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.

  1. 5d ago

    Fraud rule management: strategy, behavioral and trend rules need different owners and cadences

    We talk about fraud rules all the time, but I rarely hear anyone ask what we actually mean by the word. What are rules for, who should write them, and how often should they be reviewed? Over the course of my career, I've found that fraud rule types fall into three distinct categories, and most teams manage all of them the same way. That habit quietly costs a lot. Teams end up over-monitoring some rules, under-monitoring others, handing ownership to the wrong people, and expecting rules to perform in ways they were never designed for. In this episode, I walk through strategy rules, behavioral rules, and trend rules, and explain how each one is written, measured, and owned. If you manage a rule engine, or you're responsible for how a fraud team spends its time, this is a practical way to organize that work. What you'll hear in this episode:Why the three fraud rule types need different monitoring cadences, different owners, and different success criteriaHow ML score fraud rules and threshold fraud rules segment your population into approve, decline, and step-up groupsWhy changing one threshold in a strategy ruleset often means adjusting the others, and why those rules should be measured as a setHow behavioral rules fraud teams write, including velocity rules fraud examples, can catch several rings with the same MO without overfitting to one attackWhat compound fraud rules look like in practice, including a device fingerprint fraud rules example that combines email risk, a new device, and a country mismatchWhy trend rules are your fastest and most accurate tool at first, and why they degrade so quicklyHow the fraud rule lifecycle should work, with narrow rules acting as a stopgap while stronger defenses take overWhat goes wrong with fraud rule prioritization when every rule gets the same attention You should listen to this episode if you:Manage a rule engine and want a clear framework for how to write fraud rules that fit their purposeAre building or restructuring a fraud team and need to decide which team should own which rulesWant to improve fraud rule deployment by tying strategy rules to your model release processKeep adding narrow rules to stop attacks and are worried about the false positives piling upRely on a vendor model and need to know what to ask them for when setting thresholds

    Fraud rule management: strategy, behavioral and trend rules need different owners and cadences

  2. Oct 10

    Scaling Agentic AI: Lessons From the Field

    A few weeks ago I walked through the framework for the AI transformation journey. This conversation with Lalitha Rao, Chief Risk Officer at Imprint, is the natural next step. It is a real, in-the-field look at what it actually takes when a team commits to scaling agentic AI past the theory stage. In the nine months since her risk team started building out their agentic AI infrastructure in earnest, they’ve grown their loan book by 150%, increased revenue by 2.5x, and actually lowered loss rates on newer cohorts. All without cutting a single headcount. Lalitha’s team didn’t treat this as a cost-cutting exercise, they treated it as a way to create more value for Imprint's members and partners. And the results speak for themselves. If you’re further along in your own AI transformation journey and want a real example of what scaling agentic AI actually looks like once the framework meets the field, this is the conversation. What you'll hear in this episode:Why Imprint's centralized AI platform, built by their CTO's team, turned out to matter more than any individual agent they've builtHow ARIA, an autonomous credit policy agent, scours bureau and internal data to find early signs of portfolio deterioration and pushes the efficient frontier outward instead of just optimizing itWhy agentic AI unlocked something a SQL query or a standalone model couldn't, digesting multiple data sources in different formats into one coherent, repeatable workflowHow a residual mining agent continuously finds model errors, builds new features, and retrains models using out-of-time testing to guard against overfittingWhy Imprint never measured success by headcount reduction, and what they built instead with the team's freed-up timeInside Project Shield, a suite of ten fraud agents that runs for six hours, costs about $300 in tokens, and saves hundreds of thousands of dollars in fraud lossesHow a shared skills repository and clear data ownership let a non-engineering risk team build and maintain production-grade agentsLalitha's honest build versus buy framework, and the specific vendors, including Sardine, LexisNexis, and Proof, Imprint still relies onThe real hiccups Imprint hit along the way, bloated proposals, false certainty in AI-generated reviews, and how they built guardrails to fix both You should listen to this episode if you:Are further along in the AI transformation journey and want a concrete look at what scaling actually requires beyond the initial frameworkLead a risk, fraud, or credit team and are trying to decide which agentic AI use cases to prioritize firstAre weighing build versus buy decisions for AI agents and want a clear mental model for where proprietary data changes the calculusWorry that scaling AI means shrinking your team, and want to hear a real counterexampleWant practical detail on how to actually enable non-engineers to build and maintain production-grade AI agents

    Scaling Agentic AI: Lessons From the Field

  3. Oct 3

    Is It 1st Party Fraud or 3rd Party Fraud?

