--- title: What AdTech Taught Me About Financial Fraud, with Gilit Saporta source_page: https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/ad-tech-in-financial-fraud canonical: https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/ad-tech-in-financial-fraud format: text/markdown date: 2026-07-04T03:00:00.000Z description: Ad tech in financial fraud exposes new lessons for fraud teams using AI, bot detection, device hijacking insights, and stronger cross-industry response… --- **Quick links:** [Human page](https://www.sardine.ai/media/the-saturday-fraud-strategist/episodes/ad-tech-in-financial-fraud) · [Home](https://www.sardine.ai) · [Customers](https://www.sardine.ai/customers) · [Blog](https://www.sardine.ai/blog) · [Demo](https://www.sardine.ai/demo) --- # What AdTech Taught Me About Financial Fraud, with Gilit Saporta **Published:** 2026-07-04T03:00:00.000Z Ad tech in financial fraud exposes new lessons for fraud teams using AI, bot detection, device hijacking insights, and stronger cross-industry response… In this episode of The Saturday Fraud Strategist, I talk with Gilit Saporta about ad tech in financial fraud, which sounds very niche until you realize it touches malware, fake traffic, bot detection, consumer abuse, advertising fraud, and a lot of systems quietly pretending they have this handled. Ad tech fraud is not just “someone clicked a fake ad.” That would almost be simple. What we’re really talking about is a whole ecosystem where fraudsters monetize traffic, hijack devices, manipulate advertising spend, and sometimes pull regular people into the mess without them even knowing it happened. Gilit brings nearly two decades of fraud-fighting experience across financial services, crypto, e-commerce, and digital advertising, so the conversation gets practical pretty quickly. We look at what makes ad tech in financial fraud different from traditional fraud, where fraud detection is improving, and where systems still fall apart because the context is missing. And honestly, that’s the part that keeps coming up. You can have AI fraud prevention. You can have automated fraud detection. You can have dashboards that look very impressive in a board meeting. But if the system can’t tell the difference between a weird but legitimate pattern and an actual fraud signal, now you’ve got a problem. Maybe a very expensive problem. What you’ll hear in this episode: A step-by-step breakdown of how ad tech fraud works and why it does not behave like traditional financial fraud A practical look at what modern fraud detection is getting right and where it still struggles Why institutions, platforms, and technology teams need to stop treating fraud risk management as someone else’s cleanup job A conversation about AI-powered fraud fighting, automation, accountability, and the uncomfortable gap between speed and judgment How consumers get pulled into device hijacking, malware, scams, fake apps, and fraudulent advertising ecosystems Why better financial fraud prevention depends on context, not just bigger models or faster alerts A discussion about collaboration between fraud teams, trust and safety, researchers, regulators, and the organizations sitting on all the useful data Who should listen: Fraud professionals and financial institution leaders Risk, compliance, and cybersecurity teams AI, machine learning, and fraud analytics practitioners Trust and safety teams Ad tech, e-commerce, and digital platform leaders Regulators, policy advisors, and industry advocates Anyone trying to understand why fraud keeps finding the cracks between systems This episode is for people who care about fraud prevention strategies beyond the press release version. Detection matters. Compliance matters. But you’ve got to ask yourself, are we actually preventing harm, or are we just getting better at labeling it after the fact? ### Episode notes Ad tech in financial fraud has become a very convenient playground for fraudsters. You have money moving through complex advertising systems, traffic that can be faked or manipulated, devices that can be hijacked, and consumers who often have no idea they were part of the operation. That’s already messy. Now add AI, automation, bots, fake apps, malware, and advertising networks that were not exactly built for perfect transparency, and the whole thing starts to look less like a simple fraud problem and more like an ecosystem problem. Gilit and I talk through how organizations are using AI fraud detection, bot detection, advanced fraud analytics, and intelligence sharing to improve prevention. And to be fair, some of this is real progress. I am not here just to complain into the microphone. Mostly. But there are still major gaps. AI systems can move fast, but they can also jump to conclusions. They can detect patterns, but they do not always understand why those patterns matter. They can flag anomalies, but without strong fraud investigation workflows and human judgment, you may just end up with very confident confusion. We also get into the difference between malicious automation, legitimate automation, and wasteful automation. That distinction matters more than people think. If every bot is treated the same, or every strange behavior is treated as fraud, teams create noise. If they ignore the automation problem completely, fraudsters get a very nice invitation. Not a good look. The consumer impact is a major part of this conversation. Device hijacking, malware, fraudulent applications, scam-driven advertising, and deceptive ecosystems often hit people who never expected to become victims. And when they do, shame and embarrassment can keep them from reporting it. Behind the bot traffic, fake installs, automated clicks, and suspicious patterns, there is usually a person somewhere who got exploited, manipulated, or quietly harmed by a system that did not catch the problem early enough. We also talk about the upcoming Fraud Fighters AI Playbook and what responsible AI adoption should actually look like for fraud teams. Not the shiny version. The operational version. The version where teams have to think about governance, data quality, accountability, fraud prevention technology, investigation workflows, and whether their tools are helping people make better decisions or just giving them more alerts to triage. ### Key takeaways Awareness helps, but awareness is not prevention. Fraud detection needs technology, context, and human judgment working together. AI-powered fraud fighting is only useful when it is supported by strong investigative frameworks. Ad tech in financial fraud creates risk across advertising fraud, identity abuse, malware, consumer protection, and financial services. Organizations need better ways to separate malicious automation from legitimate or simply wasteful automation. Ad fraud detection cannot rely only on faster alerts. It needs better context, better data, and better operational judgment. Fraud teams, trust and safety teams, researchers, regulators, and platforms need to collaborate more directly. The human cost of fraud should not be treated as a side note. The bigger question is whether organizations are building systems that actually reduce harm, or just systems that explain the harm after it has already happened. The alternative is accepting fragmented systems, weak accountability, and fraud ecosystems that keep scaling faster than the defenses around them. That’s not a great plan. To go deeper, get your copy of The Fraud Fighter’s AI Playbook by Gilit Saporta, Chen Zamir, and Shoshana Maraney on O’Reilly: https://www.oreilly.com/library/view/the-fraud-fighters/9798341660359/ Not ready to stop the conversation about my, and hopefully your, favorite subject? Subscribe to The Saturday Fraud Strategist newsletter.