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Fraud and Fraud Detection: A Data Analytics Approach, + Website

Book Description

Detect fraud faster—no matter how well hidden—with IDEA automation

Fraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare's IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book.

Business systems' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to:

  • Understand the different areas of fraud and their specific detection methods

  • Identify anomalies and risk areas using computerized techniques

  • Develop a step-by-step plan for detecting fraud through data analytics

  • Utilize IDEA software to automate detection and identification procedures

  • The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification.

    Table of Contents

    1. Foreword
    2. Preface
      1. HOW THIS BOOK IS ORGANIZED
    3. Acknowledgments
    4. CHAPTER 1 Introduction
      1. DEFINING FRAUD
      2. ANOMALIES VERSUS FRAUD
      3. TYPES OF FRAUD
      4. ASSESS THE RISK OF FRAUD
      5. CONCLUSION
      6. NOTES
    5. CHAPTER 2 Fraud Detection
      1. RECOGNIZING FRAUD
      2. DATA MINING VERSUS DATA ANALYSIS AND ANALYTICS
      3. DATA ANALYTICAL SOFTWARE
      4. ANOMALIES VERSUS FRAUD WITHIN DATA
      5. FRAUDULENT DATA INCLUSIONS AND DELETIONS
      6. CONCLUSION
      7. NOTES
    6. CHAPTER 3 The Data Analysis Cycle
      1. EVALUATION AND ANALYSIS
      2. OBTAINING DATA FILES
      3. PERFORMING THE AUDIT
      4. FILE FORMAT TYPES
      5. PREPARATION FOR DATA ANALYSIS
      6. ARRANGING AND ORGANIZING DATA
      7. CONCLUSION
      8. NOTES
    7. CHAPTER 4 Statistics and Sampling
      1. DESCRIPTIVE STATISTICS
      2. INFERENTIAL STATISTICS
      3. MEASURES OF CENTER
      4. MEASURE OF DISPERSION
      5. MEASURE OF VARIABILITY
      6. SAMPLING
      7. CONCLUSION
      8. NOTES
    8. CHAPTER 5 Data Analytical Tests
      1. BENFORD’S LAW
      2. NUMBER DUPLICATION TEST
      3. Z-SCORE
      4. RELATIVE SIZE FACTOR TEST
      5. SAME-SAME-SAME TEST
      6. SAME-SAME-DIFFERENT TEST
      7. EVEN AMOUNTS
      8. CONCLUSION
      9. NOTES
    9. CHAPTER 6 Advanced Data Analytical Tests
      1. CORRELATION
      2. TREND ANALYSIS
      3. GEL-1 AND GEL-2
      4. CONCLUSION
      5. NOTES
    10. CHAPTER 7 Skimming and Cash Larceny
      1. SKIMMING
      2. CASH LARCENY
      3. CASE STUDY
      4. CONCLUSION
    11. CHAPTER 8 Billing Schemes
      1. DATA AND DATA FAMILIARIZATION
      2. BENFORD’S LAW TESTS
      3. RELATIVE SIZE FACTOR TEST
      4. Z-SCORE
      5. EVEN DOLLAR AMOUNTS
      6. SAME-SAME-SAME TEST
      7. SAME-SAME-DIFFERENT TEST
      8. PAYMENTS WITHOUT PURCHASE ORDERS TEST
      9. LENGTH OF TIME BETWEEN INVOICE AND PAYMENT DATES TEST
      10. SEARCH FOR POST OFFICE BOX
      11. MATCH EMPLOYEE ADDRESS TO SUPPLIER
      12. DUPLICATE ADDRESSES IN VENDOR MASTER
      13. PAYMENTS TO VENDORS NOT IN MASTER
      14. GAP DETECTION OF CHECK NUMBER SEQUENCES
      15. CONCLUSION
      16. NOTES
    12. CHAPTER 9 Check-Tampering Schemes
      1. ELECTRONIC PAYMENTS FRAUD PREVENTION
      2. CHECK TAMPERING
      3. DATA ANALYTICAL TESTS
      4. CONCLUSION
    13. CHAPTER 10 Payroll Fraud
      1. DATA AND DATA FAMILIARIZATION
      2. DATA ANALYSIS
      3. THE PAYROLL REGISTER
      4. PAYROLL MASTER AND COMMISSION TESTS
      5. CONCLUSION
      6. NOTES
    14. CHAPTER 11 Expense Reimbursement Schemes
      1. DATA AND DATA ANALYSIS
      2. CONCLUSION AND AUDIT TRAIL
      3. NOTES
    15. CHAPTER 12 Register Disbursement Schemes
      1. FALSE REFUNDS AND ADJUSTMENTS
      2. FALSE VOIDS
      3. CONCEALMENT
      4. DATA ANALYTICAL TESTS
      5. CONCLUSION
    16. CHAPTER 13 Noncash Misappropriations
      1. TYPES OF NONCASH MISAPPROPRIATIONS
      2. CONCEALMENT OF NONCASH MISAPPROPRIATIONS
      3. DATA ANALYTICS
      4. CONCLUSION
    17. CHAPTER 14 Corruption
      1. BRIBERY
      2. TENDER SCHEMES
      3. KICKBACKS, ILLEGAL GRATUITIES, AND EXTORTION
      4. CONFLICT OF INTEREST
      5. DATA ANALYTICAL TESTS
      6. CONCEALMENT
      7. CONCLUSION
    18. CHAPTER 15 Money Laundering
      1. THE MONEY-LAUNDERING PROCESS
      2. OTHER MONEY TRANSFER SYSTEMS AND NEW OPPORTUNITIES
      3. AUDIT AREAS AND DATA FILES
      4. CONCLUSION
    19. CHAPTER 16 Zapper Fraud
      1. POINT-OF-SALES SYSTEM CASE STUDY
      2. QUANTIFYING THE ZAPPED RECORDS
      3. ADDITIONAL POS DATA FILES TO ANALYZE
      4. MISSING AND MODIFIED BILLS
      5. THE MARKUP RATIOS
      6. CONCLUSIONS AND SOLUTIONS
      7. NOTES
    20. CHAPTER 17 Automation and IDEAScript
      1. CONSIDERATIONS FOR AUTOMATION
      2. CREATING IDEASCRIPTS
      3. CONCLUSION
    21. CHAPTER 18 Conclusion
      1. FINANCIAL STATEMENT FRAUD
      2. IDEA FEATURES DEMONSTRATED
      3. PROJECTS OVERVIEW
      4. DATA ANALYTICS: FINAL WORDS
      5. NOTES
    22. About the Author
    23. About the Website
    24. Index
    25. End User License Agreement