Practical Fraud Prevention

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• Foreword
• Preface
  • Introduction to Practical Fraud Prevention
  • How to Read This Book
  • Who Should Read This Book?
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Introduction to Fraud Analytics
• 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
• 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
• 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 Situationsand Decisions
    • Prepare for Shifts in User Behaviors
    • Interdepartmental Communication and Collaboration
    • Friendly Fraud
    • Prioritizing New Projects
  • Summary
• 4. Fraud Prevention Evaluation and Investment
  • Types of Fraud Prevention Solutions
    • Rules Engines
    • Machine Learning
    • Hybrid Systems
    • Data Enrichment Tools
    • Consortium Model
      • Using consortium data
      • Providerless consortiums
  • 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
• 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
      • Be clear about who is responsible for which elements
      • Establish a goal
      • Establish your focus
      • Know what main features the model should be looking at
      • Determine your risk appetite
      • Determine what prior knowledge can be introduced into the model
      • Determine which model best suits the problem
      • Find out what the process is for fine tuning, retraining, and gap analysis
      • If you dont understand, ask
      • Be kind to one another
  • Popular Machine Learning Approaches
    • Accuracy Versus Explainability and Predictability
    • Classification Versus Clustering
      • Precision versus recall
      • Success criteria
  • Summary
• II. Ecommerce Fraud Analytics
• 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
      • Response to the latest technology
      • Resolution of supported locales
      • Default locales
  • 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
• 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
• 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
• 9. Digital Goods and Cryptocurrency Fraud
  • Definition and Fraudster MO
  • Ticketing Fraud
  • Gift Card Fraud
  • Social Engineering
  • Identification and Mitigation
  • Summary
• 10. First-Party Fraud (aka Friendly Fraud) and Refund Fraud
  • Types of Friendly Fraud
    • Genuine Mistake
    • Family Fraud
    • Buyers Remorse, Customer Resentment, and Mens Rea
    • Fraud Versus Abuse
    • The Tendency to Tolerate Abuse
    • Reseller Abuse
    • Refund Fraud
  • Identification and Mitigation
    • Identification
    • Mitigation
  • Summary
• III. Consumer Banking Fraud Analytics
• 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
• 12. Online Account Opening Fraud
  • False Accounts: Context
  • Identification and Mitigation
    • Asking Questions, Mapping the Story
    • Document Verification
    • Customer Personas
    • Data Retention
  • Summary
• 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
• 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
• 15. Identity Theft and Synthetic Identities
  • How Identity Fraud Works
  • Identification and Mitigation
    • Linking
    • Collaboration
      • Cross-sector fraud checks and cross-organizational data sharing: Cifas, SIRA, and credit bureaus
  • Summary
• 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
• IV. Marketplace Fraud
• 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
• 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
• V. AML and Compliance Analytics
• 19. AntiMoney Laundering and Compliance: Wider Context
  • AML Challenges and Advantages
  • Summary
• 20. Shell Payments: Criminal and Terrorist Screening
  • How Shell Payments Work
  • Identification and Mitigation
  • Criminal and Terrorist Screening
  • Summary
• 21. Prohibited Items
  • Identification and Mitigation
  • Summary
• 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
• 23. Adtech Fraud
  • The Ultimate Money Maker
  • Beyond Bot Detection: Looking into Invisible Ads
  • Bot Identification in Adtech and Beyond
  • Summary
• 24. Fraud, Fraud Prevention, and the Future
  • Collaboration in the Era of The New Normal
• Index

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