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