The Saturday Fraud Strategist

Chen Zamir
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  • Entrepreneurship

Fraud strategy. No fluff. Real talk from 16 years in the industry, every Saturday. Chen Zamir breaks down the decisions, frameworks, and hard calls behind fraud strategy for professionals who want practical insights they can actually use. Whether you work in fraud, product, or the C-suite, every episode leaves you with one clear takeaway. New episode every Saturday. Subscribe so you never miss one.

Episodes

  1. False Positives Masterclass Pt. 2

    14h ago

    False Positives Masterclass Pt. 2

    One of the most common mistakes I see fraud teams make is attacking false positives head on. A customer complains. The CEO says the model is blocking too much. Someone opens a dashboard, adjusts a fraud model threshold, maybe tweaks a few fraud rules, and suddenly everyone feels like progress is happening. Honestly, not a good look. Not because false positive reduction is the wrong goal. It is absolutely the right goal. The problem is that most teams go tactical immediately. And if you are tactical about the way you reduce false positives, you should probably only expect tactical gains. In this episode, we get into the second part of the false positives masterclass: how to break down false positive fraud detection models into buckets you can actually prioritize and fix. We look at where false positives come from, which ones are driven by fraud detection models, fraud rules, manual review, upstream partners, fraud analysts, data quality issues, corrupted fraud signals, and payment fraud detection workflows. Quantifying false positives is useful. But it is not a plan. What you’ll hear in this episode:Why reducing false positives requires root cause analysis, not just model tuningHow to identify who actually declined the event: a rule, fraud model threshold, AI agent, fraud analyst, manual review team, issuer, acquirer, or fraud vendorWhy upstream payment partners can create false positives your own fraud prevention systems cannot directly fixHow fraud decisioning breaks down across payment fraud detection, fraud risk scoring, and operational workflowsWhy fraud system optimization starts with identifying the worst offendersHow data quality issues, corrupted fraud signals, and model drift create false positives that look like fraud riskHow fraud operations teams can prioritize the buckets that are large enough, fixable enough, and valuable enough to address first Who should listen:Fraud operations leaders trying to improve fraud detection accuracyFraud analysts working through manual review queuesRisk teams managing fraud rules, fraud model thresholds, and fraud risk scoringData science teams responsible for fraud detection models and model driftPayment fraud detection teams dealing with issuer declines and upstream partner decisionsFraud prevention teams trying to reduce false positives without increasing lossesAnyone who has ever stared at a false positive dashboard and thought, “Okay, now what?”

    10 min
  2. Why PSPs Struggle With Fraud Prevention Technology

    Jun 20

    Why PSPs Struggle With Fraud Prevention Technology

    So over the years I’ve had a lot of conversations with Payment Service Providers that wanted to build fraud prevention technology. Not necessarily for their own internal risk controls. That would almost be too obvious. What they really wanted was to offer fraud prevention software as a value-added service to their merchants. Honestly, I get it. Payment fraud prevention can help PSPs differentiate, win deals, increase stickiness, and create new revenue. Pretty good on paper. But then you get to the uncomfortable part. A lot of teams assume fraud detection technology is basically an API, payment data, and a machine learning fraud detection model that returns a fraud score. Not a good look. I break down why fraud prevention technology is much harder to build, maintain, and operationalize than it looks, why fraud scoring alone does not solve merchant fraud prevention, and why payment service providers need to think seriously about fraud operations, chargeback prevention, false positive reduction, payment risk management, and the support merchants actually need. What you will hear in this episode:Why PSPs want to offer fraud prevention technology as a value-added serviceWhere fraud prevention software becomes more complicated than expectedWhy AI fraud detection and machine learning fraud detection are not enough on their ownHow fraud scoring can create confusion if merchants do not know how to act on itWhy merchant fraud prevention requires operational support, not just fraud detection softwareHow chargeback prevention, false positive reduction, and payment risk management affect real business outcomesWhy fraud prevention for payment service providers needs to include strategy, support, and fraud investigation tools Who should listen:Payment service providers considering fraud prevention technologyPSP product leaders building payment fraud prevention servicesFraud operations and payment risk management teamsMerchants evaluating fraud prevention software or fraud management softwareRisk leaders responsible for chargeback prevention and false positive reductionTeams working with fraud detection models, fraud scoring, or fraud investigation toolsAnyone trying to understand why fraud prevention technology is not just a model returning a score This episode is for people who want to reduce fraud without pretending fraud risk management is magically solved because someone added “AI” to the roadmap.

