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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

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

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

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.

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.

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

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

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

Most fraud teams that have started adopting AI agents in fraud operations started in the right place. They piloted inside investigation. They enriched alerts, structured cases, and recommended resolutions for investigators to validate. And for the most part, they are seeing real efficiency gains. The problem is that almost nobody goes further. In part one of this series, I made the argument that the master KPI in fraud is not precision or accuracy. It is the reaction cycle. The time it takes your system to detect a gap, whether that is a new fraud attack or a misbehaving control, and ship a fix for it. Automating your investigation process is the first step in that journey. It is not the journey. Even if you automate investigations completely, the rest of your links in the chain are still running at human speed. The rules you deploy to flag events are still degrading. The labels feeding your models are still arriving weeks late. You are running faster investigations inside a broken loop. This episode is about closing that loop. All five steps of it. Before we get into the notes, if you landed here first, I'd recommend going back and listening to part one. We covered quite a lot that will make this one easier to follow. Link is below. What you’ll hear in this episode:Why automation of fraud investigation is only the first step in adopting AI agents in fraud, not the destinationHow the fraud reaction cycle breaks down into five distinct steps, each producing the input the next one needsWhy fraud alert clustering is the step that turns a pile of unrelated alerts into a curated set of assembled ring-level casesWhy automated fraud labeling is the single most important bottleneck in the entire reaction cycle and how to close itHow continuous fraud labeling at scale changes what your models and rules can doWhy fraud risk segmentation is the most underestimated layer in fraud strategy and why it has to come before detection automationHow the fraud rule recommendation engine in step five only works if the four steps before it are already in placeWhy fraud ops AI transformation is not a one-quarter project and what teams further along actually look likeThe organizational and governance capabilities your team needs to build at each stage before the next stage makes senseWhy the goal is not just lower cost but a fundamentally different and better fraud organization You should listen to this episode if you:Are working through the question of where to start when adopting AI agents in fraud and want a concrete, sequenced answerHave already deployed agents inside investigation and are trying to figure out what comes nextManage fraud analytics, rule writing, or model governance and want to understand where agentic AI fits into your work specificallyAre responsible for fraud ops reaction time and want to understand how to measure and improve it at scaleHave felt the pain of delayed chargebacks slowing down model retraining and want to understand how automated fraud labeling solves itAre building the business case for agentic AI in fraud and need a framework that goes beyond efficiency gains in investigationsLead a fraud team that is feeling pressure to adopt AI quickly and want clarity on how to do it without creating the governance problems that cause these projects to fail

Okay, this episode made me rethink something I've been hearing more and more over the past year: "Our fraud stack is AI-powered, so we're ready for AI fraud." Honestly, that's a comforting story. It just isn't necessarily true. Because the problem isn't simply that fraudsters have AI now. It's that AI-generated fraud is becoming increasingly difficult to distinguish from legitimate customer behavior. The old tells are disappearing, and many of the assumptions we've relied on for years are starting to break down. In this episode, I sit down with fraud strategy advisor David Liu to discuss what undetectable AI-powered fraud actually looks like, why traditional fraud detection systems are struggling to keep pace, and how fraud leaders should rethink their fraud prevention strategy before today's attacks become tomorrow's baseline. This conversation explores everything from deepfake fraud and synthetic identity fraud to autonomous fraud attacks, AI-powered cyber fraud, and the growing role of AI fraud detection in modern fraud operations. More importantly, we discuss how organizations can build fraud prevention systems that continue learning as attackers evolve. What you'll hear in this episode:Why undetectable AI-powered fraud represents a fundamental shift in fraud risk managementHow AI-generated fraud is changing the effectiveness of traditional fraud detection modelsWhy deepfake fraud and synthetic identity fraud continue to become more convincingHow AI fraud detection can improve real-time fraud detection without relying solely on static rulesWhy fraud operations automation should focus on accelerating learning rather than replacing analysts You should listen to this episode if you:Lead fraud operations or fraud strategy teamsAre evaluating AI fraud detection solutionsWant to modernize your fraud prevention systemNeed a stronger fraud prevention strategy for AI-enabled attacksWant to understand how autonomous fraud attacks are changing the fraud landscape

