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More About This Title Fraud Data Analytics Methodology: The fraud scenario approach to uncovering fraud in core business systems
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Fraud Data Analytics Methodology addresses the need for clear, reliable fraud detection with a solid framework for a robust data analytic plan. By combining fraud risk assessment and fraud data analytics, you'll be able to better identify and respond to the risk of fraud in your audits. Proven techniques help you identify signs of fraud hidden deep within company databases, and strategic guidance demonstrates how to build data interrogation search routines into your fraud risk assessment to locate red flags and fraudulent transactions. These methodologies require no advanced software skills, and are easily implemented and integrated into any existing audit program. Professional standards now require all audits to include data analytics, and this informative guide shows you how to leverage this critical tool for recognizing fraud in today's core business systems.
Fraud cannot be detected through audit unless the sample contains a fraudulent transaction. This book explores methodologies that allow you to locate transactions that should undergo audit testing.
- Locate hidden signs of fraud
- Build a holistic fraud data analytic plan
- Identify red flags that lead to fraudulent transactions
- Build efficient data interrogation into your audit plan
Incorporating data analytics into your audit program is not about reinventing the wheel. A good auditor must make use of every tool available, and recent advances in analytics have made it accessible to everyone, at any level of IT proficiency. When the old methods are no longer sufficient, new tools are often the boost that brings exceptional results. Fraud Data Analytics Methodology gets you up to speed, with a brand new tool box for fraud detection.
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LEONARD W. VONA, CPA, CFE, is the CEO of Fraud Auditing and a world-renowned authority in fraud auditing. He has provided expert witness testimony in federal and state courts; consulted with corporations around the world; and is the author of Fraud Risk Assessment: Building the Fraud Audit Program and The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, both published by Wiley. A forensic auditor with more than 38 years of diversified forensic auditing experience, he has provided more than 1,500 days of fraud training around the world.
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Preface ix
Acknowledgments xi
Chapter 1: Introduction to Fraud Data Analytics 1
Chapter 2: Fraud Scenario Identification 17
Chapter 3: Data Analytics Strategies for Fraud Detection 41
Chapter 4: How to Build a Fraud Data Analytics Plan 81
Chapter 5: Data Analytics in the Fraud Audit 109
Chapter 6: Fraud Data Analytics for Shell Companies 127
Chapter 7: Fraud Data Analytics for Fraudulent Disbursements 149
Chapter 8: Fraud Data Analytics for Payroll Fraud 183
Chapter 9: Fraud Data Analytics for Company Credit Cards 205
Chapter 10: Fraud Data Analytics for Theft of Revenue and Cash Receipts 227
Chapter 11: Fraud Data Analytics for Corruption Occurring in the Procurement Process 247
Chapter 12: Corruption Committed by the Company 269
Chapter 13: Fraud Data Analytics for Financial Statements 285
Chapter 14: Fraud Data Analytics for Revenue and Accounts Receivable Misstatement 311
Chapter 15: Fraud Data Analytics for Journal Entries 333
Appendix A: Data Mining Audit Program for Shell Companies 349
About the Author 363
Index 365
