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Fraud Data Analytics Methodology: The Fraud Scenario Approach to Uncovering Fraud in Core Business Systems




Fraud Data Analytics Methodology: The Fraud Scenario Approach to Uncovering Fraud in Core Business Systems

Leonard W. Vona

ISBN: 978-1-119-18679-3 March 2017 400 Pages


Uncover hidden fraud and red flags using efficient data analytics

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.

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