- ホーム
- > 洋書
- > 英文書
- > Business / Economics
Full Description
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 flagsData mining: tools, usage, and limitationsTraditional statistical terms and methods applicable to forensic accountingUncertainty and probability theories and their forensic implicationsBayesian analysis and networksStatistical inference, sampling, sample size, estimation, regression, correlation, classification, and predictionHow to construct and conduct valid and defensible statistical testsHow to articulate and effectively communicate findings to other interested and knowledgeable parties
Contents
Foreword xiiiAcknowledgments xvPreface xviii1 Introduction: The Challenges in Forensic Accounting 11.1 Introduction 11.2 Characteristics and Types of Fraud 31.3 Management Fraud Schemes 71.4 Employee Fraud Schemes 111.5 Cyber-crime 171.6 Chapter Summary 181.7 Endnotes 192 Legislation, Regulation, and Guidance Impacting Forensic Accounting 212.1 Introduction 212.2 U.S. Legislative Response to Fraudulent Financial Reporting 222.3 The Emphasis on Prosecution of Fraud at the Department of Justice 242.4 The Role of the FBI in Detecting Corporate Fraud 262.5 Professional Guidance in SAS 99 272.6 Chapter Summary 282.7 Endnotes 293 Preventive Measures: Corporate Governance and Internal Controls 313.1 Introduction 313.2 Corporate Governance Issues in Developed Economies 333.3 Emerging Economies and Their Unique Corporate Governance Issues 343.4 Organizational Controls 393.5 A System of Internal Controls 413.6 The COSO Framework on Internal Controls 463.7 Benefits, Costs, and Limitations of Internal Controls 523.8 Incorporation of Fraud Risk in the Design of Internal Controls 563.9 Legislation on Internal Controls 583.10 Chapter Summary 583.11 Endnotes 604 Detection of Fraud: Shared Responsibility 614.1 Introduction 614.2 Expectations Gap in the Accounting Profession 644.3 Responsibility of the External Auditor 664.4 Responsibility of the Board of Directors 684.5 Role of the Audit Committee 714.6 Management's Role and Responsibilities in the Financial Reporting Process 754.7 The Role of the Internal Auditor 784.8 Who Blows the Whistle 804.9 Chapter Summary 844.10 Endnotes 855 Data Mining 895.1 Introduction 895.2 Data Classification 915.3 Association Analysis 935.4 Cluster Analysis 955.5 Outlier Analysis 985.6 Data Mining to Detect Money Laundering 1005.7 Chapter Summary 1035.8 Endnotes 1036 Transitioning to Evidence 1056.1 Introduction 1056.2 Probability Concepts and Terminology 1066.3 Schematic Representation of Evidence 1086.4 Information and Evidence 1106.5 Mathematical Definitions of Prior, Conditional, and Posterior Probability 1106.6 The Probative Value of Evidence 1146.7 Bayes' Rule 1176.8 Chapter Summary 1226.9 Endnote 1237 Discrete Probability Distributions 1257.1 Introduction 1257.2 Generic Definitions and Notations 1267.3 The Binomial Distribution 1277.4 Poisson Probability Distribution 1357.5 Hypergeometric Distribution 1407.6 Chapter Summary 1457.7 Endnotes 1478 Continuous Probability Distributions 1498.1 Introduction 1498.2 Conceptual Development of Probability Framework 1508.3 Uniform Probability Distribution 1568.4 Normal Probability Distribution 1578.5 Testing for Normality 1688.6 Chebycheff 's Inequality 1708.7 Binomial Distribution Expressed as a Normal Distribution 1718.8 The Exponential Distribution 1728.9 Joint Distribution of Continuous Random Variables 1738.10 Chapter Summary 1769 Sampling Theory and Techniques 1799.1 Introduction 1799.2 Motivation for Sampling 1809.3 Theory Behind Sampling 1819.4 Statistical Sampling Techniques 1829.5 Nonstatistical Sampling Techniques 1869.6 Sampling Approaches in Auditing 1899.7 Chapter Summary 1919.8 Endnotes 19310 Statistical Inference from Sample Information 19510.1 Introduction 19510.2 The Ability to Generalize Sample Data to Population Parameters 19610.3 Central Limit Theorem and non-Normal Distributions 19910.4 Estimation of Population Parameter 20010.5 Confidence Intervals 20310.6 Confidence Interval for Large Sample When Population Standard Deviation Is Known 20510.7 Confidence Interval for a Large Sample When Population Standard Deviation Is Unknown 20910.8 Confidence Intervals for Small Samples 21110.9 Confidence Intervals for Proportions 21310.10 Chapter Summary 21410.11 Endnote 21811 Determining Sample Size 21911.1 Introduction 21911.2 Computing Sample Size When Population Deviation Is Known 22011.3 Sample Size Estimation when Population Deviation Is Unknown 22211.4 Sample Size Estimation for Proportions 22511.5 Chapter Summary 22812 Regression and Correlation 23112.1 Introduction 23112.2 Probabilistic Linear Models 23212.3 Correlation 23312.4 Least Squares Regression 23412.5 Coefficient of Determination 23612.6 Test of Significance and p-Values 23712.7 Prediction Using Regression 23812.8 Caveats and Limitations of Regression Models 23912.9 Other Regression Models 24212.10 Chapter Summary 245Index 249
