Fraud and Fraud Detection - A Data Analytics Approach + Website

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.

Sommario

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
 

Info autore

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.

Riassunto

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.

Dettagli sul prodotto

• Autori: Sunder Gee, Gee Sunder
• Editore: Wiley, John and Sons Ltd
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 29.01.2015
• EAN: 9781118779651
• ISBN: 978-1-118-77965-1
• Pagine: 352
• Dimensioni: 182 mm x 260 mm x 30 mm
• Serie: Wiley Corporate F&A; Wiley Corporate F&A
• Categorie: Scienze sociali, diritto, economia > Economia > Economia aziendale; Rechnungswesen, Accounting, Bilanzdelikt, Crime and criminology, Rechnungswesen / Technologie, Accounting Technology