    When you get hit by a fraud attack, the second question should be what kind of fraud am I actually dealing with. First party fraud vs third party fraud isn’t just a technical distinction. It fundamentally changes how you respond and what tools you deploy. I’m constantly amazed by how many teams either can’t tell the difference or don’t think it matters. This episode is about fixing that. What you’ll hear in this episode:The core definitions of first party fraud and third party fraudThe specific signals that separate the two in practiceTwo real, costly examples of teams deploying the wrong toolsWhy third party fraud may be easier to detect than most people assume and why first party fraud may be harderThe specific tool stack each fraud type requiresWhy most fraud prevention tooling was built around third party fraud by default You should listen to this episode if you:Are trying to diagnose a rising fraud rate and aren't sure whether you're facing first party or third party fraudHave invested in KYC identity verification or device fingerprinting fraud tools without seeing the results you expectedAre responsible for account takeover detection and want a clearer sense of which signals actually matterNeed to make the case internally for fraud prevention ROI by matching the right tool to the right fraud typeAre dealing with money mule detection or collusion fraud cases that don't cleanly fit either category

    Is It 1st Party Fraud or 3rd Party Fraud?

  4. Sep 26

    Who is the Saturday Fraud Strategist, with Hailey Windham

    I handed my microphone over to Hailey Windham, host of Fraud Forward. For once, I'm the one answering the questions instead of asking them. Hailey opens by asking how I'd describe my mission today, since "builder" doesn't quite cover what I do anymore between advising, educating, and writing. That question sets up the real spine of this conversation. What fraud strategy actually means, why I think predictive fraud analytics is mostly a fabrication, and how a fraud strategist's real job is making sure a team can react well when things break, not pretending anyone can stay ahead of fraudsters. Hailey and I talk about where healthy skepticism turns into a blind spot, why foundation models are proving more valuable for enriching data than for scoring transactions, and I close out with real advice for anyone quietly wondering whether they could go independent themselves.

    Who is the Saturday Fraud Strategist, with Hailey Windham

  5. Sep 19

    The Rise of Agentic Ops, part 4: How to Monitor AI Agents

    If one of your AI agents had been degrading for six weeks would you know? Most people tell me no, and that’s the problem I’m digging into. Drifting agents can generate outputs that look fine on the outside while quietly getting worse underneath. Earlier in this series I talked about the reaction cycle as the master KPI of fraud effectiveness, and how agentic AI can make that cycle dramatically faster. This time I want to answer the question that matters once you’ve deployed those agents. How do you know they’re still working. Most dashboards answer the wrong questions and only answer whether an agent is running. AI agent monitoring means tracking an agent deliberately, and I will walk you through exactly how to do it. What you’ll hear in this episode:Why a degrading agent is genuinely more dangerous than no agent at all.How to measure fraud reaction cycle speed at each individual stage rather than just watching one lagging number.Why AI agent performance metrics fraud teams should track don’t need to be perfectly automated to be useful.The difference between human-in-the-loop agent monitoring and autonomous agent monitoring for agents making decisions at scale.What a rising rejection rate actually tells you.How to catch a silently failing autonomous agent before real damage compounds.A practical three-layer AI agent monitoring dashboard fraud teams can build. You should listen to this episode if you:Are running any agentic fraud ops monitoring program and want a real framework for catching a degrading agent before it shows up in your losses.Are responsible for AI agent governance fraud policies and need language that connects technical monitoring to leadership reporting.Have deployed human-in-the-loop tools like investigation copilots or rule recommendation agents and want to know what to actually track.Are running autonomous agents, like auto-labeling or alert clustering, with no human reviewing every decision, and worry about silent failure.Want to build a genuine business case for AI agent ROI fraud investment using the reaction cycle instead of just automation hours saved.

