What the F Happened? Fraud and Financial Crime, Deconstructed

NASA Federal Credit Union wins the Celent Model Risk Manager of the Year Award

NASA Federal Credit Union set out to solve a growing challenge in financial crime prevention: fraud and AML teams operating in separate systems. By partnering with DataVisor to build a unified FRAML platform, the credit union gained real-time visibility across fraud, money laundering, and account activity. In this episode, you'll learn: * Why fraud and AML silos create dangerous blind spots * How AI, machine learning, and real-time intelligence improve detection * The impact of a unified approach, including a 42% reduction in false positives and significant operational savings Listen to discover how NASA FCU earned Celent's Model Risk Manager of the Year award and what it means for the future of financial crime prevention. Read the Case Study here: https://www.datavisor.com/intelligence-center/case-studies/nasa-federal-credit-union-modernizes-fraud-aml-with-a-unified-framl-platform [https://www.datavisor.com/intelligence-center/case-studies/nasa-federal-credit-union-modernizes-fraud-aml-with-a-unified-framl-platform] Chapters: 00:00 Why NASA FCU Won Celent's Top Award 02:30 The Fraud & AML Silo Problem 05:22 Why Legacy AML Systems Fail 08:00 What Is FRAML? 10:52 Building a Real-Time Risk Engine 13:41 How AI Detects Unknown Threats 16:31 AI Agents Cut Manual Work 19:20 42% Fewer False Positives 21:52 The Future of AI-Powered Compliance

Webinar Highlights - AI Tools Fraudsters Use

In this Webinar Highlights episode, we recap key takeaways from our recent session on the AI tools fraudsters are using in 2026 and how financial institutions can respond to the growing threat of AI-powered fraud. From autonomous voice scams and deepfake identities to search poisoning and synthetic KYC attacks, this discussion explores how generative AI is transforming the fraud landscape. You’ll hear highlights on: * How fraudsters are using AI to automate social engineering, voice scams, and identity fraud at scale * The rise of AI-powered tools that can bypass traditional KYC and liveness detection controls * Why behavioral intelligence and AI-driven fraud detection are becoming essential for financial institutions * The concept of "fraud-as-a-service" and how the barrier to entry for cybercrime has dramatically decreased * How organizations can combine AI and human expertise to detect fraud while reducing customer friction Watch the full webinar: https://www.datavisor.com/intelligence-center/webinars/defend-webinar-ai-tools-fraudsters-use-2026-edition [https://www.datavisor.com/intelligence-center/webinars/defend-webinar-ai-tools-fraudsters-use-2026-edition] Chapters: 00:00 AI Scammer Calls Your Bank 02:39 Why AI Fraud Is Exploding 05:52 AI Search Poisoning Explained 09:15 Voice Bots & Prompt Injection 12:31 Fake Receipts & Scam Websites 14:32 Beating KYC With AI 17:10 AI-Powered Romance Scams 18:45 How Banks Fight Back With AI

Case File - TaskRabbit: Uncovering Hidden Fraud Networks

In this Case File, we break down how a leading online service marketplace addressed sophisticated fraud threats, including account takeover (ATO), mass fake account registration, and collusion fraud.  Learn how TaskRabbit partnered with DataVisor to move from reactive fraud prevention to proactive network-based detection, protecting platform integrity, user trust, and millions of dollars in revenue. You’ll learn: * The fraud challenges TaskRabbit faced, from identity theft and fake account creation to organized collusion schemes * How machine learning, device intelligence, and Knowledge Graph technology helped uncover hidden fraud networks in real time * The measurable impact, including millions of dollars in fraud losses prevented, 25x faster fraud response times, and a 60x increase in fraud investigation efficiency Read the full case study: https://www.datavisor.com/intelligence-center/case-studies/taskrabbit-ecommerce-fraud-prevention [https://www.datavisor.com/intelligence-center/case-studies/taskrabbit-ecommerce-fraud-prevention] Chapters: 00:00 Why Marketplace Trust Matters 01:10 The Three Fraud Threats 02:32 How Account Takeovers Work 03:13 The Bot Account Explosion 04:00 The Fraud Scheme Nobody Sees 05:45 TaskRabbit Fights Back 06:33 Catching Fraud with Devices 07:13 Exposing Hidden Fraud Rings 09:50 Millions Saved, 60x Efficiency 11:06 The Future of Fraud Detection

