Statistical Techniques for Forensic Accounting

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.   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.   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 million 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.

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