Fraud and Fraud Detection, + Website: A Data Analytics Approach(Wiley Corporate F&A)

Detect fraud faster—no matter how well hidden—with 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 data analytics skills that can bring these anomalies to light. Step-by-step instruction and practical advice provide the specific abilities that will enhance the audit and investigation process. Readers will learn to: Understand the different areas of fraud and their specific detection methods Identify anomalies and risk areas using computerized techniques Develop a step-by-step plan for detecting fraud through data analytics Utilize IDEA software to automate detection and identification procedures The delineation of detection techniques for each type of fraud makes this book a must-have for students and new fraud prevention professionals, and the step-by-step guidance to automation and complex analytics will prove useful for even experienced examiners. With datasets growing exponentially, increasing both the speed and sensitivity of detection helps fraud professionals stay ahead of the game. Fraud and Fraud Detection is a guide to more efficient, more effective fraud identification.

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

About the Author :
SUNDER GEE spent much of his career at the Canada Revenue Agency, including holding the position of Electronic Commerce Audit Advisor for the Head Office. He has advised tax authorities around the world on the topic of computer-assisted audit techniques (CAAT). Sunder has prepared widely respected corporate training material and college courses on forensic accounting, anti–money laundering, and data analytics.