- Cover
- Title Page
- Copyright
- Dedication
- Preface
- Acknowledgments
- Chapter 1: Introduction to Fraud Data Analytics
- Chapter 2: Fraud Scenario Identification
- Fraud Risk Structure
- How to Define the Fraud Scope: Primary and Secondary Categories of Fraud
- Understanding the Inherent Scheme Structure
- The Fraud Circle
- The Five Categories of Fraud Scenarios
- What a Fraud Scenario Is Not
- How to Write a Fraud Scenario
- Understanding Entity Permutations Associated with the Entity Structure
- Practical Examples of a Properly Written Fraud Scenario
- Style versus Content of a Fraud Scenario
- How the Fraud Scenario Links to the Fraud Data Analytics
- Summary
- Appendix 1
- Appendix 2
- Chapter 3: Data Analytics Strategies for Fraud Detection
- Understanding How Fraud Concealment Affects Your Data Analytics Plan
- Low Sophistication
- Medium Sophistication
- High Sophistication
- Shrinking the Population through the Sophistication Factor
- Building the Fraud Scenario Data Profile
- Fraud Data Analytic Strategies
- Internal Control Avoidance
- Data Interpretation Strategy
- Number Anomaly Strategy
- Pattern Recognition and Frequency Analysis
- Strategies for Transaction Data File
- Summary
- Chapter 4: How to Build a Fraud Data Analytics Plan
- Plan Question One: What Is the Scope of the Fraud Data Analysis Plan?
- Plan Question Two: How Will the Fraud Risk Assessment Impact the Fraud Data Analytics Plan?
- Plan Question Three: Which Data‐Mining Strategy Is Appropriate for the Scope of the Fraud Audit?
- Plan Question Four: What Decisions Will the Plan Need to Make Regarding the Availability, Reliability, and Usability of the Data?
- Plan Question Five: Do You Understand the Data?
- Plan Question Six: What Are the Steps to Designing a Fraud Data Analytics Search Routine?
- Plan Question Seven: What Filtering Techniques Are Necessary to Refine the Sample Selection Process?
- Plan Question Eight: What Is the Basis of the Sample Selection Process?
- Plan Question Nine: What Is the Plan for Resolving False Positives?
- Plan Question Ten: What Is the Design of the Fraud Audit Test for the Selected Sample?
- Summary
- Appendix: Standard Naming Table List for Shell Company Audit Program
- Chapter 5: Data Analytics in the Fraud Audit
- How Fraud Auditing Integrates with the Fraud Scenario Approach
- How to Use Fraud Data Analytics in the Fraud Audit
- Fraud Data Analytics for Financial Reporting, Asset Misappropriation, and Corruption
- Impact of Fraud Materiality on the Sampling Strategy
- How Fraud Concealment Affects the Sampling Strategy
- Predictability of Perpetrators' Impact on the Sampling Strategy
- Impact of Data Availability and Data Reliability on the Sampling Strategy
- Change, Delete, Void, Override, and Manual Transactions Are a Must on the Sampling Strategy
- Planning Reports for Fraud Data Analytics
- How to Document the Planning Considerations
- Key Workpapers in Fraud Data Analytics
- Summary
- Chapter 6: Fraud Data Analytics for Shell Companies
- What Is a Shell Company?
- What Is a Conflict‐of‐Interest Company?
- What Is a Real Company?
