the latest AARP initiatives, revealing the staggering reality of a $200 billion a year crime loss that impacts countless demographics across the United States. We break down the production behind AARP's Fraud Wars YouTube series, a historic project designed to humanize the impact of fraud and challenge the long-standing apathy within enterprise spaces. Finally, we examine how an organization's willingness to block scams depends almost entirely on shifting corporate mindsets away from accepting multi-million dollar losses as just an "acceptable level of risk."