    The Rise of Agentic Ops, part 4: How to Monitor AI Agents

  6. Sep 13

    The AI Adoption Journey for Fraud & Risk Teams

    If you’ve been following fraud on LinkedIn for any real stretch of time, you are probably familiar with Brian Davis and have been reading his posts. He has been the first fraud hire at many companies across physical goods, e-commerce, marketplaces, and fintech. Today he sits at the center of it all running deep dive retreats through Safeguard. I have talked with Brian before about AI adoption for fraud and risk teams, and he said something that stuck with me. There’s a real difference between AI activation and AI enablement. Most organizations think they’ve done the second when really they’ve only done the first. I wanted to bring our conversation to all of you, because I think fraud teams may underestimate or overestimate where they sit on this journey. What you’ll hear in this episode:Why AI activation vs AI enablement is the distinction most companies get wrong, and what it actually looks like when you throw a tool over the fence with no guidance. Brian's four pillars for real AI enablement are clear policies, actual training, dedicated incentives and time, and a feedback loop that doesn't die after three weeks.Why change management for fraud teams is really a people management problem wearing a technology costume.Brian's full five-stage framework, covering AI blocked, AI aware, AI enabled, AI first, and AI native, and how to honestly assess where your own team sits.Why AI enablement leadership buy-in has to start at the top, and how executives showing their own AI usage removes the imposter syndrome holding everyone else back.Why so many fraud teams try to go big on day one, transaction monitoring, KYC, and end up frustrated, when the smarter path is workflow design for AI adoption that starts small.Concrete, non-technical AI use cases for fraud, including pattern analysis, internal reporting, and OKR alignment with sales and marketing.How reducing engineering dependency with AI is changing what fraud analyst upskilling with AI actually looks like day to day.Brian's personal framework for fraud practitioner AI use cases, including his own daily habits and how he thinks about build versus buy AI fraud tools. You should listen to this episode if you:Are a fraud or risk leader trying to figure out whether your team is actually AI enabled or just AI activatedAre responsible for fraud team AI training or building out fraud team AI governance policies from scratchAre a fraud analyst wondering how to build AI literacy without waiting for your company to hand you a roadmapAre trying to motivate a team through fraud team change management without losing the people who are cautious about the changeAre comparing AI first fraud organizations against the fully rebuilt AI native fraud teams and wondering which one is actually the realistic goal

    The AI Adoption Journey for Fraud & Risk Teams

  7. Sep 5

    The Rise of Agentic Fraud Ops, part 3: Scaling Fraud Analytics

    Risk leaders are under pressure right now to use AI to cut costs. Cutting costs usually means cutting headcount. In fraud operations specifically, that instinct creates a blind spot in teams. The previous episodes in this series walked through what a transformation actually looks like moving from manual fraud operations to AI powered ones. What those episodes didn’t get into is why scaling fraud analytics has to happen alongside that shift. There’s a second order effect almost nobody plans for. One function doesn’t shrink when a team adopts AI, it has to grow. If a fraud team headcount planning doesn’t account for that, the result is a smaller team that isn’t actually equipped to govern the automated systems it just deployed. That is fraud analytics. Skipping its growth is how AI rollout quietly turns into a bigger risk than the manual process it replaced. What you’ll hear in this episode:Why scaling fraud analytics matters more than any other staffing decision in an AI transformation, and most teams get this backwards.Why most fraud teams break down into the four functions of fraud ops, fraud analytics, fraud strategy, and data science in fraud teams. And why almost none of them have all four fully staffed.Why fraud ops vs fraud analytics respond in opposite directions to AI adoption.The is a real difference between reviewing an individual agent decision and governing a fully automated pipeline at scale.What silent pipeline failure actually looks like in practice, and why automated systems don’t announce when they’ve gone wrong.Why rule writing automation still requires human review, and what that review has to catch.How KPI monitoring for automated systems and root cause analysis in fraud systems are skills fraud teams already have, just aimed at a new target.Where fraud team restructuring for AI usually breaks down in quarterly reviews. Why it happens when missing error thresholds, and because audits happen monthly instead of weekly.Why fraud analysts, not investigators or engineers, are becoming the new AI team leaders.How to think about fraud team budget planning during this shift, including funding analytics growth from fraud ops savings. You should listen to this episode if you:Lead a fraud team currently planning or mid-way through an AI transformation and haven't yet mapped what happens to your analytics functionAre under pressure to cut fraud team headcount and need a clear argument for where that logic breaks downHave deployed or are about to deploy agentic AI for investigations, labeling, or rule writing and want to understand the governance gap most teams missAre building a fraud team budget case for your board and need language that connects cost savings to where they should actually be reinvestedWant a practical framework for fraud team org design that accounts for pipeline-level monitoring, not just individual case reviewAre wondering whether your fraud analytics function is sized for the automation you're already running, or the automation you're about to add