Monthly Brief - June - SEC Deadlines, FedNow & Agentic AI

In this month's briefing, Emily and Jeff unpack two major June 2026 compliance deadlines, including SEC Regulation S-P and Nacha's ACH fraud monitoring requirements. They explore how FedNow and instant payments are forcing financial institutions to detect fraud in milliseconds rather than days, why mule accounts remain a critical threat, and how information sharing is becoming essential to stopping scams before funds disappear. The conversation also examines the growing role of agentic AI in fraud operations, the rise of AI-powered fraud tactics such as synthetic identities and deepfakes, and what Patrick Harr's appointment as CEO means for DataVisor's next phase of growth in the AI-native fraud prevention market. Links * 5 Ways Fraud Teams are Using Agentic AI Right Now [https://www.datavisor.com/intelligence-center/ebooks/ways-fraud-teams-are-using-agentic-ai-right-now] * 5 Ways AML Teams are Using Agentic AI Right Now [https://www.datavisor.com/intelligence-center/ebooks/ways-aml-teams-are-using-agentic-ai-right-now] * BusinessWire: Patrick Harr Appointed CEO, DataVisor [https://www.businesswire.com/news/home/20260527469062/en/DataVisor-Appoints-Patrick-Harr-as-CEO-to-Scale-AI-Native-Market-Leadership-in-Fraud-and-Financial-Crime-Prevention] * DEFEND Webinar | AI Tools Fraudsters Use, 2026 Edition [https://www.datavisor.com/intelligence-center/webinars/defend-webinar-ai-tools-fraudsters-use-2026-edition] * DEFEND Training [https://www.datavisor.com/defend-training] * Transcript [https://www.datavisor.com/defend-podcast] Chapters 00:00 Introduction: Why Real-Time Payments Change Everything 01:11 SEC Regulation S-P Deadline Explained 02:52 Nacha ACH Fraud Monitoring Phase 2 Requirements 04:08 Authorized Push Payment Scams and Behavioral Detection 05:58 From Legacy Payment Rails to FedNow 07:49 Mule Accounts and Financial Crime Networks 09:35 Collective Intelligence and Real-Time Risk Sharing 10:22 Agentic AI and the Future of Fraud Operations 12:47 Deepfakes, Synthetic Identities, and AI-Powered Fraud 14:18 Patrick Harr Named CEO of DataVisor 16:23 Key Takeaways for Fraud and Compliance Teams 18:16 Closing Thoughts and DEFEND Training

Case Files - BNPL Fraud: How Crime Rings Get Caught

Every time you click "Buy Now, Pay Later," an invisible war plays out in milliseconds, and the fraudsters are organized, well-funded, and sophisticated. In this episode of What the F Happened? Fraud and Financial Crime Deconstructed, hosts Jeff and Emily break down the real engineering battle behind BNPL fraud prevention. You'll learn how unsupervised machine learning (UML) and link analysis graphs are solving a problem that traditional supervised AI and rules engines simply couldn't, including detecting synthetic identity fraud, account takeovers, and abuse rings in milliseconds. In this episode: * How synthetic identities are built and why they fool legacy AI models * The link analysis technique that exposed a 10,000-person fraud ring sharing the same first name * A 5:1 "hurt ratio" that was killing one major fintech's business * How unsupervised machine learning, knowledge graph technology, and real-time behavioral analysis detects coordinated fraud rings in milliseconds * The measurable impact on operations and revenue, including tens of millions in prevented fraud losses, reduced false positives, and dramatically improved review efficiency Read the following case studies:  * https://www.datavisor.com/intelligence-center/case-studies/stopping-emerging-bnpl-fraud-rings-in-real-time [https://www.datavisor.com/intelligence-center/case-studies/stopping-emerging-bnpl-fraud-rings-in-real-time] * https://www.datavisor.com/intelligence-center/case-studies/5-case-studies-uml-real-time-fraud-prevention [https://www.datavisor.com/intelligence-center/case-studies/5-case-studies-uml-real-time-fraud-prevention] * https://www.datavisor.com/intelligence-center/case-studies/consumer-lender-raises-the-bar-for-customer-experience-and-reduces-fraud-with-machine-learning-wp [https://www.datavisor.com/intelligence-center/case-studies/consumer-lender-raises-the-bar-for-customer-experience-and-reduces-fraud-with-machine-learning-wp] Chapters: 00:03 Introduction & BNPL Friction  01:44 The Business of the Hurt Ratio 04:17 Inside Organized Fraud Rings 05:54 Why Supervised AI Fails  07:32 Limits of Traditional Clustering  09:24 Shifting to Unsupervised ML  10:02 Feature Generation & Data Tech  11:29 Graph Databases & Live Testing 17:52 Financial Results & Conclusion