Fraud or misrepresentation often creates patterns of error within complex financial data. The discipline of statistics has developed sophisticated techniques and well-accepted tools for uncovering these patterns and demonstrating that they are the result of deliberate malfeasance. Statistical Techniques for Forensic Accounting is the first comprehensive guide to these tools and techniques: understanding their mathematical underpinnings, using them properly, and effectively communicating findings to non-experts. Dr. Saurav Dutta, one of the field's leading experts, has been engaged as an expert in many of the world's highest-profile fraud cases, including Worldcom, Global Crossing, Cendant, and HealthSouth. Now, he covers everything forensic accountants, auditors, investigators, and litigators need to know to use these tools and interpret others' use of them. Coverage includes:  Exploratory data analysis: identifying the "Fraud Triangle" and other red flags Data mining: tools, usage, and limitations Traditional statistical terms and methods applicable to forensic accounting Uncertainty and probability theories and their forensic implications Bayesian analysis and networks Statistical inference, sampling, sample size, estimation, regression, correlation, classification, and prediction How to construct and conduct valid and defensible statistical tests How to articulate and effectively communicate findings to other interested and knowledgeable parties

Table of Contents:
Foreword     xiii Acknowledgments     xv Preface     xviii 1 Introduction: The Challenges in Forensic Accounting     1 1.1 Introduction     1 1.2 Characteristics and Types of Fraud     3 1.3 Management Fraud Schemes     7 1.4 Employee Fraud Schemes     11 1.5 Cyber-crime     17 1.6 Chapter Summary     18 1.7 Endnotes     19 2 Legislation, Regulation, and Guidance Impacting Forensic Accounting     21 2.1 Introduction     21 2.2 U.S. Legislative Response to Fraudulent Financial Reporting     22 2.3 The Emphasis on Prosecution of Fraud at the Department of Justice     24 2.4 The Role of the FBI in Detecting Corporate Fraud     26 2.5 Professional Guidance in SAS 99     27 2.6 Chapter Summary     28 2.7 Endnotes     29 3 Preventive Measures: Corporate Governance and Internal Controls     31 3.1 Introduction     31 3.2 Corporate Governance Issues in Developed Economies     33 3.3 Emerging Economies and Their Unique Corporate Governance Issues     34 3.4 Organizational Controls     39 3.5 A System of Internal Controls     41 3.6 The COSO Framework on Internal Controls     46 3.7 Benefits, Costs, and Limitations of Internal Controls     52 3.8 Incorporation of Fraud Risk in the Design of Internal Controls     56 3.9 Legislation on Internal Controls     58 3.10 Chapter Summary     58 3.11 Endnotes     60 4 Detection of Fraud: Shared Responsibility     61 4.1 Introduction     61 4.2 Expectations Gap in the Accounting Profession     64 4.3 Responsibility of the External Auditor     66 4.4 Responsibility of the Board of Directors     68 4.5 Role of the Audit Committee     71 4.6 Management’s Role and Responsibilities in the Financial Reporting Process     75 4.7 The Role of the Internal Auditor     78 4.8 Who Blows the Whistle     80 4.9 Chapter Summary     84 4.10 Endnotes     85 5 Data Mining     89 5.1 Introduction     89 5.2 Data Classification     91 5.3 Association Analysis     93 5.4 Cluster Analysis     95 5.5 Outlier Analysis     98 5.6 Data Mining to Detect Money Laundering     100 5.7 Chapter Summary     103 5.8 Endnotes     103 6 Transitioning to Evidence     105 6.1 Introduction     105 6.2 Probability Concepts and Terminology     106 6.3 Schematic Representation of Evidence     108 6.4 Information and Evidence     110 6.5 Mathematical Definitions of Prior, Conditional, and Posterior Probability     110 6.6 The Probative Value of Evidence     114 6.7 Bayes’ Rule     117 6.8 Chapter Summary     122 6.9 Endnote     123 7 Discrete Probability Distributions     125 7.1 Introduction     125 7.2 Generic Definitions and Notations     126 7.3 The Binomial Distribution     127 7.4 Poisson Probability Distribution     135 7.5 Hypergeometric Distribution     140 7.6 Chapter Summary     145 7.7 Endnotes     147 8 Continuous Probability Distributions     149 8.1 Introduction     149 8.2 Conceptual Development of Probability Framework     150 8.3 Uniform Probability Distribution     156 8.4 Normal Probability Distribution     157 8.5 Testing for Normality     168 8.6 Chebycheff ’s Inequality     170 8.7 Binomial Distribution Expressed as a Normal Distribution     171 8.8 The Exponential Distribution     172 8.9 Joint Distribution of Continuous Random Variables     173 8.10 Chapter Summary     176 9 Sampling Theory and Techniques     179 9.1 Introduction     179 9.2 Motivation for Sampling     180 9.3 Theory Behind Sampling     181 9.4 Statistical Sampling Techniques     182 9.5 Nonstatistical Sampling Techniques     186 9.6 Sampling Approaches in Auditing     189 9.7 Chapter Summary     191 9.8 Endnotes     193 10 Statistical Inference from Sample Information     195 10.1 Introduction     195 10.2 The Ability to Generalize Sample Data to Population Parameters     196 10.3 Central Limit Theorem and non-Normal Distributions     199 10.4 Estimation of Population Parameter     200 10.5 Confidence Intervals     203 10.6 Confidence Interval for Large Sample When Population Standard Deviation Is Known     205 10.7 Confidence Interval for a Large Sample When Population Standard Deviation Is Unknown     209 10.8 Confidence Intervals for Small Samples     211 10.9 Confidence Intervals for Proportions     213 10.10 Chapter Summary     214 10.11 Endnote     218 11 Determining Sample Size     219 11.1 Introduction     219 11.2 Computing Sample Size When Population Deviation Is Known     220 11.3 Sample Size Estimation when Population Deviation Is Unknown     222 11.4 Sample Size Estimation for Proportions     225 11.5 Chapter Summary     228 12 Regression and Correlation     231 12.1 Introduction     231 12.2 Probabilistic Linear Models     232 12.3 Correlation     233 12.4 Least Squares Regression     234 12.5 Coefficient of Determination     236 12.6 Test of Significance and p-Values     237 12.7 Prediction Using Regression     238 12.8 Caveats and Limitations of Regression Models     239 12.9 Other Regression Models     242 12.10 Chapter Summary     245 Index     249