Practical Fraud Prevention: Fraud and AML Analytics for Fintech and eCommerce, Using SQL and Python

Description

Over the past two decades, the booming ecommerce and fintech industries have become a breeding ground for fraud. Organizations that conduct business online are constantly engaged in a cat-and-mouse game with these invaders. In this practical book, Gilit Saporta and Shoshana Maraney draw on their fraud-fighting experience to provide best practices, methodologies, and tools to help you detect and prevent fraud and other malicious activities. Data scientists, data analysts, and fraud analysts will learn how to identify and quickly respond to attacks. You'll get a comprehensive view of typical incursions as well as recommended detection methods. Online fraud is constantly evolving. This book helps experienced researchers safely guide and protect their organizations in this ever-changing fraud landscape. With this book, you will: Examine current fraud attacks and learn how to mitigate them Find the right balance between preventing fraud and providing a smooth customer experience Share insights across multiple business areas, including ecommerce, banking, cryptocurrency, anti-money laundering, and ad tech Evaluate potential risks for a new vertical, market, or product Train and mentor teams by boosting collaboration and kickstarting brainstorming sessions Get a framework of fraud methods, fraud-fighting analytics, and data science methodologies Cover Copyright Table of Contents Foreword Preface Introduction to Practical Fraud Prevention How to Read This Book Who Should Read This Book? Conventions Used in This Book O’Reilly Online Learning How to Contact Us Acknowledgments Part I. Introduction to Fraud Analytics Chapter 1. Fraudster Traits Impersonation Techniques Deception Techniques Social Engineering The Dark Web Fraud Rings/Linking Volatility Card and Account Testing Abuse Versus Fraud Money Laundering and Compliance Violations Summary Chapter 2. Fraudster Archetypes Amateur Fraudster Cookie-Cutter Fraudster Gig Economy Fraudster Psychological Fraudster Product-Savvy Fraudster Tech-Savvy Fraudster Bot Generator Hacker Organized Crime Fraudster Distinction Between Organized Crime and Cookie-Cutter Fraudsters Small But Organized Crime Friendly Fraudster Pop Quiz Summary Chapter 3. Fraud Analysis Fundamentals Thinking Like a Fraudster A Professional Approach to Fraud Treat Categories with Caution Account Versus Transaction The Delicate Balance Between Blocking Fraud and Avoiding Friction Profit Margins Maintaining Dynamic Tension The Psychological Cost Tiers of Trust Anomaly Detection Practical Anomaly Detection: Density Case Study Crises: Planning and Response Economic Stress Affects Consumers’ Situations—and Decisions Prepare for Shifts in User Behaviors Interdepartmental Communication and Collaboration Friendly Fraud Summary Chapter 4. Fraud Prevention Evaluation and Investment Types of Fraud Prevention Solutions Rules Engines Machine Learning Hybrid Systems Data Enrichment Tools Consortium Model Building a Research Analytics Team Collaborating with Customer Support Measuring Loss and Impact Justifying the Cost of Fraud Prevention Investment Interdepartmental Relations Data Analysis Strategy Fraud Tech Strategy Data Privacy Considerations Identifying and Combating New Threats Without Undue Friction Keeping Up with New Fraud-Fighting Tools Summary Chapter 5. Machine Learning and Fraud Modeling Advantages of Machine Learning The Challenges of Machine Learning in Fraud Prevention Relative Paucity of Data Delayed Feedback and Overfitting The Labeled Data Difficulty Intelligent Adversary Explainability, Ethics, and Bias Dynamic Policies and the Merits of Story-Based Models Data Scientists and Domain Experts: Best Practices for a Fruitful Collaboration Working Well Together Popular Machine Learning Approaches Accuracy Versus Explainability and Predictability Classification Versus Clustering Summary Part II. Ecommerce Fraud Analytics Chapter 6. Stolen Credit Card Fraud Defining Stolen Credit Card Fraud Modus Operandi Identification Mismatched IP Repeat Offender IP Nonunique IPs Masked IP Warning: The Reliability of IP Analysis May Vary Depending on Locale Mitigation Example 1: Using IP Geolocation to Identify