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

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Features

The first complete guide to using statistical techniques to identify financial fraud and effectively communicate the findings. 

• Explores the mathematical concepts and principles behind the techniques, so practitioners can use them properly
• Walks through constructing and conducting valid, defensible statistical tests, and explaining analyses for anyone involved in corporate finance
• Covers exploratory data analysis, data mining, software tools, and more

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.

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The Challenges in Forensic Accounting

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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

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