    5 min
  3. Should Fraud and Cybersecurity Teams Converge?

    Jun 13

    Should Fraud and Cybersecurity Teams Converge?

    Every few years our industry rediscovers the same debate: should fraud and cybersecurity teams actually sit together? And honestly, usually both sides hate the idea immediately. Not because they dislike each other. Mostly because both teams are already overwhelmed and nobody wants another meeting. But over the last couple of years, something changed. The signals started converging. Credential stuffing became account takeover. Account takeover became fraud. Fraud became phishing. Phishing became invoice fraud and ACH fraud. And suddenly the same security telemetry that detects compromised infrastructure also helps identify fraudulent users before they ever reach checkout. That is where things start getting weird. In this episode, I sat down with Cy Khormaee, who helped build Recaptcha at Google and now runs Aegis AI, to talk about why AI phishing detection is forcing fraud and cybersecurity teams closer together whether they like it or not. And honestly, once you realize the same behavioral signals can stop both account takeover and payment fraud detection, the organizational separation starts feeling a little artificial. We get into AI email security, AI-powered fraud, fraudster ROI, upstream fraud detection, and why modern attackers are moving faster than most enterprise security stacks were designed for. Also, I learned that Google literally tracked the market price of breaking CAPTCHA systems like a stock ticker. Which honestly feels extremely fraud-brained. What you’ll hear in this episode:A practical look at why fraud and cybersecurity teams are starting to share the same signalsHow credential stuffing and account takeover pushed security tools into fraud prevention use casesWhy AI phishing detection depends on more than static email rules or reputation checksHow AI email security is changing as attackers use AI to generate more targeted phishing attacksWhere invoice fraud, ACH fraud, and accounts payable fraud sit between security and fraud operationsWhy security telemetry and fraud telemetry become more useful when teams connect the full user journeyHow Recaptcha evolved from image puzzles into behavioral detection and fraud prevention infrastructureWhy “good people leave tracks” still applies across both fraud and security signalsHow upstream fraud detection helps stop problems before money leaves the platformWhy fraudster ROI is one of the most useful ways to think about modern defenseWhat teams should ask vendors before buying AI-powered fraud or AI security tools Expect a conversation about tools, signals, attacker economics, and the awkward reality that fraud and security may already be converging, whether the org chart admits it or not. Who should listen:Fraud leaders and fraud analystsCybersecurity professionalsTrust and safety teamsFinTech fraud prevention teamsEmail security teamsAccounts payable and payment risk teamsTeams evaluating AI phishing detection or AI email security vendorsAnyone working on credential stuffing, account takeover, invoice fraud, ACH fraud, or upstream fraud detection Basically, if your fraud team and cybersecurity team only meet during incident review, this one may be worth playing in both rooms.

    50 min
  4. False Positives Masterclass: How To Measure FPs In Systems That Hide Them

    Jun 6

    False Positives Masterclass: How To Measure FPs In Systems That Hide Them

    Honestly, most fraud teams have no idea how many good users they are actually blocking. Ask someone for their chargeback data and you’ll usually get a very precise answer. Ask how many legitimate customers were declined by mistake and suddenly things get a lot less scientific. Usually somewhere between a shrug and “probably not many.” Not a great sign. False positive fraud detection is fundamentally difficult, not because fraud teams do not care, but because fraud systems are often designed in ways that make false positives invisible by default. If you approve a transaction, the system gets feedback. Fraud turns into chargebacks. Legitimate users come back and transact again. But when you block someone, the signal disappears. The complaint gets buried in a support queue. The customer never retries. The event never becomes a label. And suddenly your fraud analytics pipeline has no idea the mistake even happened. That is really the core problem this episode explores. More specifically, how fraud teams can start measuring false positive rates using imperfect but practical approaches like fraud rules simulation, manual review, entity resolution, control groups, transaction monitoring, and user feedback. Before you can reduce false positives, you first need to prove they exist. What you’ll hear in this episode:Why false positive fraud detection is difficult in systems built around incomplete feedback loopsHow declined transactions disappear from fraud analytics and model training dataWhy chargeback data is easier to measure than blocked legitimate usersA breakdown of fraud rules simulation and where simulation fails operationallyHow manual review helps identify hidden false positives inside payment fraud detection systemsWhy entity resolution becomes one of the strongest tools for linking blocked users to later legitimate behaviorHow control groups expose hidden weaknesses in fraud decisioning systemsWhere user feedback loops can help, and where they become dangerousWhy fraud prevention strategy depends on understanding false positive reduction at the operational levelHow fraud risk management changes once teams understand where false positives actually come from A conversation about fraud systems, hidden mistakes, operational blind spots, and why measuring false positives is mostly an exercise in triangulation rather than certainty. Who should listen:Fraud leaders and fraud analystsRisk and compliance teamsFraud operations managersFinTech fraud prevention teamsPayment fraud detection professionalsTeams managing fraud decisioning systemsData science and fraud analytics teamsAnyone responsible for transaction monitoring, fraud prevention tools, or false positive reduction Basically, if you have ever looked at your fraud system and wondered whether you are blocking more good users than you realize, this episode is for you. Honestly, the answer is probably yes.

