FOR3705
ASSIGNMENT 1
DUE DATE: MARCH 2026
, FOR3705 ASSIGNMENT 1 2026
MEMO SEMESTER 1 2026
DUE MARCH 2026
QUESTION 1
Explain what is meant by ‘data mining’ and discuss how data mining tools are
used to identify patterns and indicators of fraud in large datasets.
Data mining refers to the systematic process of searching and analysing large volumes
of data to discover hidden patterns, trends, relationships, or anomalies that may not be
immediately apparent (Learning Unit 2). In the context of forensic and fraud
investigations, data mining is used to identify irregular activities that could indicate
fraudulent behaviour. According to the Fraud Examiners Manual (2016), data mining is
the science of extracting meaningful patterns from extensive datasets, particularly
where manual analysis would be impractical due to the size and complexity of the data
(Fraud Examiners Manual, 2016: 3.701–3.705).
Data mining tools assist fraud investigators by automatically scanning large electronic
datasets such as financial transactions, vendor records, payroll data, and customer
databases. These tools use algorithms and analytical techniques to detect unusual
patterns, such as duplicate payments, abnormal transaction amounts, inconsistent
vendor details, or transactions occurring outside normal business hours. For example,
techniques such as Benford’s Law analysis can be applied to identify irregular number
distributions that may suggest data manipulation or fabricated transactions (Learning
Unit 2; Fraud Examiners Manual, 2016: 3.720–3.725).
ASSIGNMENT 1
DUE DATE: MARCH 2026
, FOR3705 ASSIGNMENT 1 2026
MEMO SEMESTER 1 2026
DUE MARCH 2026
QUESTION 1
Explain what is meant by ‘data mining’ and discuss how data mining tools are
used to identify patterns and indicators of fraud in large datasets.
Data mining refers to the systematic process of searching and analysing large volumes
of data to discover hidden patterns, trends, relationships, or anomalies that may not be
immediately apparent (Learning Unit 2). In the context of forensic and fraud
investigations, data mining is used to identify irregular activities that could indicate
fraudulent behaviour. According to the Fraud Examiners Manual (2016), data mining is
the science of extracting meaningful patterns from extensive datasets, particularly
where manual analysis would be impractical due to the size and complexity of the data
(Fraud Examiners Manual, 2016: 3.701–3.705).
Data mining tools assist fraud investigators by automatically scanning large electronic
datasets such as financial transactions, vendor records, payroll data, and customer
databases. These tools use algorithms and analytical techniques to detect unusual
patterns, such as duplicate payments, abnormal transaction amounts, inconsistent
vendor details, or transactions occurring outside normal business hours. For example,
techniques such as Benford’s Law analysis can be applied to identify irregular number
distributions that may suggest data manipulation or fabricated transactions (Learning
Unit 2; Fraud Examiners Manual, 2016: 3.720–3.725).
