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Foundation models in fraud are on every conference agenda this year, and I keep hearing the same question. Is this the real thing or the next buzzword? When I landed at Money20/20 Vegas, I wanted to ask someone who builds with them every day. Pedro Sanzovo works on identity and fraud products, and has been in fraud his whole career. He started at Palantir, built a fraud company for merchants in Brazil, and sold it to Signifyd, where he led product work on machine learning models. Six months ago he joined Plaid. We kept this one short and practical. What is a foundation model, and how is it different from the models most of us run today? Where does it beat XGBoost, and where does it hit the same walls? And what do you do with an embedding when your team writes rules? Pedro had some good answers, and a few things I think you’ll want to hear. What you’ll hear in this episode:What a foundation model is, and how transformer models differ from the chained rules and XGBoost models fraud teams have used for yearsWhy legacy models depend on handcrafted features and clean supervised labels, and why that is hard for smaller companiesHow financial data foundation models treat transactions as a sequence of events and predict what comes nextWhat Pedro means by the precision uplift of up to 34% he is seeing, and why data quality still decides the outcomeWhy latency constraints, such as 300 milliseconds for card transactions, shape which model belongs whereHow foundation model explainability compares with XGBoost, and why credit and fraud face different expectationsWhy embeddings are hard to turn into rules, and how AI agents might help You should listen to this episode if you:Lead or work with a fraud data science team and are deciding whether to explore foundation modelsRun a smaller fraud team and struggle with labeling and feature engineeringWant a plain explanation of how foundation models differ from XGBoostNeed to weigh precision gains against latency and explainabilityAre curious how embeddings and AI agents could work together to produce fraud rules

There has always been an arms race in the vendor space, and this year it has been about AI. At the same time, the practitioners I talk with are building far more than they used to. A few weeks ago I spoke with a risk leader who described what her team built in house, and it was impressive. That made me wonder whether the ground is shifting beneath vendors, and whether we will still need them in a year or two. I invited Netanel Kabala to help me think it through. Netanel and I met at PayPal 17 years ago, when he started as a fraud analyst after the acquisition of PayPal’s Tel Aviv fraud sciences team. He later co-founded Simplex, the first crypto on-ramp provider, led AI and analytics at Nuvei after the acquisition, and recently co-founded Duly, which helps payment providers and fintechs own their decisioning stack. We look at building vs buying fraud vendors from several angles, including vendor fatigue, what teams build first, and what foundation models and decision models change. We don’t land on a yes or no answer, and I think that’s the honest result. Instead, we sketch where to build, where to buy, and where joint development makes sense. What you’ll hear in this episode:Why vendor fatigue is pushing teams to build, and what that means for the vendor relationshipWhy the most successful in-house builds so far are small tools that save one expert time and don’t replace a whole platformHow vendors are shifting from end-to-end solutions toward data providers, with network data and device intelligence valued most by larger teamsWhy manual review viewers are easy to build with AI coding, and why a rule engine is a very different kind of projectHow AI slop shows up in risk teams, from dozens of models built overnight to latency and drift monitoring problems nobody planned forWhat foundation models and decision models change about features, anomaly detection, and who can deploy a real-time fraud modelWhy segmentation is the best place to start, and why building generic capabilities like KYC or sanction screening is a trapHow to judge whether your organization is ready to build, including engineering support, access to risk talent, and leadership buy-inWhy the maintenance trap catches analysts who become product manager, DevOps engineer, and backend engineer at once You should listen to this episode if you:Lead a fraud or risk team and are deciding what to build and what to keep buyingFeel vendor fatigue and want a clearer way to think about which relationships still earn their placeHave built something in house with AI and want to know whether it can scaleNeed to explain to leadership what a build roadmap requires beyond a quick proof of conceptWant to know how foundation models and decision models may change the feature layer and vendor data

For a couple of years now, Jaanus Uudmäe’s LinkedIn posts have kept showing up in my feed, and they always carry a practical take on what fraud looks like at Bolt. Bolt is an on-demand marketplace headquartered in Estonia that operates in more than 50 countries, with ride hailing, food delivery, scooters, e-bikes, and car rentals. That means his team sees almost every kind of customer and almost every kind of abuse. Jaanus started as a software developer at Skype in 2007 and landed on the fraud team by accident. He joined Bolt in 2017 as a hands-on analyst when the fraud team had two people, and today he has 14 reporting to him. In this conversation, we spend most of our time in what I’d call the gray zone. Gray zone fraud is behavior where you can’t tell whether you’re looking at a fraudster, a frustrated customer, or an ordinary person who tried a trick a friend told them about. We talk about refund abuse and bonus abuse, why first time customers are the hardest to judge, and how to build labels when no one hands them to you. We also get into how to explain losses to the business when the numbers aren’t clean, and how Jaanus’s team uses AI in a way that surprised me. What you’ll hear in this episode:How Jaanus went from a software developer at Skype to leading a 14 person fraud team at BoltWhy marketplace fraud prevention gets harder in a three sided marketplace with riders, drivers, restaurants, and couriersWhat gray zone first party fraud looks like in practice, from refund abuse to bonus abuseWhy first time customer fraud is the hardest call, and how Bolt decides when to act and when to fall back on standard policyThe difference between opportunistic vs professional fraud, and why opportunistic customers make A/B testing unreliableHow fraud label manufacturing works when nobody hands you the answers, and how to avoid recursive fraud labelingWhy label maturity matters, and which proxy metrics keep a team honestHow Bolt thinks about fraud risk appetite and fraud expansion cost when entering a new marketHow AI works as a context layer for analysts, and why the fraud analyst mindset still matters You should listen to this episode if you:Deal with refund abuse or bonus abuse and can’t prove intentWork at a marketplace or on-demand platform with more than two sidesAre launching in a new market and need a way to talk about fraud losses with the businessCreate your own fraud labels and worry about fooling yourselfWant to see how a technical fraud team uses AI for analysis without removing human judgment

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