    9 min
  5. I Used to Stalk People on Facebook

    May 30

    I Used to Stalk People on Facebook

    Back in 2009, when I started working in fraud prevention at PayPal, we had this saying: “Good people leave tracks.” And honestly, that was kind of the whole job. Fraudsters tried to erase themselves. Fake identities, disposable emails, wiped browser cookies, brand-new accounts. Legitimate users, meanwhile, usually left digital breadcrumbs everywhere because nobody really thought much about online privacy back then. So yes, part of the job was basically social media investigation. And honestly, I got weirdly good at it. In this episode, I tell the story of how a random Facebook profile picture, a colonial-looking building, and an old backpacking trip through Vietnam helped us approve a transaction that initially looked like obvious fraud. Now, if listening to that story makes you cringe a little, good. It should. The bigger conversation here is not really about Facebook stalking. It is about how fraud prevention changed once online privacy, customer privacy, and data privacy became much more serious priorities across the internet. And now we have this strange tradeoff. As private citizens, most of us are probably happy that publicly available information is harder to access than it was 15 years ago. But as fraud professionals, we also lost a huge amount of visibility that once helped us understand identity intelligence, behavior patterns, and fraud risk. Not a simple problem. What you’ll hear in this episode:How social media investigation worked inside fraud teams in the early days of fintech fraud preventionWhy fraud analysts relied heavily on publicly available information and digital breadcrumbsA real fraud investigation story involving Facebook, geolocation mismatch, and identity verificationHow online privacy and data privacy reshaped fraud prevention workflowsWhy social media OSINT became harder as platforms tightened customer privacy controlsHow open source intelligence techniques evolved from manual investigation into AI OSINT toolsWhy identity intelligence became more difficult once social networks reduced public visibilityA practical discussion about OSINT for fraud prevention and its limits todayHow scammers and social engineering scams changed the privacy conversation entirelyWhy fraud fighters may need to rethink their relationship with privacy regulations A conversation that starts with an old-school fraud investigation story that turns into a broader discussion about whether losing access to personal data may have actually protected us in the long run. Who should listen:Fraud leaders and fraud investigatorsTrust and safety professionalsFinTech fraud prevention teamsRisk and compliance professionalsOSINT and digital investigation practitionersCybersecurity and identity teams Anyone interested in social media OSINT, online privacy, identity intelligence, or open source intelligence techniques. Basically, if you ever used Facebook like an investigative database, this episode is probably going to make you a little uncomfortable.

