The Saturday Fraud Strategist

Chen Zamir
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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.

  1. 7h ago

    False Positives Masterclass Part 4: Building a safety net over your fraud stack

    Here is the uncomfortable thing about fraud systems: even when every individual part looks reasonable, the whole thing can still behave like a maze. You fix one rule. Great. You tune a model. Nice. You clean up a manual review flow. Very responsible. And then a good user still gets blocked somewhere else because another rule, partner response, payment routing decision, KYC check, AI agent, device intelligence signal, or some forgotten logic from three quarters ago decided to step in and say, absolutely not. In this episode of the False Positives Masterclass, I’m talking about fraud override logic, which is one of the more powerful tools mature fraud teams can use when reducing false positives across a complex fraud stack. The idea is simple in theory: build a high-level safety net over the system that can recognize users you already have strong reason to trust, even if one actor in the stack tries to block them. But simple in theory is where many bad fraud ideas are born. So we need to be careful. A fraud system override is not a shortcut. It is not a “good vibes” approval layer. It is not an excuse to ignore bad logic underneath. It is a controlled, evidence-based mechanism that asks: before we block this user, do we have airtight evidence that they are actually legitimate? That sounds obvious. It is not. Otherwise, more teams would do it well. What you’ll hear in this episode:Why even well-tuned fraud prevention logic can still create false positivesHow fraud override logic works as a safety net over rules, models, AI agents, manual review, KYC checks, and partner responsesWhy some fraud detection rules should never be overridden automaticallyHow known good users and inherited trust signals can help reduce false positivesWhy high-exposure environments can be useful false positive indicatorsHow geo-chaining can help distinguish travelers and legitimate mismatches from fraudWhy non-resellable or low-risk items can support safer payment fraud approvalsHow to deploy fraud override systems safely using shadow mode testing and gradual rollout You should listen to this episode if you:Work in fraud operations and your stack has too many independent blocking pointsAre trying to reduce false positives without weakening fraud detection rulesNeed a safer way to identify trusted users across accounts, devices, cards, or flowsWant practical examples of fraud override logic beyond generic allowlistsAre evaluating when to use device intelligence, geo-chaining, manual review, or challenger rules to improve decisioning

    False Positives Masterclass Part 4: Building a safety net over your fraud stack

  2. Jul 18

    How to Pick the Right Fraud Vendor For You, with Holly Sandberg

    Choosing the right fraud vendor can be challenging without falling for the wrong promises. A few years ago, I watched a fraud vendor demo what was, honestly, too smooth. The dashboard was clean. The detection sounded instant. The integration was described as “lightweight,” which is one of those words that should immediately make everyone in the room sit up a little straighter. Okay. Lightweight for who? Because fraud systems do not live in slide decks. They live inside messy customer journeys, half-documented data flows, payment edge cases, manual review queues, chargeback rules, executive pressure, and a backlog that engineering has already politely said is “full.” So when a vendor says they can reduce fraud, improve approvals, lower chargebacks, protect revenue, and do it all quickly, you’ve got to ask yourself a very basic question. What are they not saying? Not in a conspiracy way. Just in a normal, practical, “someone is eventually going to have to explain this to leadership” kind of way. That is where this episode of the Saturday Fraud Strategist Podcast begins. Not with the shiny version of picking fraud vendors, but with the version fraud teams actually live through. The one with good intentions, unclear requirements, pressure from every direction, and a tool that may or may not behave the same way after the contract is signed. Picking fraud vendors is not just procurement. It is not just technology. It is not even just fraud strategy. It is an accountability decision. A bad choice can create false declines, missed fraud, operational cleanup, customer frustration, revenue loss, and a fraud team stuck explaining why the thing that was supposed to make life easier has somehow created a new category of meetings. Anyway. Very normal. Very fun. What you’ll hear in this episode:A structured breakdown of picking fraud vendors, and why there is no single “right” vendor.A practical look at what is working, and what is still falling short, in today’s crowded fraud technology market.A discussion of how institutions should think about internal alignment, stakeholder buy-in, and fraud vendor implementation realities.A closer look at ethical tensions around marketing claims, fraud vendor black box models, guarantees, and accountability.An examination of how poor vendor decisions affect fraud teams, customers, revenue, chargebacks, and business operations.Practical considerations for due diligence, fraud vendor POC planning, integrations, contracts, fraud vendor SLA terms, and escalation paths.A comparison of how teams should think about a chargeback vendor, fraud prevention platform, fraud detection platform, identity verification vendor, or doc verification vendor, depending on the actual problem they need to solve.A call for better fraud vendor relationship management between merchants, fraud leaders, vendors, engineering teams, finance, legal, product, and trust and safety stakeholders. Who should listen:Financial institution leaders and fraud professionals.Risk, compliance, trust and safety, and cybersecurity teams.Merchant-side fraud leaders evaluating vendors or dealing with inherited systems.Product, engineering, finance, and legal stakeholders involved in fraud technology decisions.Fraud vendors, solution providers, and customer success teams.Industry advocates focused on stronger fraud prevention outcomes.Anyone trying to understand how vendor selection impacts real people, real teams, and real institutions.