    The Rise of Agentic Fraud Ops, part 3: Scaling Fraud Analytics

  8. Aug 29

    What’s New in Merchant Fraud, with Dajana G.

    I have been wanting to have this conversation for a while. Dajana Gajic-Fisic has 26 years in merchant fraud. She started at Macy's in 2000, calling Visa and Mastercard in multiple languages to manually verify addresses at the point of sale. She has watched e-commerce fraud detection evolve from its earliest form through chip and pin migration, the explosion of online fraud, and now the AI era. She is currently VP of Fraud Strategy at The Wolfe Companies, working in the gift card space, which if you think has no fraud, you would be wrong. What I liked most about this conversation is that Dajana is not someone who talks about merchant fraud from the sidelines. She fights it every day, including building out merchant fraud ring detection processes from scratch and figuring out how to detect merchant fraud attacks before they ever reach a payment page. That gives her a perspective on what is actually happening right now versus what the industry tends to talk about. We covered a lot of ground. One thing I want to flag before you dive in. This is one of those conversations where we keep coming back to basics. Not because the threat landscape is simple. But because getting the basics right is actually how you handle whatever the threat landscape throws at you next. I thought that was worth saying up front. What you’ll hear in this episode:Why e-commerce fraud trends follow predictable patterns when the environment shifts, and what the chip and pin migration of 2015 tells us about AI todayHow Dajana thinks about AI powered fraud attacks as a practitioner who is actively fighting them, not just theorizing about themWhy first party fraud and refund abuse in ecommerce may be the number one threat for merchants right now, not AIHow fraud as a service has made it possible to commit refund fraud without any technical knowledge, and what that means for merchant fraud teamsWhy nearly fifty years of chargeback dispute rules regulation have not kept pace with the environment merchants are actually operating in The cross-merchant fraud intelligence sharing story that could have prevented six months of losses at another merchant's businessWhy end to end fraud monitoring is one of the most neglected basics in merchant fraud prevention strategiesDajana's 360 approach to fraud operations, which separates the fraud process into four parts and maps how they feed each otherWhy merchant fraud KPIs like chargeback rate alone tell an incomplete story and what to measure alongside themHow the lines between merchant fraud vs bank fraud are blurring at the identity and behavior layer You should listen to this episode if you:Work in e-commerce fraud detection and want a practitioner's view on what is actually changing versus what is being overhypedAre dealing with first party fraud, friendly fraud chargebacks, or refund abuse and want to hear how someone with 26 years of merchant fraud experience thinks about itHave felt frustrated that merchant fraud collaboration and intelligence sharing stops at your immediate networkAre building or restructuring your fraud team structure and want a framework that actually scalesLead a merchant fraud team and are trying to figure out how to get upstream of the payment rather than catching fraud at checkoutWant to understand how merchant cyber security collaboration is evolving and what convergence actually looks like in practice on the merchant sideAre newer to the space and want a fraud fighter career development perspective from someone who grew up inside the industry

    What’s New in Merchant Fraud, with Dajana G.
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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.

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