    4 min
  6. Dark Web Services Bypass KYC Checks For $150

    May 23

    Dark Web Services Bypass KYC Checks For $150

    A year and a half ago, I wrote that for around 150 bucks, anyone could buy a service on the dark web that bypassed a KYC vendor. People were shocked. Today? Honestly, not so much. Now the threat is cheaper, faster, and harder to spot. Document checks can be bypassed. Selfies can be bypassed. Even 3D liveness checks, the ones that looked unbeatable not that long ago, can be bypassed. Not a good look. So in this episode, I want to talk about what fraud teams actually do next. Because if your KYC fraud prevention strategy still assumes that a clean KYC pass means a clean user, you are already behind. The answer is layering. But not the lazy version where you just buy more KYC vendors and hope one of them saves you. I mean real multi-layer fraud defense: device intelligence, behavioral biometrics, behavioral signals, identity intelligence, device telemetry, post-signup fraud monitoring, and KYC vendor orchestration used in the right sequence. Because a KYC check is a signal. It is not a verdict. What you’ll hear in this episode:A breakdown of why KYC bypass prevention has become harder as fraud kits get cheaper and more specializedWhy KYC fraud checks, document checks, selfies, and 3D liveness can no longer carry the whole fraud prevention strategyHow device intelligence asks different questions than a KYC vendorWhy behavioral signals and behavioral biometrics can expose what a document check missesHow identity intelligence helps connect emails, phone numbers, addresses, and documentation into a more cohesive pictureWhy post-signup fraud monitoring and high-risk user monitoring matter after account openingHow step-up verification can add friction only when the risk actually justifies itWhy KYC vendor orchestration can be useful for a small, high-risk segmentHow fraudster ROI changes when fraud teams stop relying on a single point of failure A practical conversation about layered fraud defense, operational blind spots, and why modern KYC fraud detection depends on connecting signals instead of trusting one onboarding result. Who should listen:Fraud leaders and fraud operatorsRisk and compliance teamsFinTech teams managing onboarding and account opening fraudTrust and safety professionalsIdentity verification and KYC teamsTeams evaluating behavioral biometrics, device intelligence, and synthetic identity detection Basically, if your fraud stack still depends heavily on one KYC vendor, or if device telemetry is collected but barely used, or if onboarding and transaction monitoring teams are still operating in silos this episode is probably going to feel uncomfortably familiar. Honestly, that stack fails every time eventually.

    5 min
  7. Real-Time Fraud Prevention: Zero to Hero w/ Matt Vega

    May 16

    Real-Time Fraud Prevention: Zero to Hero w/ Matt Vega

    This episode is a bit of a full-circle moment. Years ago, Matt Vega interviewed me on one of my first podcast appearances. And now, somehow, here we are, roles reversed, with Matt joining me for the first full interview episode of The Saturday Fraud Strategist. Honestly, not a bad way to start. In this episode, Matt and I talk about what it actually takes to build real-time fraud prevention from zero. Not the polished vendor version. The real version. The one with hiring decisions, messy processes, fragile fraud prevention tech stacks, disconnected vendors, and systems that look impressive right up until they break. Not a good look. While real-time fraud detection sounds like a technology problem, the conversation goes deeper. We talk about people, process, product, real-time fraud monitoring, tactical friction, fraud prevention guardrails, AI readiness, and why teams need to move upstream before the money is gone. Because once the payment moves, especially in real-time transaction monitoring or real-time payment environments, you are not preventing fraud anymore. You are documenting the damage. What you’ll hear in this episode:A breakdown of Matt Vega’s people, process, and product framework for real-time fraud preventionA practical discussion of how to build a fraud prevention strategy from zeroInsight into hiring for curiosity, trust, flexibility, and actual problem-solving abilityA conversation about reactive vs proactive fraud prevention in real-time payment environmentsA focused look at upstream fraud detection, tactical friction, and why friction done right can increase trustPractical considerations for building a fraud prevention tech stack where vendors, signals, and workflows actually communicateA discussion of AI fraud prevention, machine learning fraud detection, and agentic AI in fraud prevention Listeners can expect a conversation that moves from theory to operating reality, and from operating reality to practical decisions fraud teams can actually use. Who should listen:Fraud leaders and fraud professionalsRisk, compliance, and cybersecurity teamsFintech, banking, and payments teamsProduct leaders building real-time payment experiencesFraud operations teams moving from manual review to automationFounders, operators, and executives building fraud prevention programs from scratch Anyone evaluating fraud detection rules, behavioral biometrics, device intelligence, KYC fraud prevention, account takeover prevention, or the best fraud prevention tools for their stack. The discussion is designed for professionals who are committed not only to detecting fraud, but to building systems that can scale without becoming fragile.

