Fraud and Fraud Detection: A Data Analytics Approach + Website

THE MUST–HAVE GUIDE TO USING BIG DATA AND AUTOMATION TO DETECT FRAUD FASTER

Sooner or later, every fraudster makes a mistake. Fraud and Fraud Detection: A Data Analytics Approach will show you how to automate statistical and data analytical tests to catch those mistakes, identify criminals, and eliminate the fraud.

With big data analytics, you can find and stop fraudulent transactions much more efficiently than ever before. Detecting a few tiny abnormalities among thousands or millions of legitimate transactions has become a simple matter thanks to big data analytics, and Fraud and Fraud Detection explains how you can use these new techniques to mitigate losses and stop valuable resources from seeping through the cracks.

Fraud and Fraud Detection will help you reach the next phase in fraud mitigation so you can keep up with the latest challenges. You′ll learn how to automatically apply key tests, including variability from the center, Benford′s Law, the number duplication test, the relevant size factor test, and many more, to all the major types of fraud. Starting with how to identify important data sets and ending with implementing a well–designed, automated fraud detection plan, this book covers every step of the process.

Fraud and Fraud Detection explains what you need to understand about big data and provides a step–by–step guide to implementing the most important tools and techniques including templates, downloadable automation scripts, and access to the CaseWare IDEA software. This approach has been ready to go for years, and it′s time you took advantage of big data to uncover needless losses.

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.