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Bank Fraud: Using Technology to Combat Losses

Book Description

Learn how advances in technology can help curb bank fraud

Fraud prevention specialists are grappling with ever-mounting quantities of data, but in today's volatile commercial environment, paying attention to that data is more important than ever. Bank Fraud provides a frank discussion of the attitudes, strategies, and—most importantly—the technology that specialists will need to combat fraud.

Fraudulent activity may have increased over the years, but so has the field of data science and the results that can be achieved by applying the right principles, a necessary tool today for financial institutions to protect themselves and their clientele. This resource helps professionals in the financial services industry make the most of data intelligence and uncovers the applicable methods to strengthening defenses against fraudulent behavior. This in-depth treatment of the topic begins with a brief history of fraud detection in banking and definitions of key terms, then discusses the benefits of technology, data sharing, and analysis, along with other in-depth information, including:

  • The challenges of fraud detection in a financial services environment

  • The use of statistics, including effective ways to measure losses per account and ROI by product/initiative

  • The Ten Commandments for tackling fraud and ways to build an effective model for fraud management

  • Bank Fraud offers a compelling narrative that ultimately urges security and fraud prevention professionals to make the most of the data they have so painstakingly gathered. Such professionals shouldn't let their most important intellectual asset—data—go to waste. This book shows you just how to leverage data and the most up-to-date tools, technologies, and methods to thwart fraud at every turn.

    Table of Contents

    1. Cover Page
    2. Title Page
    3. Copyright
    4. Dedication
    5. Contents
    6. Preface
    7. Acknowledgments
    8. About the Author
    9. CHAPTER 1: Bank Fraud: Then and Now
      1. THE EVOLUTION OF FRAUD
      2. THE EVOLUTION OF FRAUD ANALYSIS
      3. SUMMARY
    10. CHAPTER 2: Quantifying Fraud: Whose Loss Is It Anyway?
      1. FRAUD IN THE CREDIT CARD INDUSTRY
      2. THE ADVENT OF BEHAVIORAL MODELS
      3. FRAUD MANAGEMENT: AN EVOLVING CHALLENGE
      4. FRAUD DETECTION ACROSS DOMAINS
      5. USING FRAUD DETECTION EFFECTIVELY
      6. SUMMARY
    11. CHAPTER 3: In God We Trust. The Rest Bring Data!
      1. DATA ANALYSIS AND CAUSAL RELATIONSHIPS
      2. BEHAVIORAL MODELING IN FINANCIAL INSTITUTIONS
      3. SETTING UP A DATA ENVIRONMENT
      4. UNDERSTANDING TEXT DATA
      5. SUMMARY
    12. CHAPTER 4: Tackling Fraud: The Ten Commandments
      1. 1. DATA: GARBAGE IN; GARBAGE OUT
      2. 2. NO DOCUMENTATION? NO CHANGE!
      3. 3. KEY EMPLOYEES ARE NOT A SUBSTITUTE FOR GOOD DOCUMENTATION
      4. 4. RULES: MORE DOESN'T MEAN BETTER
      5. 5. SCORE: NEVER REST ON YOUR LAURELS
      6. 6. SCORE + RULES = WINNING STRATEGY
      7. 7. FRAUD: IT IS EVERYONE'S PROBLEM
      8. 8. CONTINUAL ASSESSMENT IS THE KEY
      9. 9. FRAUD CONTROL SYSTEMS: IF THEY REST, THEY RUST
      10. 10. CONTINUAL IMPROVEMENT: THE CYCLE NEVER ENDS
      11. SUMMARY
    13. CHAPTER 5: It Is Not Real Progress Until It Is Operational
      1. THE IMPORTANCE OF PRESENTING A SOLID PICTURE
      2. BUILDING AN EFFECTIVE MODEL
      3. SUMMARY
    14. CHAPTER 6: The Chain Is Only as Strong as Its Weakest Link
      1. DISTINCT STAGES OF A DATA-DRIVEN FRAUD MANAGEMENT SYSTEM
      2. THE ESSENTIALS OF BUILDING A GOOD FRAUD MODEL
      3. A GOOD FRAUD MANAGEMENT SYSTEM BEGINS WITH THE RIGHT ATTITUDE
      4. SUMMARY
    15. CHAPTER 7: Fraud Analytics: We Are Just Scratching the Surface
      1. A NOTE ABOUT THE DATA
      2. DATA
      3. REGRESSION 1
      4. LOGISTIC REGRESSION 1
      5. “MODELS SHOULD BE AS SIMPLE AS POSSIBLE, BUT NOT SIMPLER”
      6. SUMMARY
    16. CHAPTER 8: The Proof of the Pudding May Not Be in the Eating
      1. UNDERSTANDING PRODUCTION FRAUD MODEL PERFORMANCE
      2. THE SCIENCE OF QUALITY CONTROL
      3. FALSE POSITIVE RATIOS
      4. MEASUREMENT OF FRAUD DETECTION AGAINST ACCOUNT FALSE POSITIVE RATIO
      5. UNSUPERVISED AND SEMISUPERVISED MODELING METHODOLOGIES
      6. SUMMARY
    17. CHAPTER 9: The End: It Is Really the Beginning!
    18. Notes
    19. Index