Fraudology Podcast with Karisse Hendrick

Welcome back to Fraudology.
Assaf Kipnis first joined me back in 2023, just after he left Facebook, for a two part conversation on how bad actors use social media platforms. Since then he has worked at Google and an AI startup, started his own consultancy, and spent most of his time building with AI. He now spends about 90% of his working day on AI builds, and that makes him the right person to ask a question I keep hearing from fraud leaders. How do we use AI without losing control of it?
We talk about AI guardrails, and why Assaf believes they have to be enforced in code. We also talk about why abuse fighting is not the same as blocking, what happens to a fraud team when leadership expects AI to replace it, and why AI governance is far harder when you start after deployment. Along the way, Assaf shares how he builds investigation tools, why he treats AI like a threat actor, and why he thinks you cannot run a business with a chatbot.
What you’ll hear in this episode:
Why AI changes the speed of fraud more than the tactics, and what that means for defenders who also have to keep doing their jobs.
Why abuse fighting is not blocking, and why leaders who see it that way expect AI to remove the team.
How AI scales fraud analysts, from running several investigations at once to turning a multi day report into a quick review.
Why fraud teams lose knowledge when people leave, and how an AI knowledge base can keep what they learned.
Why AI never says I don’t know, and why a chatbot is not the same as a search engine
Why bad decisions matter more than hallucinations, and why you cannot trust AI output without bounds.
How deterministic code, and not prompts, keeps a nondeterministic AI inside the lines, including a warning about spreadsheets.
Why AI governance after deployment is hard, from AI agent sprawl to defining account takeover consistently.
Why black box decisions fail when legal, courts, or law enforcement ask for proof.

You should listen to this episode if you:
Lead or work on a fraud team and are being asked to use AI for part of your job
Worry that leadership expects AI to replace your fraud team, or are the leader weighing that choice
Are building AI agents for fraud investigations and want to know where guardrails belong
Need to explain AI governance to legal, compliance, or an executive team
Want a practical reason to write guardrails in code and not in a prompt

Welcome back to Fraudology.
This is part two of my conversation with Mohammad Muzahir, also known as Red Bull, the cybercrime whistleblower in the WIRED reporting on scam compounds. In part one, Mohammad described how a fake job offer took him to a scam center in the Golden Triangle region of Laos, and how forced workers were handed scripts, personas, and stolen accounts. This time we pick up with what keeps people inside, how deepfakes make a persona believable, and how he finally got out. This conversation includes human trafficking, violence, and corruption, and Mohammad describes all of it from his own experience.
Mohammad is a human trafficking survivor, and he is clear that most of the people forced to scam were tricked in the same way he was. His scam compound escape includes details he told me he has never shared publicly. We also talk about practical scam prevention. He explains how to test a video call for a deepfake, why banks should consider a cooling off period for large crypto transfers, and why scam victims should speak up without shame. If you work in fraud, risk, or law enforcement, I think his advice is worth your time.

What you’ll hear in this episode:
How Mohammad finally got out of the compound, including a part of his scam compound escape that is not in the WIRED reporting
What life inside a scam center looks like, from dormitories and constant surveillance to forced scamming by workers trafficked from several countries
How a trusted friend can become a recruiter, and how recruiters are paid for the people they bring in
Why some personas in romance scams and pig butchering scams are real people who work on commission
How deepfake scams use stolen photos, live video, and voice notes to convince victims that a persona is real
Simple deepfake detection tests you can use on a video call, including digital arrest scams
Why banks may need a cooling off period for large crypto transfers, and what law enforcement can learn from a victim’s chats
What Mohammad says about scam center corruption in Southeast Asia, and why we both call this a humanitarian crisis
Why scam victims should talk about what happened and why the shame belongs to the criminals

You should listen to this episode if you:
Work in fraud, risk, or compliance and want to understand how social engineering scams keep victims from believing the truth
Train bank or credit union staff who talk with customers who are moving money to crypto
Work in law enforcement and want to know what information victims hold that can help an investigation
Support scam victims and want language that takes away shame
Want a scam survivor’s account of a scam center escape and what it takes to leave a scam compound

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