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Statistical Techniques for Forensic Accounting: Understanding the Theory and Application of Data Analysis

Book Description

Master powerful statistical techniques for uncovering fraud or misrepresentation in complex financial data. The discipline of statistics has developed sophisticated, well-accepted approaches for identifying financial fraud and demonstrating that it is deliberate. Statistical Techniques for Forensic Accounting is the first comprehensive guide to these tools and techniques. Leading expert Dr. Saurav Dutta explains their mathematical underpinnings, shows how to use them properly, and guides you in communicating your findings to other interested and knowledgeable parties, or assessing others' analyses. Dutta is singularly well-qualified to write this book: he has been engaged as an expert in many of the world's highest-profile financial fraud cases, including Worldcom, Global Crossing, Cendant, and HealthSouth. Here, he covers everything professionals need to know to construct and conduct valid and defensible statistical tests, perform analyses, and interpret others' analyses. Coverage includes: exploratory data analysis to identify the "Fraud Triangle" and other red flags… data mining tools, usage, and limitations… statistical terms and methods applicable to forensic accounting… relevant uncertainty and probability concepts… Bayesian analysis and networks… statistical inference, sampling, sample size, estimation, regression, correlation, classification, prediction, and much more. For all forensic accountants, auditors, investigators, and litigators involved with corporate financial reporting; and for all students interested in forensic accounting and related fields.

Table of Contents

  1. About This eBook
  2. Title Page
  3. Copyright Page
  4. Praise for Statistical Techniques for Forensic Accounting
  5. Dedication Page
  6. Table of Contents
  7. Foreword
  8. Acknowledgments
  9. About the Author
  10. Preface
  11. 1. Introduction: The Challenges in Forensic Accounting
    1. 1.1. Introduction
    2. 1.2. Characteristics and Types of Fraud
    3. 1.3. Management Fraud Schemes
    4. 1.4. Employee Fraud Schemes
    5. 1.5. Cyber-crime
    6. 1.6. Chapter Summary
    7. 1.7. Endnotes
  12. 2. Legislation, Regulation, and Guidance Impacting Forensic Accounting
    1. 2.1. Introduction
    2. 2.2. U.S. Legislative Response to Fraudulent Financial Reporting
    3. 2.3. The Emphasis on Prosecution of Fraud at the Department of Justice
    4. 2.4. The Role of the FBI in Detecting Corporate Fraud
    5. 2.5. Professional Guidance in SAS 99
    6. 2.6. Chapter Summary
    7. 2.7. Endnotes
  13. 3. Preventive Measures: Corporate Governance and Internal Controls
    1. 3.1. Introduction
    2. 3.2. Corporate Governance Issues in Developed Economies
    3. 3.3. Emerging Economies and Their Unique Corporate Governance Issues
    4. 3.4. Organizational Controls
    5. 3.5. A System of Internal Controls
    6. 3.6. The COSO Framework on Internal Controls
    7. 3.7. Benefits, Costs, and Limitations of Internal Controls
    8. 3.8. Incorporation of Fraud Risk in the Design of Internal Controls
    9. 3.9. Legislation on Internal Controls
    10. 3.10. Chapter Summary
    11. 3.11. Endnotes
  14. 4. Detection of Fraud: Shared Responsibility
    1. 4.1. Introduction
    2. 4.2. Expectations Gap in the Accounting Profession
    3. 4.3. Responsibility of the External Auditor
    4. 4.4. Responsibility of the Board of Directors
    5. 4.5. Role of the Audit Committee
    6. 4.6. Management’s Role and Responsibilities in the Financial Reporting Process
    7. 4.7. The Role of the Internal Auditor
    8. 4.8. Who Blows the Whistle
    9. 4.9. Chapter Summary
    10. 4.10. Endnotes
  15. 5. Data Mining
    1. 5.1. Introduction
    2. 5.2. Data Classification
    3. 5.3. Association Analysis
    4. 5.4. Cluster Analysis
    5. 5.5. Outlier Analysis
    6. 5.6. Data Mining to Detect Money Laundering
    7. 5.7. Chapter Summary
    8. 5.8. Endnotes
  16. 6. Transitioning to Evidence
    1. 6.1. Introduction
    2. 6.2. Probability Concepts and Terminology
    3. 6.3. Schematic Representation of Evidence
    4. 6.4. Information and Evidence
    5. 6.5. Mathematical Definitions of Prior, Conditional, and Posterior Probability
    6. 6.6. The Probative Value of Evidence
    7. 6.7. Bayes’ Rule
    8. 6.8. Chapter Summary
    9. 6.9. Endnote
  17. 7. Discrete Probability Distributions
    1. 7.1. Introduction
    2. 7.2. Generic Definitions and Notations
    3. 7.3. The Binomial Distribution
    4. 7.4. Poisson Probability Distribution
    5. 7.5. Hypergeometric Distribution
    6. 7.6. Chapter Summary
    7. 7.7. Endnotes
  18. 8. Continuous Probability Distributions
    1. 8.1. Introduction
    2. 8.2. Conceptual Development of Probability Framework
    3. 8.3. Uniform Probability Distribution
    4. 8.4. Normal Probability Distribution
    5. 8.5. Testing for Normality
    6. 8.6. Chebycheff’s Inequality
    7. 8.7. Binomial Distribution Expressed as a Normal Distribution
    8. 8.8. The Exponential Distribution
    9. 8.9. Joint Distribution of Continuous Random Variables
    10. 8.10. Chapter Summary
  19. 9. Sampling Theory and Techniques
    1. 9.1. Introduction
    2. 9.2. Motivation for Sampling
    3. 9.3. Theory Behind Sampling
    4. 9.4. Statistical Sampling Techniques
    5. 9.5. Nonstatistical Sampling Techniques
    6. 9.6. Sampling Approaches in Auditing
    7. 9.7. Chapter Summary
    8. 9.8. Endnotes
  20. 10. Statistical Inference from Sample Information
    1. 10.1. Introduction
    2. 10.2. The Ability to Generalize Sample Data to Population Parameters
    3. 10.3. Central Limit Theorem and non-Normal Distributions
    4. 10.4. Estimation of Population Parameter
    5. 10.5. Confidence Intervals
    6. 10.6. Confidence Interval for a Large Sample When Population Standard Deviation Is Known
    7. 10.7. Confidence Interval for a Large Sample When Population Standard Deviation Is Unknown
    8. 10.8. Confidence Intervals for Small Samples
    9. 10.9. Confidence Intervals for Proportions
    10. 10.10. Chapter Summary
    11. 10.11. Endnote
  21. 11. Determining Sample Size
    1. 11.1. Introduction
    2. 11.2. Computing Sample Size When Population Deviation Is Known
    3. 11.3. Sample Size Estimation when Population Deviation Is Unknown
    4. 11.4. Sample Size Estimation for Proportions
    5. 11.5. Chapter Summary
  22. 12. Regression and Correlation
    1. 12.1. Introduction
    2. 12.2. Probabilistic Linear Models
    3. 12.3. Correlation
    4. 12.4. Least Squares Regression
    5. 12.5. Coefficient of Determination
    6. 12.6. Test of Significance and p-Values
    7. 12.7. Prediction Using Regression
    8. 12.8. Caveats and Limitations of Regression Models
    9. 12.9. Other Regression Models
    10. 12.10. Chapter Summary
  23. Index
  24. FT Press