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 experience of fraud fighting to provide best practices, methodologies, and tools to help your organization 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 analytic methods. Online fraud is constantly evolving. This book helps experienced researchers safely guide and protect their organizations in the 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 and banking
  • Evaluate potential risks for a new vertical, market, or product
  • Train and mentor teams by initiating hackathons and kickstarting brainstorming sessions
  • Get a framework of fraud methods and fraud-fighting analytics

Table of Contents

  1. 1. Fraudster Archetypes
    1. Amateur Fraudsters
    2. Mechanical Turk Fraudsters
    3. Psychological Fraudster
    4. Product-savvy Fraudster
    5. Tech-Savvy Fraudster
    6. Bot Generator
    7. Hacker
    8. Organized Fraud Crime
    9. Distinction Between Organized Crime and Mechanical Turk Fraudsters
    10. Small But Organized Crime
    11. Friendly Fraudsters
    12. Pop Quiz
    13. Summary
  2. 2. Fraud Analysis Fundamentals
    1. Thinking Like A Fraudster
    2. A Professional Approach to Fraud
    3. Treat Categories with Caution
    4. Account Versus Transaction
    5. Between Blocking Fraud and Avoiding Friction: A Balance
    6. Anomaly Detection
    7. Practical Anomaly Detection - Density Case Study
    8. Crises: Planning and Response
    9. Economic Stress Affects Consumers’ Situations - and Decisions
    10. Prepare for Shifts in User Behaviors
    11. Inter-Departmental Communication and Collaboration
    12. Friendly Fraud
    13. Summary
  3. 3. Fraud Prevention Evaluation and Investment
    1. Types of Fraud Prevention Solutions
    2. Rules Engines
    3. Machine Learning
    4. Hybrid Systems
    5. Data Enrichment Tools
    6. Consortium Model
    7. Building A Res earch Analytics Team
    8. Collaborating With Customer Support
    9. Measuring Loss and Impact
    10. Justifying the Cost of Fraud Prevention Investment
    11. Inter-Departmental Relations
    12. Data Analysis Strategy
    13. Fraud Tech Strategy
    14. Data Privacy Considerations
    15. Identifying and Combating New Threats Without Undue Friction
    16. Keeping Up With New Fraud Fighting Tools
    17. Summary