Every fraud leader I've spoken to this year has heard the same message: cut costs. You've probably already had the conversation. You've explained why fraud isn't just another operating expense, why reducing investigators today often means higher fraud losses tomorrow, and why your team is already stretched thin. And yet the budget cuts are still coming. Here's the question I'd rather ask. What if finance isn't actually your biggest problem? In this episode, I introduce the idea of agentic fraud ops and explain why the real issue isn't shrinking budgets. It's that most fraud organizations are still operating at human learning speed while their adversaries have already moved to machine speed. We talk about why fraud detection systems decay over time, why fraud rules and machine learning fraud detection models become less effective the moment they're deployed, and why the future of fraud prevention AI isn't about replacing analysts. It's about building systems that continuously learn. Honestly, optimizing a slow system is still optimizing a slow system. This episode explores what happens when AI-powered fraud detection becomes part of the reaction cycle itself instead of just another automation project. If we're going to redesign fraud operations, we first need to rethink what performance actually means. What you'll hear in this episode:Why agentic fraud ops changes how fraud teams should measure successWhy every fraud detection system begins decaying the day it goes liveHow fraud detection rules, machine learning models, and manual review age differentlyWhy reaction speed is becoming the most important fraud metricHow AI agents for fraud detection can compress learning cycles from weeks to hoursWhy fraud operations automation should focus on new capabilities instead of replacing peopleHow fraudsters are already using AI-driven fraud prevention techniques against defenders You should listen to this episode if you:Lead a fraud operations or fraud strategy teamWant to modernize your fraud prevention systemAre evaluating AI agents for fraud detectionNeed to reduce costs without increasing fraud lossesAre planning the future of your fraud operations transformation

So this episode starts with a data executive walking into a fraud team. Which sounds like the setup to a very niche joke. And maybe it is. But honestly, it also describes a real problem most fraud teams know too well: we rely on data for almost everything, but the relationship between fraud teams and data teams is often messier than anyone wants to admit. In this episode, I’m joined by Shachar Meir, a data advisor and former director of data at Meta, to talk about LLM analytics, self-service analytics, data infrastructure, and why fraud teams cannot just throw AI on top of messy data and hope it becomes strategy. Because yes, LLM-powered self-service analytics sounds amazing. Ask a question in plain English, get an answer, move faster, avoid waiting three weeks for a data team with 900 priorities. Great. I want that world too. But then you’ve got to ask yourself: where did the answer come from? Which tables did it join? What definition did it use? Did it understand the business context? Did it hallucinate? Did you ask the right question in the first place? Not a small detail. This conversation is really about the gap between the promise of AI analytics tools and the operational reality of fraud analytics. Fraud and risk teams work in adversarial environments. The problem keeps changing. The signal-to-noise ratio is bad. The cost of getting it wrong can be massive, whether that means letting fraud through or blocking legitimate users. So if you want LLM analytics to actually help, you need more than a shiny interface. You need data foundations, governance, semantic clarity, and the humility to start small. What you’ll hear in this episode:Why fraud and risk teams are unusually complex data customersHow fraud teams can work better with data teams instead of requesting endless point solutionsWhy self-service analytics failed so often before LLMs entered the pictureWhat changes, and what does not change, when LLM analytics becomes the interfaceWhy asking the right data question matters more than getting a fast answerHow data governance, semantic layers, and data quality shape AI analytics resultsWhy fraud teams should start AI adoption with one curated table, one use case, and one measurable outcomeHow to think about the one-hour, one-day, one-week, and one-month versions of a data project You should listen to this episode if you:Work in fraud operations and depend on data teams to ship risk or fraud analytics projectsAre considering LLM analytics or AI agents for data analysis inside your fraud stackHave been promised AI data querying that sounds too easy, because it probably isNeed better ways to align with data engineering, analytics, or risk data infrastructure teamsWant a practical way to think about AI data governance before deploying tools that affect real users

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

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.

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