Fraud and Fraud Detection The Wiley Corporate F&A series provides information, tools, and insights to corporate professionals responsible for issues affecting the profi tability of their company, from accounting and fi nance to internal controls and performance management. Founded in 1807, John Wiley & Sons is the oldest independent publishing company in the United States. With offi ces in North America, Europe, Asia, and Australia, Wiley is globally committed to developing and marketing print and electronic products and services for our customers’ professional and personal knowledge and understanding. Fraud and Fraud Detection A Data Analytics Approach SUNDER GEE Cover image: ©iStock.com/Mr.Vi Cover design: Wiley Copyright © 2015 by Sunder Gee. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. 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Fraud. 2. Managerial accounting. 3. Data mining. I. Title. HV6691.G44 2014 658.4'73—dc23 2014021352 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 Contents Foreword ix Preface xi Acknowledgments xv Chapter 1: Introduction 1 Defi ning 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 v vi ◾ Contents 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 Offi ce 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 Contents ◾ vii 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 Confl ict 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 viii ◾ Contents 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 Modifi ed 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