WEEK
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LEARNING OBJECTIVE
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CONTENT
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READINGS
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1
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LO1: Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting
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Introduction to Fraud
Types of Fraud
The Need for Analysis Tools
Matrices
Link Diagrams
Social Network Analysis
Analysing Networks
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Forensic Accounting and Fraud Investigation for Non-Experts, H. Silverstone and M. Sheetz, Chapter 2, Fraud in Society
Forensic Accounting and Fraud Investigation for Non-Experts, H. Silverstone and M. Sheetz, Chapter 12, Analysis Tools for Investigators
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2
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LO1: Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting
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Introduction to Financial Analysis
Key Ratios
Data Mining as an Analysis Tool
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Forensic Accounting and Fraud Investigation for Non-Experts, H. Silverstone and M. Sheetz, Chapter 5, Fundamental Principles of Financial Analysis
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3
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LO1: Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting
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Introduction to Data Mining
Data Classification
Association Analysis
Cluster Analysis
Outlier Analysis
Application: Data Mining to Detect Money Laundering
Tracing
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Statistical Techniques for Forensic Accounting, S. K. Dutta, Chapter 5, Understanding the Theory and Application of Data Analysis
Financial Investigation and Forensic Accounting, G. A. Manning, Chapter 14, Accounting and Audit Techniques
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4
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LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data
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Data Mining Routines
Understanding the Integrity of the Data
Understanding the Norm of the Data
Entity Structures and Search Routines
Strategies for Data Mining
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The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 7, Data Mining for Fraud
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5
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LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data
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Disbursement Fraud
Payroll Fraud
Fraud Risk Structure
Data Analysis
Data Mining Planning
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The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 10, Disbursement Fraud
The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 12, Payroll Fraud
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6
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LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data
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Revenue Misstatement
Inventory Fraud
Fraud Risk Structure
Data Analysis
Data Mining Planning
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The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 13, Revenue Misstatement
The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 14, Inventory Fraud
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MID-SEMESTER BREAK
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7
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LO3: Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour
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Industry Data
Financial Analysis
Types of Fraud Revisited
Fraud Detection
Interpreting Potential Red Flags
Professional Scepticism
Fraud Triangle
Risk Factors
Information Gathering
Analytical Procedures and Techniques
Sampling Theory
Statistical Sampling Techniques
Non-statistical Sampling Techniques
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Financial Investigation and Forensic Accounting, G. A. Manning, Chapter 24, Audit Programs
A Guide to Forensic Accounting Investigation, W. Kenyon and P. D. Tilton, Chapter 13, Potential Red Flags and Fraud Detection Techniques
Statistical Techniques for Forensic Accounting, S. K. Dutta, Chapter 9, Sampling Theory and Techniques
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8
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LO3: Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour
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Probability
Schematic Representation of Evidence
Probative Value of Evidence
Constraints and Limitations of Data Analysis
Collection of Data
Data Analysis Tools
Descriptive Statistics
Models for Displaying Data
Data Analysis Software
Benford’s Law
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Statistical Techniques for Forensic Accounting, S. K. Dutta, Chapter 6, Transitioning to Evidence
Forensic Accounting, R. Rufus, L. Miller and W. Hahn, Chapter 8, Transforming Data into Evidence (Part 1)
Forensic Accounting, R. Rufus, L. Miller and W. Hahn, Chapter 9, Transforming Data into Evidence (Part 2)
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9
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LO4: Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures
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Critical Steps in Gathering Evidence
Chain of Custody
Evidence Created
Introduction to Digital Forensics
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A Guide to Forensic Accounting Investigation, W. Kenyon and P. D. Tilton, Chapter 10, Building a Case: Gathering and Documenting Evidence
Essentials of Forensic Accounting, M. A. Crain and others, Chapter 11, Digital Forensics
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10
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LO4: Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures
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Forensic Soundness
Forensic Analysis Fundamentals
Crime Reconstruction
Networks and the Internet
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Handbook of Digital Forensics and Investigation, E. Casey, Chapter 1, Introduction
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11
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LO5: Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations
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Scientific Method and Digital Forensics
Digital Forensic Analysis
Data Gathering and Observation
Conclusions and Reporting
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Handbook of Digital Forensics and Investigation, E. Casey, Chapter 2, Forensic Analysis
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12
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LO5: Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations
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Introduction to Electronic Discovery
Case Management
Identification of Electronic Data
Forensic Preservation of Data
Data Processing
Production of Electronic Data
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Handbook of Digital Forensics and Investigation, E. Casey, Chapter 3, Electronic Discovery
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13
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Revision
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