- 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
Fraud Data Analytics Methodology
The Fraud Scenario Approach to Uncovering Fraud in Core Business Systems
LEONARD W. VONA
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