Students

ACCG8076 – Forensic and Data Analytics

2021 – Session 1, Special circumstances

Notice

As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.

To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Kathleen Clough
Credit points Credit points
10
Prerequisites Prerequisites
ACCG6011 or ACCG611 or (admission to MActPrac or MBkgFin or MBusAnalytics or GradCertForAccg or GradDipForAccg or MForAccgFinCri)
Corequisites Corequisites
Co-badged status Co-badged status
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.
  • 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.
  • ULO2: Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.
  • 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.

General Assessment Information

Late Submission(s) of Assessment: Where assessment is to be submitted through Turnitin, late assessment must also, where applicable, be submitted through Turnitin. No extensions will be granted. There will be a deduction of 10% of the total available marks made from the total awarded mark for each 24 hour period or part thereof that the submission is late (for example, 25 hours late in submission incurs a 20% penalty). Late submissions will not be accepted after solutions have been discussed and/or made available. This penalty does not apply for cases in which an application for Special Consideration is made and approved. Note: applications for Special Consideration Policy must be made within 5 (five) business days of the due date and time.

Assessment Tasks

Name Weighting Hurdle Due
Participation 20% No Weekly, 11.59PM Sunday
Written Presentation 20% No Wednesday, 31st March 2021 (2PM)
Online Test 20% No Saturday, 1st May 2021 (10AM)
Critical Essay 40% No Wednesday, 26th May 2021 (2PM)

Participation

Assessment Type 1: Participatory task
Indicative Time on Task 2: 20 hours
Due: Weekly, 11.59PM Sunday
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.
  • Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour.
  • Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.
  • 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.

Written Presentation

Assessment Type 1: Report
Indicative Time on Task 2: 18 hours
Due: Wednesday, 31st March 2021 (2PM)
Weighting: 20%

 

In this assessment students will submit a written 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.
  • Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour.
  • Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.

Online Test

Assessment Type 1: Quiz/Test
Indicative Time on Task 2: 18 hours
Due: Saturday, 1st May 2021 (10AM)
Weighting: 20%

 

The Online 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.
  • Manage and interpret complex or disparate sets of data to underpin business development, interpret risk, understand behavioural patterns, and detect suspicious or irregular behaviour.
  • Investigate applications and strategies, including data mining, to enable collection, assessment, review, production and presentation of unstructured data.
  • 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.

Critical Essay

Assessment Type 1: Essay
Indicative Time on Task 2: 34 hours
Due: Wednesday, 26th May 2021 (2PM)
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.

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

Delivery and Resources

This is an online unit. Details of assessments and online forums will be available on iLearn.

Unit Schedule

Unit Schedule

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

 

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

 

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

MID-SEMESTER BREAK

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

 

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

 

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

 

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

Revision

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Results

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Student Support

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