Students
ACCG8076 – Forensic and Data Analytics
2023 – Session 2, In person-scheduled-weekday, North Ryde
General Information
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| Unit convenor and teaching staff |
Unit convenor and teaching staff
Nuraddeen Nuhu
Batul Towfique Hasan
|
|---|---|
| Credit points |
Credit points
10
|
| Prerequisites |
Prerequisites
ACCG6011 or ACCG611 or (admission to MActPrac or MBkgFin or MBusAnalytics or GradCertForAccg or GradDipForAccg or MForAccgFinCri)
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| Corequisites |
Corequisites
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| Co-badged status |
Co-badged status
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| Unit description |
Unit description
In this unit students will be exposed to the theory and application of data analytics skills and techniques in relation to fraud detection and identifying business risks. The unit will introduce students to mechanisms and principles relevant to tracing assets, investigating flow of funds and reconstructing accounting information. Visual and location analytic capabilities that use a variety of tools and techniques, along with external data sets, will be explored. The unit will also equip students with the capacity to appraise applications and strategies to enable collection, assessment, review, production and presentation of unstructured data. |
Important Academic Dates
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
Learning Outcomes
On successful completion of this unit, you will be able to:
- ULO1: Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting.
- ULO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.
- ULO3: Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour.
- ULO4: Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures.
- ULO5: Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations.
Assessment Tasks
| Name | Weighting | Hurdle | Due |
|---|---|---|---|
| Critical Essay | 40% | No | Week 13 |
| Class Test | 20% | No | Week 8 |
| Class Presentation | 20% | No | Week 7 |
| Participation | 20% | No | Week 3, Week 4, Week 6, Week 10 |
Critical Essay
Assessment Type 1: Essay
Indicative Time on Task 2: 34 hours
Due: Week 13
Weighting: 40%
In this assessment students will be required to critically reflect on the key issues and principles of professional digital forensic practice in the recovery of digital evidence to support an investigation. The submission should not exceed 2500 words.
On successful completion you will be able to:
- Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures.
- Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations.
Class Test
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 18 hours
Due: Week 8
Weighting: 20%
The class test may include one, or a combination of, the following types of assessment: multiple-choice questions, true/false questions, short answer style questions, problem scenario or evidence- based questions.
On successful completion you will be able to:
- Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting.
- Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.
- Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour.
- Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures.
- Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations.
Class Presentation
Assessment Type 1: Presentation
Indicative Time on Task 2: 18 hours
Due: Week 7
Weighting: 20%
In this assessment students will deliver a 10-minute presentation that requires a consolidation of the theory, and application of data analytics skills and techniques to enable the assessment, review, and presentation of unstructured data relevant to advance a forensic accounting investigation. A summary report will be required to accompany the presentation.
On successful completion you will be able to:
- Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting.
- Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.
- Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour.
Participation
Assessment Type 1: Participatory task
Indicative Time on Task 2: 20 hours
Due: Week 3, Week 4, Week 6, Week 10
Weighting: 20%
This assessment involves evidence of preparation for, participation in, and contribution to seminars and online discussion forums.
On successful completion you will be able to:
- Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting.
- Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.
- Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour.
- Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures.
- Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations.
1 If you need help with your assignment, please contact:
- the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
- the Writing Centre for academic skills support.
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
Unit Schedule
|
Week |
Learning Objective |
Content |
Reading |
|
1 |
LO1: Evaluate the theory & principles of application of data analytics skills & techniques relevant to forensic accounting
|
Introduction to Fraud
Types of Fraud
Fraud Theories
Fraud Detection/Internal Control
Interpreting Potential Red Flags
Professional Scepticism
Risk Factors
|
Fraud theories & White-collar crimes https://researchleap.com/fraud-theories-white-collar-crimes-lessons-nigerian-banking-industry/
Forensic Accounting and Fraud Investigation for Non-Experts, H. Silverstone and M. Sheetz, Chapter 2, Fraud in Society
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
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|
2 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data
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
|
Introduction to Financial Analysis
Key Ratios
Industry Data
Information Gathering
|
Forensic Accounting and Fraud Investigation for Non-Experts, H. Silverstone and M. Sheetz, Chapter 5 Fundamental Principles of Financial Analysis
A Guide to Forensic Accounting Investigation, W. Kenyon and P. D. Tilton, Chapter 10, Building a Case: Gathering and Documenting Evidence
|
|
3 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data
|
Critical Steps in Gathering Evidence
Chain of Custody
Evidence Created
|
A Guide to Forensic Accounting Investigation, W. Kenyon and P. D. Tilton, Chapter 10, Building a Case: Gathering and Documenting Evidence (continued)
Additional reading materials provided |
|
4 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data
LO4: Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures |
Data Mining Routines
Understanding the Integrity of the Data
Understanding the Norm of the Data Entity
Structures and Search Routines
Strategies for Data Mining
|
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 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data |
Revenue Misstatement
Inventory fraud
Fraud risk structure
Data analysis
Data mining planning
|
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 |
|
6 |
LO1: Evaluate the theory & principles of application of data analytics skills & techniques relevant to forensic accounting |
The Need for Analysis Tools
Matrices
Link Diagrams
Social Network Analysis
Analysing Networks
Data Mining as an Analysis Tool
|
Forensic Accounting and Fraud Investigation for Non-Experts, H. Silverstone and M. Sheetz, Chapter 12, Analysis Tools for Investigators |
|
7 |
LO1: Evaluate the theory & principles of application of data analytics skills & techniques relevant to forensic accounting |
Introduction to Data Mining
Data Classification
Association Analysis
Cluster Analysis
Outlier Analysis
Application: Data Mining to Detect Money Laundering
Tracing |
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 |
|
MID SEMESTER BREAK |
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8 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data |
Class Test
Payroll Fraud
Fraud Risk Structure
Data Analysis
|
|
|
9 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data |
Disbursement Fraud
Fraud Risk Structure
Data Analysis
Data mining planning |
The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 12, Payroll Fraud
The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 10, Disbursement Fraud |
|
10 |
LO4: Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures |
Introduction to Digital Forensics
Forensic Soundness
Forensic Analysis Fundamentals
Crime reconstruction
Networks & the Internet |
Essentials of Forensic Accounting, M. A. Crain and others, Chapter 11, Digital Forensics
Handbook of Digital Forensics and Investigation, E. Casey, Chapter 1, Introduction |
|
11 |
LO5: Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations |
Scientific Method and Digital Forensics
Digital Forensic Analysis
Data Gathering And Observation
Conclusions and Reporting |
Handbook of Digital Forensics and Investigation, E. Casey, Chapter 2, Forensic Analysis |
|
12 |
LO5: Diagnose and appraise mechanisms to uncover or recover evidence from digital devices to support litigation and investigations |
Introduction to electronic discovery
Case management
Identification of electronic data Forensic Preservation of data
Data Processing
Production of Electronic Data |
Handbook of Digital Forensics and Investigation, E. Casey, Chapter 3, Electronic Discovery |
|
13 |
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
|
Analytical Procedures And Techniques
Sampling Theory
Statistical Sampling
Techniques
Non-statistical Sampling Techniques
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 |
Statistical Techniques for Forensic Accounting, S. K. Dutta, Chapter 9, Sampling Theory and Techniques
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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Unit information based on version 2023.02 of the Handbook