Unit convenor and teaching staff |
Unit convenor and teaching staff
Convenor
Vladimir Chugunov
Contact via Email
Moderator
Rahat Munir
Cissy Zhan
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Credit points |
Credit points
10
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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. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
To complete this unit satisfactorily, students must attempt all components of the assessments and obtain a minimum aggregate grade of 50%.
LATE SUBMISSIONS OF ASSESSMENTS
Unless a Special Consideration request has been submitted and approved, no extensions will be granted. There will be a deduction of 10% of the total available assessment-task marks made from the total awarded mark for each 24-hour period or part thereof that the submission is late. Late submissions will only be accepted up to 96 hours after the due date and time.
No late submissions will be accepted for timed assessments – e.g., quizzes, online tests.
SPECIAL CONSIDERATION
To request an extension on the due date/time for a timed or non-timed assessment task, you must submit a Special Consideration application.
An application for Special Consideration does not guarantee approval.
The approved extension date for a student becomes the new due date for that student. The late submission penalties above then apply as of the new due date.
Name | Weighting | Hurdle | Due |
---|---|---|---|
Participation | 20% | No | Weekly |
Class Presentation | 20% | No | Week 7, Sunday, 11.59 PM |
Class Test | 20% | No | Week 8, Saturday, 11 AM |
Critical Essay | 40% | No | Week 12, Sunday, 11.59 PM |
Assessment Type 1: Participatory task
Indicative Time on Task 2: 20 hours
Due: Weekly
Weighting: 20%
This assessment involves evidence of preparation for, participation in, and contribution to seminars and online discussion forums.
Assessment Type 1: Presentation
Indicative Time on Task 2: 18 hours
Due: Week 7, Sunday, 11.59 PM
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.
Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 18 hours
Due: Week 8, Saturday, 11 AM
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.
Assessment Type 1: Essay
Indicative Time on Task 2: 34 hours
Due: Week 12, Sunday, 11.59 PM
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.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
This unit is run in intensive mode. Please review the actual timetable for classes at www.timetables.mq.edu.au.
Details of assessments and online forums will be available on iLearn.
TECHNOLOGY USED AND REQUIRED
Students are expected to be proficient in MS Word, Excel, and PowerPoint. Knowledge of Macquarie University iLearn - for downloading lecture materials, etc. Knowledge of the library.
WEEK |
LEARNING OBJECTIVE |
CONTENT |
READINGS |
1 |
LO1: Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting |
Introduction to Fraud Types of Fraud The Need for Analysis Tools Matrices Link Diagrams Social Network Analysis Analysing Networks |
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 |
LO1: Evaluate the theory, and principles of application, of data analytics skills and techniques relevant to forensic accounting |
Introduction to Financial Analysis Key Ratios Data Mining as an Analysis Tool |
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 |
LO1: Evaluate the theory, and principles of application, of data analytics skills and 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 |
4 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data |
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 |
5 |
LO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data |
Disbursement Fraud Payroll 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 10, Disbursement Fraud
The Fraud Audit: Responding to the Risk of Fraud in Core Business Systems, L. W. Vona, Chapter 12, Payroll Fraud |
6 |
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 |
7 |
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 |
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 |
8 |
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 |
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 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) |
9 |
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 |
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 |
10 |
LO4: Examine issues and key principles of professional digital forensic practice, including chain of custody and best practice procedures |
Forensic Soundness Forensic Analysis Fundamentals Crime Reconstruction Networks and the Internet |
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
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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
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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 |
Revision |
Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:
Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
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At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.
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Got a question? Ask us via AskMQ, or contact Service Connect.
For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/.
When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.
Unit information based on version 2022.02 of the Handbook