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 positives
• How fraud override logic works as a safety net over rules, models, AI agents, manual review, KYC checks, and partner responses
• Why some fraud detection rules should never be overridden automatically
• How known good users and inherited trust signals can help reduce false positives
• Why high-exposure environments can be useful false positive indicators
• How geo-chaining can help distinguish travelers and legitimate mismatches from fraud
• Why non-resellable or low-risk items can support safer payment fraud approvals
• How 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 points
• Are trying to reduce false positives without weakening fraud detection rules
• Need a safer way to identify trusted users across accounts, devices, cards, or flows
• Want practical examples of fraud override logic beyond generic allowlists
• Are evaluating when to use device intelligence, geo-chaining, manual review, or challenger rules to improve decisioning

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.

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 instinct
• How to decide whether a fraud rule should be removed, downgraded, or improved
• Why fraud model precision and fraud model recall matter when rules catch fraud but hurt good users
• How to build exclusions without accidentally creating a back door for fraudsters
• Why shadow mode testing and challenger rules are essential before release
• How data quality issues can make otherwise reasonable fraud prevention logic misbehave
• Why 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 flows
• Are trying to reduce false positives without increasing fraud losses
• Have a high false positive rate but are not sure which part of the system is causing it
• Need a more structured way to review manual review samples and top offenders
• Are dealing with corrupted data, noisy signals, or flows where fraud prevention logic keeps misfiring

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 fraud
• A practical look at what modern fraud detection is getting right and where it still struggles
• Why institutions, platforms, and technology teams need to stop treating fraud risk management as someone else’s cleanup job
• A conversation about AI-powered fraud fighting, automation, accountability, and the uncomfortable gap between speed and judgment
• How consumers get pulled into device hijacking, malware, scams, fake apps, and fraudulent advertising ecosystems
• Why better financial fraud prevention depends on context, not just bigger models or faster alerts
• A 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 leaders
• Risk, compliance, and cybersecurity teams
• AI, machine learning, and fraud analytics practitioners
• Trust and safety teams
• Ad tech, e-commerce, and digital platform leaders
• Regulators, policy advisors, and industry advocates
• Anyone 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?

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 tuning
• How to identify who actually declined the event: a rule, fraud model threshold, AI agent, fraud analyst, manual review team, issuer, acquirer, or fraud vendor
• Why upstream payment partners can create false positives your own fraud prevention systems cannot directly fix
• How fraud decisioning breaks down across payment fraud detection, fraud risk scoring, and operational workflows
• Why fraud system optimization starts with identifying the worst offenders
• How data quality issues, corrupted fraud signals, and model drift create false positives that look like fraud risk
• How 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 accuracy
• Fraud analysts working through manual review queues
• Risk teams managing fraud rules, fraud model thresholds, and fraud risk scoring
• Data science teams responsible for fraud detection models and model drift
• Payment fraud detection teams dealing with issuer declines and upstream partner decisions
• Fraud prevention teams trying to reduce false positives without increasing losses
• Anyone who has ever stared at a false positive dashboard and thought, “Okay, now what?”

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 service
• Where fraud prevention software becomes more complicated than expected
• Why AI fraud detection and machine learning fraud detection are not enough on their own
• How fraud scoring can create confusion if merchants do not know how to act on it
• Why merchant fraud prevention requires operational support, not just fraud detection software
• How chargeback prevention, false positive reduction, and payment risk management affect real business outcomes
• Why fraud prevention for payment service providers needs to include strategy, support, and fraud investigation tools

Who should listen:

• Payment service providers considering fraud prevention technology
• PSP product leaders building payment fraud prevention services
• Fraud operations and payment risk management teams
• Merchants evaluating fraud prevention software or fraud management software
• Risk leaders responsible for chargeback prevention and false positive reduction
• Teams working with fraud detection models, fraud scoring, or fraud investigation tools
• Anyone 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.

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 signals
• How credential stuffing and account takeover pushed security tools into fraud prevention use cases
• Why AI phishing detection depends on more than static email rules or reputation checks
• How AI email security is changing as attackers use AI to generate more targeted phishing attacks
• Where invoice fraud, ACH fraud, and accounts payable fraud sit between security and fraud operations
• Why security telemetry and fraud telemetry become more useful when teams connect the full user journey
• How Recaptcha evolved from image puzzles into behavioral detection and fraud prevention infrastructure
• Why “good people leave tracks” still applies across both fraud and security signals
• How upstream fraud detection helps stop problems before money leaves the platform
• Why fraudster ROI is one of the most useful ways to think about modern defense
• What 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 analysts
• Cybersecurity professionals
• Trust and safety teams
• FinTech fraud prevention teams
• Email security teams
• Accounts payable and payment risk teams
• Teams evaluating AI phishing detection or AI email security vendors
• Anyone 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.

