Description
Book SynopsisDetect fraud fasterno matter how well hiddenwith IDEA automation Fraud and Fraud Detection takes an advanced approach to fraud management, providing step-by-step guidance on automating detection and forensics using CaseWare''s IDEA software. The book begins by reviewing the major types of fraud, then details the specific computerized tests that can detect them. Readers will learn to use complex data analysis techniques, including automation scripts, allowing easier and more sensitive detection of anomalies that require further review. The companion website provides access to a demo version of IDEA, along with sample scripts that allow readers to immediately test the procedures from the book.
Business systems'' electronic databases have grown tremendously with the rise of big data, and will continue to increase at significant rates. Fraudulent transactions are easily hidden in these enormous datasets, but Fraud and Fraud Detection helps readers gain the d
Table of Contents
Foreword ix
Preface xi
Acknowledgments xv
Chapter 1: Introduction 1
Defining Fraud 1
Anomalies versus Fraud 2
Types of Fraud 2
Assess the Risk of Fraud 4
Conclusion 6
Notes 6
Chapter 2: Fraud Detection 7
Recognizing Fraud 7
Data Mining versus Data Analysis and Analytics 10
Data Analytical Software 11
Anomalies versus Fraud within Data 12
Fraudulent Data Inclusions and Deletions 14
Conclusion 14
Notes 15
Chapter 3: The Data Analysis Cycle 17
Evaluation and Analysis 17
Obtaining Data Files 19
Performing the Audit 22
File Format Types 24
Preparation for Data Analysis 24
Arranging and Organizing Data 33
Conclusion 35
Notes 35
Chapter 4: Statistics and Sampling 37
Descriptive Statistics 37
Inferential Statistics 38
Measures of Center 38
Measure of Dispersion 39
Measure of Variability 40
Sampling 41
Conclusion 65
Notes 65
Chapter 5: Data Analytical Tests 67
Benford’s Law 68
Number Duplication Test 77
Z-Score 81
Relative Size Factor Test 84
Same-Same-Same Test 93
Same-Same-Different Test 94
Even Amounts 98
Conclusion 99
Notes 100
Chapter 6: Advanced Data Analytical Tests 101
Correlation 101
Trend Analysis 104
GEL-1 and GEL-2 109
Conclusion 121
Note 122
Chapter 7: Skimming and Cash Larceny 123
Skimming 123
Cash Larceny 124
Case Study 124
Conclusion 131
Chapter 8: Billing Schemes 133
Data and Data Familiarization 134
Benford’s Law Tests 138
Relative Size Factor Test 139
Z-Score 140
Even Dollar Amounts 141
Same-Same-Same Test 144
Same-Same-Different Test 145
Payments without Purchase Orders Test 146
Length of Time between Invoice and Payment Dates Test 151
Search for Post Office Box 152
Match Employee Address to Supplier 155
Duplicate Addresses in Vendor Master 157
Payments to Vendors Not in Master 158
Gap Detection of Check Number Sequences 161
Conclusion 162
Note 162
Chapter 9: Check-Tampering Schemes 163
Electronic Payments Fraud Prevention 164
Check Tampering 165
Data Analytical Tests 166
Conclusion 171
Chapter 10: Payroll Fraud 173
Data and Data Familiarization 175
Data Analysis 181
The Payroll Register 193
Payroll Master and Commission Tests 194
Conclusion 195
Notes 196
Chapter 11: Expense Reimbursement Schemes 197
Data and Data Analysis 201
Conclusion and Audit Trail 219
Notes 220
Chapter 12: Register Disbursement Schemes 221
False Refunds and Adjustments 221
False Voids 222
Concealment 222
Data Analytical Tests 222
Conclusion 233
Chapter 13: Noncash Misappropriations 235
Types of Noncash Misappropriations 235
Concealment of Noncash Misappropriations 237
Data Analytics 238
Conclusion 240
Chapter 14: Corruption 243
Bribery 243
Tender Schemes 244
Kickbacks, Illegal Gratuities, and Extortion 245
Conflict of Interest 246
Data Analytical Tests 247
Concealment 250
Conclusion 250
Chapter 15: Money Laundering 253
The Money-Laundering Process 254
Other Money Transfer Systems and New Opportunities 256
Audit Areas and Data Files 257
Conclusion 259
Chapter 16: Zapper Fraud 261
Point-of-Sales System Case Study 265
Quantifying the Zapped Records 294
Additional POS Data Files to Analyze 296
Missing and Modified Bills 297
The Markup Ratios 299
Conclusions and Solutions 300
Notes 302
Chapter 17: Automation and IDEAScript 303
Considerations for Automation 304
Creating IDEAScripts 306
Conclusion 316
Chapter 18: Conclusion 319
Financial Statement Fraud 319
IDEA Features Demonstrated 321
Projects Overview 323
Data Analytics: Final Words 325
Notes 326
About the Author 327
About the Website 329
Index 333
