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)
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.
  • 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

 

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

 

 

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

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