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Fraud Data Analytics Methodology

Book Description

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.

Table of Contents

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