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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

Tabla de contenidos provista por Syndetics

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

Reseñas proporcionadas por Syndetics

CHOICE Review

Surprisingly, the first third of Statistical Techniques for Forensic Accounting does not include a single formula, equation, or statistical graph. Instead, Dutta (Univ. of Albany) presents an excellent review of the various types of financial frauds; offers numerous case examples; and discusses the roles played by government regulations, corporate governance, internal control, and professional organizations in dealing with financial fraud. The preface states practicing accountants will require no prior knowledge in probability and statistics. However, unless readers are well versed in the subject matter, they should be prepared to spend significant time studying the remainder of the book. Sampling techniques to mine data in search of a pattern of errors created by financial fraud (and not random occurrences) are described in detail. Forensic accountants and investigators are provided a means not only to detect financial fraud but also, using probabilistic inferences, to transition the findings into quantitative evidence, which adds another weapon in the arsenal to prosecute perpetrators. This volume should be on the mandatory reading list for individuals developing the US Securities and Exchange Commission's Accounting Quality Model to detect accounting fraud and the SEC/PCAOB's Financial Reporting and Audit Task Force. Summing Up: Highly recommended. Advanced students studying forensic accounting; practicing forensic accountants. R. Derstine West Chester University

Notas de autor provistas por Syndetics

Dr. Saurav K. Dutta is an Associate Professor at the Department of Accounting, Business Law, and Taxation at the State University of New York at Albany, where he previously served as the Chairman of the Department. He has taught at the Graduate School of Management, Rutgers University, and at Zicklin College of Business, Baruch College, New York. He holds a Bachelor of Technology Degree in Aerospace Engineering from the Indian Institute of Technology (Bombay) and a Ph. D. in Accounting from the University of Kansas.

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His research interests are in applying statistical and analytic methodology to accounting and auditing problems, and his current work involves analyzing problems in financial reporting, as well as studying the accounting aspects of corporate sustainability initiatives. He has published over 25 research papers in academic journals, including Auditing: A Journal of Practice and Theory; Journal of Accounting, Auditing, and Finance; Journal of Accounting and Public Policy; Issues in Accounting Education; Journal of Cost Management; Journal of Corporate Accounting and Finance ; International Journal of Technology Management; The Quality Management Journal; Corporate Social Responsibility and Environmental Management; Strategic Finance; and others . Dr. Dutta has presented his research findings at numerous national and international academic conferences and has conducted research seminars at many universities including, Harvard, Oxford, New York University, Rutgers, University of Southern California, Michigan State, Bentley, and Maastricht. He has conducted professional teaching and training seminars at Dai-Ichi-Kangyo Bank, Merrill Lynch, Prudential Insurance Company, and KPMG LLP. He has been invited by the AICPA to conduct workshops on the use of statistics in forensic accounting, and he has also been the "Featured Speaker" for the Corporate Director's Group.

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Dr. Dutta has been engaged to design and analyze statistical tests on numerous accounting/litigation projects under the jurisdiction of the New York State Attorney General's Office, U.S. District Court of the Southern District of New York, and the Securities and Exchange Commission, among others. Some of these engagements involved designing statistical procedures to verify claims for settlements of amounts ranging from $500 million to $6.1 billion and include the settlements for MCI-WorldCom, Global Crossing, Cendant Corp, and HealthSouth. He was involved with the reparations of more than 400 millionnbsp;CHF from the Swiss banks, under the purview of the U.S. District Court of Eastern New York. He has also been engaged to evaluate accounting systems related to hedge accounting, fair value accounting, and mergers and acquisition. Since 2006 he has served as the Subject Matter Expert (SME) for the IMA in their preparation and updating of the CMA examination study guide.