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Fraud and Fraud Detection: A Data Analytics Approach

Fraud and Fraud Detection: A Data Analytics Approach

作者： Sunder Gee
出版商： John Wiley & Sons
ISBN: 9781118779651
出版时间 December 2014
规格： Hardback , 352 pages
适应领域： International ? 免责申明：
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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.
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
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.

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