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

Detalles Bibliográficos
Otros Autores: Vona, Leonard W., 1955- author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons [2017]
Edición:1st ed
Colección:Wiley corporate F & A series.
Materias:
Auditing.
Forensic accounting.
Fraud > Prevention.
Auditing, Internal.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849092206719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright
  • Contents
  • Preface
  • Acknowledgments
  • Chapter 1: Introduction to Fraud Data Analytics
  • What Is Fraud Data Analytics?
  • What Is Fraud Auditing?
  • What Is a Fraud Scenario?
  • What Is Fraud Concealment?
  • What Is a Red Flag?
  • What Is a False Positive?
  • What Is a False Negative?
  • Fraud Data Analytics Methodology
  • Assumptions in Fraud Data Analytics
  • The Fraud Scenario Approach
  • The Likelihood Conundrum: Internal Control Assessment or Fraud Data Analytics
  • How the Fraud Scenario Links to the Fraud Data Analytics Plan
  • Skills Necessary for Fraud Data Analytics
  • Summary
  • 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
  • Vulnerabilities in the Fraud Scenario Matrix
  • Inherent Schemes to Fraud Scenario
  • 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
  • False Entity
  • Real Entity That Is Complicit in the Fraud Scenario
  • Real Entity That Is Not Complicit in the Fraud Scenario
  • Practical Example of Permanent versus Temporary Takeover
  • Practical Examples of a Properly Written Fraud Scenario
  • First Illustration: Accounts Payable
  • Second Illustration: Payroll
  • Style versus Content of a Fraud Scenario
  • How the Fraud Scenario Links to the Fraud Data Analytics
  • Illustration of the Sample Selection Process
  • The Fraud Data Analytics Plan
  • 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
  • Precision of Matching Concept on Red Flags
  • Fraud Data Analytic Strategies
  • Specific Identification of a Data Element or an Internal Control Anomaly
  • Consider the Following Scenario
  • Internal Control Avoidance
  • The Fundamental Strategies for Internal Control Avoidance
  • Illustrative Examples of Internal Control Avoidance
  • Guidelines for Use of Internal Control Avoidance Strategy
  • Consider the Following Scenario
  • Data Interpretation Strategy
  • Guidelines for Use of Data Interpretation
  • Consider the Following Scenario
  • Number Anomaly Strategy
  • Guidelines for Using the Number Anomaly Strategy
  • Consider the Following Scenario
  • Pattern Recognition and Frequency Analysis
  • Frequency Analysis
  • Pattern Recognition
  • Strategies for Master File Data
  • Guidelines in Building Data Interrogation Routines for Entity Types
  • Strategies for Transaction Data File
  • What Data Are Available for the Business Transaction?
  • What Control Number Patterns Could Occur within the Specific Data Item?
  • What Control Number Pattern Would Normally Exist in the Database?
  • What Would Cause a Pattern to Be a Data Anomaly versus a Red Flag of Fraud?
  • Which Patterns Link to the Fraud Scenario?
  • How Do We Develop a Data Interrogation Routine to Locate the Links to the Fraud Scenario?
  • Illustrative Example of Transactional Data and False Entity
  • Summary
  • Chapter 4: How to Build a Fraud Data Analytics Plan
  • Plan Question One: What Is the Scope of the Fraud Data Analysis Plan?
  • Scope Concept for the Corruption Project
  • Plan Question Two: How Will the Fraud Risk Assessment Impact the Fraud Data Analytics Plan?
  • Continued Illustration of the Corruption Project.
  • Plan Question Three: Which Data-Mining Strategy Is Appropriate for the Scope of the Fraud Audit?
  • Continued Illustration of Corruption Project
  • Plan Question Four: What Decisions Will the Plan Need to Make Regarding the Availability, Reliability, and Usability of the Data?
  • Entity Availability and Reliability
  • Transaction Availability and Reliability
  • The Usability Analysis
  • Continued Illustration of Corruption Project
  • Plan Question Five: Do You Understand the Data?
  • Continued Illustration of Corruption Project
  • Plan Question Six: What Are the Steps to Designing a Fraud Data Analytics Search Routine?
  • Step 6.1: Identify the Fraud Scenario
  • Step 6.2: Identify the Data That Relates to the Scenario
  • Step 6.3: Select the Fraud Data Analytics Strategy
  • Step 6.4: Clean the Data Set: Data Availability, Data Reliability, and Data Usability
  • Step 6.5: Identify Logical Errors
  • Step 6.6: Create the Homogeneous Data Files Using the Inclusion and Exclusion Theory
  • Step 6.7: Build the Fraud Data Analytics Test through Identifying the Selection Criteria
  • Step 6.8: Programming Routines to Identify the Selection Criteria
  • Plan Question Seven: What Filtering Techniques Are Necessary to Refine the Sample Selection Process?
  • Continued Illustration of Corruption Project
  • Plan Question Eight: What Is the Basis of the Sample Selection Process?
  • Continued Illustration of Corruption Project
  • Plan Question Nine: What Is the Plan for Resolving False Positives?
  • Continued Illustration of Corruption Project
  • Plan Question Ten: What Is the Design of the Fraud Audit Test for the Selected Sample?
  • Continued Illustration of Corruption Project
  • Illustrative Example of a Fraud Data Analytics Plan Using Payroll Fraud Scenarios
  • Summary
  • Appendix: Standard Naming Table List for Shell Company Audit Program
  • Vendor Master File.
  • Vendor Invoice File
  • Purchase Order Data
  • Disbursement File
  • Master File Change File
  • 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
  • Understanding How to Use Data from a Fraud Perspective
  • Using Data in the Exclusion and Inclusion Theory
  • 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
  • Low Sophistication and Internal Perpetrator
  • Medium Sophistication and Internal Perpetrator
  • High Sophistication and Internal Perpetrator
  • Low Sophistication and External Perpetrator Permutation
  • Medium Sophistication and External Perpetrator
  • High Sophistication and External Perpetrator.
  • Building the Fraud Data Profile for a Shell Company
  • Shell Company Profile Information
  • Shell Companies Operating as Customers
  • Shell Companies as Employees
  • Fraud Audit Procedures to Identify the Shell Corporation
  • Entity Verification
  • Summary of Intelligence Information Regarding Shell Companies
  • 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
  • Version One Description
  • Version Two Description
  • Version Three Description
  • Version Four Description
  • Version Five Description
  • Version Six Description
  • Identify Purchase Orders with Changes
  • FDA: Changes to the Purchase Order
  • False Administration through the Invoice File
  • FDA: Change through the Invoice File
  • FDA: Circumvention through Small-Dollar Purchases
  • Searching the Opportunity Files for Specific Overbilling Techniques
  • Summary
  • Chapter 8: Fraud Data Analytics for Payroll Fraud
  • Inherent Fraud Schemes for Payroll
  • Understanding How Payroll Is Calculated
  • Planning Reports for Payroll Fraud
  • FDA for Ghost Employee Schemes
  • Fictitious Employee That Does Not Exist
  • Real Employee, Not Complicit, Temporary Takeover of Identity
  • Real Employee, Not Complicit, Permanent Takeover of Identity
  • Real Employee, Not Complicit, Employee Who Is Reactivated
  • Real Employee, Not Complicit, Pre-Employment
  • Real Employee Who Is Complicit and Performs No Services: Asset Misappropriation
  • Real Employee Who Is Complicit and Performs No Services: Corruption.
  • Human Resources Error Resulting in a Real Employee to Continue Receiving Direct Deposit after Departing from the Workforce.

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