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 loops
• How declined transactions disappear from fraud analytics and model training data
• Why chargeback data is easier to measure than blocked legitimate users
• A breakdown of fraud rules simulation and where simulation fails operationally
• How manual review helps identify hidden false positives inside payment fraud detection systems
• Why entity resolution becomes one of the strongest tools for linking blocked users to later legitimate behavior
• How control groups expose hidden weaknesses in fraud decisioning systems
• Where user feedback loops can help, and where they become dangerous
• Why fraud prevention strategy depends on understanding false positive reduction at the operational level
• How 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 analysts
• Risk and compliance teams
• Fraud operations managers
• FinTech fraud prevention teams
• Payment fraud detection professionals
• Teams managing fraud decisioning systems
• Data science and fraud analytics teams
• Anyone 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.

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 prevention
• Why fraud analysts relied heavily on publicly available information and digital breadcrumbs
• A real fraud investigation story involving Facebook, geolocation mismatch, and identity verification
• How online privacy and data privacy reshaped fraud prevention workflows
• Why social media OSINT became harder as platforms tightened customer privacy controls
• How open source intelligence techniques evolved from manual investigation into AI OSINT tools
• Why identity intelligence became more difficult once social networks reduced public visibility
• A practical discussion about OSINT for fraud prevention and its limits today
• How scammers and social engineering scams changed the privacy conversation entirely
• Why 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 investigators
• Trust and safety professionals
• FinTech fraud prevention teams
• Risk and compliance professionals
• OSINT and digital investigation practitioners
• Cybersecurity 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.

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 specialized
• Why KYC fraud checks, document checks, selfies, and 3D liveness can no longer carry the whole fraud prevention strategy
• How device intelligence asks different questions than a KYC vendor
• Why behavioral signals and behavioral biometrics can expose what a document check misses
• How identity intelligence helps connect emails, phone numbers, addresses, and documentation into a more cohesive picture
• Why post-signup fraud monitoring and high-risk user monitoring matter after account opening
• How step-up verification can add friction only when the risk actually justifies it
• Why KYC vendor orchestration can be useful for a small, high-risk segment
• How 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 operators
• Risk and compliance teams
• FinTech teams managing onboarding and account opening fraud
• Trust and safety professionals
• Identity verification and KYC teams
• Teams 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.

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 prevention
• A practical discussion of how to build a fraud prevention strategy from zero
• Insight into hiring for curiosity, trust, flexibility, and actual problem-solving ability
• A conversation about reactive vs proactive fraud prevention in real-time payment environments
• A focused look at upstream fraud detection, tactical friction, and why friction done right can increase trust
• Practical considerations for building a fraud prevention tech stack where vendors, signals, and workflows actually communicate
• A 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 professionals
• Risk, compliance, and cybersecurity teams
• Fintech, banking, and payments teams
• Product leaders building real-time payment experiences
• Fraud operations teams moving from manual review to automation
• Founders, 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.

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 issue
• Why leaders often choose fraud detection rules over stronger AI fraud tools
• How fraud risk management changes when the process touches money movement and customer acquisition
• Why fraud decisioning depends on trust, not just model accuracy
• What fraud AI tools often get wrong about explainability
• How chargeback rate optimization can become more useful when users can compare low, medium, and high-risk strategies
• Why AI trust in fraud prevention depends on clear KPIs, plain answers, and visible tradeoffs
• Listeners 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 operators
• Risk and compliance teams
• Product teams building fraud AI tools
• Financial institution leaders evaluating AI fraud prevention
• Fraud technology vendors and solution architects
• Anyone 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.

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 team
• Why “freedom” in consulting isn’t always what it seems
• How I think about fraud prevention strategy after years in the field
• What made Sardine stand out as a fraud prevention platform
• Why practitioner-led content matters more than ever
• How fraud prevention solutions should actually be built and evaluated

Who should listen:

• Fraud fighters working in fintech fraud prevention and enterprise fraud prevention
• Risk, compliance, and product teams evaluating fraud prevention solutions
• Professionals working in fraud prevention consulting
• Anyone thinking about the gap between vendor promises and real-world fraud operations
• Anyone 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.

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 covers

• How dating app identity theft becomes a gateway to romance scams and APP fraud
• Why dating platforms have no structural incentive to prevent account misuse
• What the upstream fraud trail looks like, and who ends up paying downstream

Who should listen

• Fraud ops teams at FIs and fintechs
• Teams working with identity verification systems
• Risk and compliance professionals tracking APP fraud vectors
• Anyone watching romance scam trends

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.

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