- Fraud Data Analytics Plan for Shell Companies
- Fraud Data Analytics for the Traditional Shell Company
- Fraud Data Analytics for the Assumed Entity Shell Company
- Fraud Data Analytics for the Hidden Entity Shell Company
- Fraud Data Analytics for the Limited‐Use Shell Company
- Linkage of Identified Entities to Transactional Data File
- Fraud Data Analytics Scoring Sheet
- Impact of Fraud Concealment Sophistication Shell Companies
- Building the Fraud Data Profile for a Shell Company
- Fraud Audit Procedures to Identify the Shell Corporation
- Summary
- Chapter 7: Fraud Data Analytics for Fraudulent Disbursements
- Inherent Fraud Schemes in Fraudulent Disbursements
- Identifying the Key Data: Purchase Order, Invoice, Payment, and Receipt
- Documents and Fraud Data Analytics
- FDA Planning Reports for Disbursement Fraud
- FDA for Shell Company False Billing Schemes
- Understanding How Pass‐Through Schemes Operate
- Identify Purchase Orders with Changes
- False Administration through the Invoice File
- Summary
- Chapter 8: Fraud Data Analytics for Payroll Fraud
- Chapter 9: Fraud Data Analytics for Company Credit Cards
- Abuse versus Asset Misappropriation versus Corruption
- Inherent Fraud Scheme Structure
- Real Vendor Scenarios Where the Vendor Is Not Complicit
- Real Vendor Scenarios Where the Vendor Is Complicit
- False Vendor Scenario
- Impact of Scheme versus Concealment
- Fraud Data Analytic Strategies
- Linking Human Resources to Credit Card Information
- Planning for the Fraud Data Analytics Plan
- Fraud Data Analytics Plan Approaches
- File Layout Description for Credit Card Purchases
- FDA for Procurement Card Scenarios
- Summary
- Chapter 10: Fraud Data Analytics for Theft of Revenue and Cash Receipts
- Inherent Scheme for Theft of Revenue
- Identifying the Key Data and Documents
- Theft of Revenue Before Recording the Sales Transaction
- Theft of Revenue after Recording the Sales Transaction
- Pass‐through Customer Fraud Scenario
- False Adjustment and Return Scenarios
- Theft of Customer Credit Scenarios
- Lapping Scenarios
- Illustration of Lapping in the Banking Industry with Term Loans
- Currency Conversion Scenarios or Theft of Sales Paid in Currency
- Theft of Scrap Income or Equipment Sales
- Theft of Inventory for Resale
- Bribery Scenarios for Preferential Pricing, Discounts, or Terms
- Summary
- Chapter 11: Fraud Data Analytics for Corruption Occurring in the Procurement Process
- What Is Corruption?
- Inherent Fraud Schemes for the Procurement Function
- Identifying the Key Documents and Associated Data
- Overall Fraud Approach for Corruption in the Procurement Function
- Fraud Audit Approach for Corruption
- What Data Are Needed for Fraud Data Analytics Plan?
- Fraud Data Analytics: The Overall Approach for Corruption in the Procurement Function
- Linking the Fraud Action Statement to the Fraud Data Analytics
- Bid Avoidance: Fraud Data Analytics Plan
- Favoritism in the Award of Purchase Orders: Fraud Data Analytics Plan
- Summary
- Chapter 12: Corruption Committed by the Company
- Fraud Scenario Concept Applied to Bribery Provisions
- Creating the Framework for the Scope of the Fraud Data Analytics Plan
- Planning Reports
- Planning the Understanding of the Authoritative Sources
- FDA for Compliance with Company Policies
- FDA Based on Prior Enforcement Actions Using Transactional Issues
- FDA Based on the Internal Control Attributes of DOJ Opinion Release 04‐02 or the UK Bribery Act: Guidance on Internal Controls
- Building the Fraud Data Analytics Routines to Search for Questionable Payments
- FDA for Questionable Payments That Are Recorded on the Books
- FDA for Funds That Are Removed from the Books to Allow for Questionable Payments
- Overall Strategy for the Record‐Keeping Provisions
- FDA for Questionable Payments That Fail the Record‐Keeping Provision as to Proper Recording in the General Ledger
- FDA for Questionable Payments That Have a False Description of the Business Purpose
- Summary
- Chapter 13: Fraud Data Analytics for Financial Statements
- What Is an Error?
- What Is Earnings Management?
- What Is Financial Statement Fraud?
- How Does an Error Differ from Fraud?
- Inherent Fraud Schemes and Financial Statement Fraud Scenarios
- Additional Guidance in Creating the Fraud Action Statement
- How Does the Inherent Fraud Scheme Structure Apply to the Financial Statement Assertions?
- Do I Understand the Data?
- What Is a Fraud Data Analytics Plan for Financial Statements?
- What Are the Accounting Policies for Assets, Liabilities, Equity, Revenue, and Expense Accounts?
- Summary
- Chapter 14: Fraud Data Analytics for Revenue and Accounts Receivable Misstatement
- What Is Revenue Recognition Fraud?
- Inherent Fraud Risk Schemes in Revenue Recognition
- Inherent Fraud Schemes and Creating the Revenue Fraud Scenarios
- Identifying Key Data on Key Documents
- Fraud Brainstorming for Revenue
- FDA for False Revenue Scenarios
- False Revenue for False Customers through Accounts Receivable Analysis
- Fraud Concealment Strategies for False Revenue Fraud Scenarios
- Fraud Data Analytics for Percentage of Completion Revenue Recognition
- Summary
- Chapter 15: Fraud Data Analytics for Journal Entries
- Appendix A: Data Mining Audit Program for Shell Companies
- About the Author
- Index
- End User License Agreement
This book is dedicated to my family, Patricia, Amy, David, and Jeffrey, for supporting me in my quest to explain fraud auditing. In the memory of my dad, who told me to go to college, and the memory of the women who shaped my life.
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