Fraudology Podcast with Karisse Hendrick

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.

• 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

• 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.

• 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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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

• 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.

• 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.

• 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.

• 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.

• 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.