Statistical Techniques for Forensic Accounting
Understanding the Theory and Application of Data Analysis
(Sprache: Englisch)
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...
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Klappentext zu „Statistical Techniques for Forensic Accounting “
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
Inhaltsverzeichnis zu „Statistical Techniques for Forensic Accounting “
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
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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
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Autoren-Porträt von Saurav K. Dutta
SAURAV DUTTA (Albany, NY) is Associate Professor and former chair of the Department of Accounting, Business Law and Taxation at Univ. of Albany. In 2011, he served as Academic Fellow in the SEC's Office of Chief Accountant. Dutta's research interests are in applying statistical and analytical methodologies to accounting and auditing. He has published 20+ research papers, and conducted seminars at universities including Harvard, Oxford, NYU, and Maastricht. Invited by AICPA to present on statistics for forensic accounting and litigation, he has been engaged as an expert to design statistical procedures to verify claims against Worldcom, Global Crossing, Cendant, GM, and HealthSouth.
Bibliographische Angaben
- Autor: Saurav K. Dutta
- 2013, 288 Seiten, mit Abbildungen, Maße: 15,2 x 23,8 cm, Gebunden, Englisch
- Verlag: Financial Times/ Prentice Hall
- ISBN-10: 0133133818
- ISBN-13: 9780133133813
- Erscheinungsdatum: 18.06.2013
Sprache:
Englisch
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