    1h 4m
  8. Why Leaders Choose Worse Fraud Tools

    May 14

    Why Leaders Choose Worse Fraud Tools

    In this episode, I start with a slightly strange moment at the Mastercard offices. I was catching up with someone I know and he told me I had started pushing a new narrative. Okay. Apparently, the narrative was that rules are better than AI. Honestly, I get why it looked that way. I talk about rules vs AI in fraud prevention quite a bit. But that is not really the point. The point is control. AI fraud prevention, fraud prevention AI, AI fraud detection, machine learning fraud prevention, all of it sounds great until the person responsible for money movement and customer acquisition has to approve the change. Then accuracy is not the only thing that matters. Trust matters. Explainability matters. Strategy visibility matters. And if leaders do not feel in control, they will choose worse fraud tools. Not because they are irrational. Because breaking the business is, technically speaking, not a good look. What you will hear in this episode:A breakdown of why the “rules vs AI in fraud prevention” debate misses the bigger issueWhy leaders often choose fraud detection rules over stronger AI fraud toolsHow fraud risk management changes when the process touches money movement and customer acquisitionWhy fraud decisioning depends on trust, not just model accuracyWhat fraud AI tools often get wrong about explainabilityHow chargeback rate optimization can become more useful when users can compare low, medium, and high-risk strategiesWhy AI trust in fraud prevention depends on clear KPIs, plain answers, and visible tradeoffsListeners can expect a conversation that moves from “which tool performs better?” to the more uncomfortable question: who actually feels safe enough to make the decision? Who should listen:Fraud leaders and fraud operatorsRisk and compliance teamsProduct teams building fraud AI toolsFinancial institution leaders evaluating AI fraud preventionFraud technology vendors and solution architectsAnyone involved in fraud decisioning, chargeback rate optimization, or machine learning fraud prevention Basically, if you have ever looked at a model and thought, “The performance is better, so why won’t they use it?” this one is for you.

    6 min
  9. Why I Joined Sardine

    May 14

    Why I Joined Sardine

    I wanted to take a step back and talk about something a bit more personal, but also very relevant to how I think about fraud prevention strategy. It’s been six months since I joined Sardine. And I figured it makes sense to explain how I got here, because honestly, the decision wasn’t hard, but it wasn’t simple either. This isn’t just about changing roles. It’s about moving from fraud prevention consulting into something broader, where I can connect content, product, and strategy in a way that actually helps fraud fighters do their job better. And along the way, it raises a bigger question: what does an effective fraud prevention strategy actually look like when you’ve been on the practitioner's side for long enough? What you will hear in this episode:A personal breakdown of why I moved from solo consulting back into a teamWhy “freedom” in consulting isn’t always what it seemsHow I think about fraud prevention strategy after years in the fieldWhat made Sardine stand out as a fraud prevention platformWhy practitioner-led content matters more than everHow fraud prevention solutions should actually be built and evaluated Who should listen:Fraud fighters working in fintech fraud prevention and enterprise fraud preventionRisk, compliance, and product teams evaluating fraud prevention solutionsProfessionals working in fraud prevention consultingAnyone thinking about the gap between vendor promises and real-world fraud operationsAnyone trying to build or choose a fraud prevention platform that actually works If you’ve ever asked yourself whether the tools you’re using really solve your problems, this one will probably resonate.

    7 min
  10. My Stolen Identity is Cheating on My Wife

    May 14

    My Stolen Identity is Cheating on My Wife

    A few months ago, I woke up registered on a dating app. I'm in a 13-year relationship. I did not sign up. Someone used my email to create a profile on Coffee Meets Bagel. When the platform froze the account, the scammers opened another one. Same email. Same details. Instantly. By the third account, customer support still hadn't replied. What starts as fake dating profiles doesn't stay there. They become romance scams. Then a loss your FI absorbs while the dating platform moves on with no accountability for the identity verification failure and no skin in the game. Dating platforms have no chargebacks, no regulatory pressure, and no reason to fix the lack of account takeover detection. So who actually bears the cost, and why are we still waiting for them to care about APP fraud prevention? What this episode coversHow dating app identity theft becomes a gateway to romance scams and APP fraudWhy dating platforms have no structural incentive to prevent account misuseWhat the upstream fraud trail looks like, and who ends up paying downstream Who should listenFraud ops teams at FIs and fintechsTeams working with identity verification systemsRisk and compliance professionals tracking APP fraud vectorsAnyone watching romance scam trends

    6 min

  11. May 14

    The Saturday Fraud Strategist

    Fraud strategy that's actually practical. 16 years of experience, zero fluff, one actionable takeaway every episode. Chen Zamir goes deep on fraud strategy at the intersection of business growth, customer experience, and risk management. No news digests, no vendor pitches, no recycled content. Just real talk for fraud professionals, product managers, and executives who want to think sharper about risk. New episodes every Saturday. Subscribe and join a community that's already changing how the industry thinks about fraud. Got a question or topic you want covered? Drop a comment or reach out directly. Chen reads everything.

    1 min
  • 11 Episodes

About

Fraud strategy. No fluff. Real talk from 16 years in the industry, every Saturday. Chen Zamir breaks down the decisions, frameworks, and hard calls behind fraud strategy for professionals who want practical insights they can actually use. Whether you work in fraud, product, or the C-suite, every episode leaves you with one clear takeaway. New episode every Saturday. Subscribe so you never miss one.

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