Legitimate Hotel IPs Example 2: Using IP Traffic Trends to Identify Fake-Hotel IPs Example 3: Using Hierarchy in Variable Design Using Hierarchy in IP Typology Variable Design Summary Chapter 7. Address Manipulation and Mules So Many Different Ways to Steal Physical Interception of Package: Porch Piracy Physical Interception of Package: Convince the Courier Send Package to a Convenient Location: Open House for Fraud Send Package to a Convenient Location: Reshippers Remote Interception of Package: Convince Customer Support Remote Interception of Package: AVS Manipulation Mule Interception of Package More Advanced: Adding an Address to the Card More Advanced: Adding an Address to Data Enrichment Services More Advanced: Dropshipping Direct/Triangulation Identification and Mitigation Open House Mules Reshippers Summary Chapter 8. BORIS and BOPIS Fraud Identification and Mitigation Pickup and Return: Educating Employees Outside Your Department Policy Decisions: Part of Fraud Prevention Online Identification and Mitigation Summary Chapter 9. Digital Goods and Cryptocurrency Fraud Definition and Fraudster MO Ticketing Fraud Gift Card Fraud Social Engineering Identification and Mitigation Summary Chapter 10. First-Party Fraud (aka Friendly Fraud) and Refund Fraud Types of Friendly Fraud Genuine Mistake Family Fraud Buyer’s Remorse, Customer Resentment, and Mens Rea Fraud Versus Abuse The Tendency to Tolerate Abuse Reseller Abuse Refund Fraud Identification and Mitigation Identification Mitigation Summary Part III. Consumer Banking Fraud Analytics Chapter 11. Banking Fraud Prevention: Wider Context Differences Between Banking and Ecommerce The Context of Cybercrime Social Engineering in Banking A Note on Perspective Deepfakes: A Word of Warning Summary Chapter 12. Online Account Opening Fraud False Accounts: Context Identification and Mitigation Asking Questions, Mapping the Story Document Verification Customer Personas Data Retention Summary Chapter 13. Account Takeover ATO: Fueled by Stolen Data The Attack Stages of ATO The Advantages of ATO Overlay Attacks Identification and Mitigation Biometrics Multifactor Authentication Device Fingerprinting Network Context Customer Knowledge Dynamic Friction Example: Identifying a Trusted Session Summary Chapter 14. Common Malware Attacks Types of Malware Attacks As Part of Phishing Attacks Malware with Social Engineering Identification and Mitigation Collaboration Is Key Anomaly Detection Summary Chapter 15. Identity Theft and Synthetic Identities How Identity Fraud Works Identification and Mitigation Linking Collaboration Summary Chapter 16. Credit and Lending Fraud Nonprofessional Fraudsters Engaging in Credit and Lending Fraud Professional Fraudsters and Credit and Lending Fraud Buy Now Pay Later Fraud Identification and Mitigation Summary Part IV. Marketplace Fraud Chapter 17. Marketplace Attacks: Collusion and Exit Types of Collusion Attacks Money Laundering Feedback Padding and Scams Incentives and Refund Abuse Selling Illegal Goods The Gig Economy of Fraud Identification and Mitigation Why Proximity Is Different in Marketplaces Thinking Beyond Immediate Fraud Prevention Summary Chapter 18. Marketplace Attacks: Seller Fraud Types of Seller Fraud Seller Slipup Segues into Fraud Scams Dubious Goods Identification and Mitigation Seller Slipup Segues into Fraud Scams Dubious Goods Summary Part V. AML and Compliance Analytics Chapter 19. Anti–Money Laundering and Compliance: Wider Context AML Challenges and Advantages Summary Chapter 20. Shell Payments: Criminal and Terrorist Screening How Shell Payments Work Identification and Mitigation Criminal and Terrorist Screening Summary Chapter 21. Prohibited Items Identification and Mitigation Summary Chapter 22. Cryptocurrency Money Laundering Cryptocurrency: More Regulated Than You Think, and Likely to Become More So The Challenge of Cryptocurrency Money Laundering Identification and Mitigation KYC: Combating Money Laundering from the Start Beyond KYC Summary Chapter 23. Adtech Fraud The Ultimate Money Maker Beyond Bot Detection: Looking into Invisible Ads Bot Identification in Adtech and Beyond Summary Chapter 24. Fraud, Fraud Prevention, and the Future Collaboration in the Era of “The New Normal” Index About the Authors Colophon