    How to Pick the Right Fraud Vendor For You, with Holly Sandberg

  3. Jul 11

    False Positives Masterclass Part 3: How to reduce FPs inside your system

    Okay, so here is the thing about reducing false positives. Most teams want to jump straight into tactics. Tune the rule. Adjust the threshold. Add an exemption. Move the weird edge cases into manual review. Fine. All of that might be useful. But honestly, if that is where you start, you are probably guessing. And guessing in fraud prevention is not exactly my favorite operating model. Not because it never works. Sometimes it does. Which is almost worse, because then everyone gets confident. Not a good look. In this episode, I continue the False Positives Masterclass by moving from measurement and bucketing into the part everyone actually wants to get to: fixing the parts of the system that are misbehaving. But the point is not just to reduce false positives. The point is to reduce false positives without creating a new fraud problem you only discover three weeks later when the losses mature and everyone starts quietly looking at the dashboard like it personally betrayed them. This episode is about discipline. It is about manual review, fraud rules, fraud model precision, fraud model recall, shadow mode testing, data quality issues, and the uncomfortable but necessary question every fraud team eventually has to ask: is this rule actually helping, or have we just been emotionally attached to it since that one fraud spike in 2022? What you’ll hear in this episode:Why reducing false positives should start with manual review, not instinctHow to decide whether a fraud rule should be removed, downgraded, or improvedWhy fraud model precision and fraud model recall matter when rules catch fraud but hurt good usersHow to build exclusions without accidentally creating a back door for fraudstersWhy shadow mode testing and challenger rules are essential before releaseHow data quality issues can make otherwise reasonable fraud prevention logic misbehaveWhy fraud operations teams need to be pragmatic, not elegant, when the data is broken You should listen to this episode if you:Own fraud rules, fraud detection rules, models, AI agents, or review flowsAre trying to reduce false positives without increasing fraud lossesHave a high false positive rate but are not sure which part of the system is causing itNeed a more structured way to review manual review samples and top offendersAre dealing with corrupted data, noisy signals, or flows where fraud prevention logic keeps misfiring

    False Positives Masterclass Part 3: How to reduce FPs inside your system

  4. Jul 4

    What AdTech Taught Me About Financial Fraud, with Gilit Saporta

    In this episode of The Saturday Fraud Strategist, I talk with Gilit Saporta about ad tech in financial fraud, which sounds very niche until you realize it touches malware, fake traffic, bot detection, consumer abuse, advertising fraud, and a lot of systems quietly pretending they have this handled. Ad tech fraud is not just “someone clicked a fake ad.” That would almost be simple. What we’re really talking about is a whole ecosystem where fraudsters monetize traffic, hijack devices, manipulate advertising spend, and sometimes pull regular people into the mess without them even knowing it happened. Gilit brings nearly two decades of fraud-fighting experience across financial services, crypto, e-commerce, and digital advertising, so the conversation gets practical pretty quickly. We look at what makes ad tech in financial fraud different from traditional fraud, where fraud detection is improving, and where systems still fall apart because the context is missing. And honestly, that’s the part that keeps coming up. You can have AI fraud prevention. You can have automated fraud detection. You can have dashboards that look very impressive in a board meeting. But if the system can’t tell the difference between a weird but legitimate pattern and an actual fraud signal, now you’ve got a problem. Maybe a very expensive problem. What you’ll hear in this episode:A step-by-step breakdown of how ad tech fraud works and why it does not behave like traditional financial fraudA practical look at what modern fraud detection is getting right and where it still strugglesWhy institutions, platforms, and technology teams need to stop treating fraud risk management as someone else’s cleanup jobA conversation about AI-powered fraud fighting, automation, accountability, and the uncomfortable gap between speed and judgmentHow consumers get pulled into device hijacking, malware, scams, fake apps, and fraudulent advertising ecosystemsWhy better financial fraud prevention depends on context, not just bigger models or faster alertsA discussion about collaboration between fraud teams, trust and safety, researchers, regulators, and the organizations sitting on all the useful data Who should listen:Fraud professionals and financial institution leadersRisk, compliance, and cybersecurity teamsAI, machine learning, and fraud analytics practitionersTrust and safety teamsAd tech, e-commerce, and digital platform leadersRegulators, policy advisors, and industry advocatesAnyone trying to understand why fraud keeps finding the cracks between systems This episode is for people who care about fraud prevention strategies beyond the press release version. Detection matters. Compliance matters. But you’ve got to ask yourself, are we actually preventing harm, or are we just getting better at labeling it after the fact?

    What AdTech Taught Me About Financial Fraud, with Gilit Saporta

  5. Jun 27

    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?”

    False Positives Masterclass Pt. 2

  6. 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.

    Why PSPs Struggle With Fraud Prevention Technology

  7. 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.

    Should Fraud and Cybersecurity Teams Converge?

  8. 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.

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