| Description |
1 online resource (403 pages) |
| Contents |
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 |
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Chapter 2: Fraud Scenario IdentificationFraud 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 |
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Practical Example of Permanent versus Temporary TakeoverPractical 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 |
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Shrinking the Population through the Sophistication FactorBuilding 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 |
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Consider the Following ScenarioNumber 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? |
| Summary |
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 |
| Notes |
What Would Cause a Pattern to Be a Data Anomaly versus a Red Flag of Fraud? |
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Print version record |
| Subject |
Auditing.
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Forensic accounting.
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Fraud -- Prevention
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Auditing, Internal.
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BUSINESS & ECONOMICS -- Auditing.
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Auditing
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Auditing, Internal
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Forensic accounting
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Fraud -- Prevention
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| Form |
Electronic book
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| ISBN |
9781119270348 |
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1119270340 |
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9781119270355 |
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1119270359 |
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