FOR3705
ASSIGNMENT 1
Academically sound.
Ethically crafted.
Distinction driven.”
The material provided is custom-written and
intended solely for study support and
academic guidance. It must not be
submitted as-is to any institution. All
documents are plagiarism-checked to
ensure originality. By purchasing, you agree
to use this material responsibly and in
accordance with your university’s code of
conduct.
, FOR3705 ASSIGNMENT 1 SEMESTER 1 2026
DUE DATE: MARCH 2026
ADVANCED FORENSIC CRIME INTELLIGENCE
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. (6 marks)
Meaning of Data Mining
Data mining refers to the systematic process of analysing large volumes of data to
identify hidden patterns, relationships, trends, and anomalies that are not easily
detectable through traditional data analysis methods. It combines statistical
techniques, machine learning algorithms, and database technology to transform raw
data into meaningful information that supports decision-making, risk management,
and control activities (Han, Kamber & Pei, 2012).
Use of Data Mining Tools to Identify Fraud
Data mining tools are used in fraud detection by examining large datasets to identify
unusual patterns and indicators that may suggest fraudulent behaviour.
Classification techniques compare current transactions with known fraud cases to
predict the likelihood of fraud. Clustering groups similar transactions and highlights
outliers that deviate from normal behaviour. Association rule mining identifies
suspicious relationships, such as repeated links between certain employees and
suppliers, while anomaly detection techniques flag transactions that fall outside
expected norms, including duplicate payments or abnormal transaction amounts.
These techniques enable organisations and auditors to proactively detect potential
fraud risks efficiently within large datasets (Singleton & Singleton, 2010; Wells,
2017).
Question 2
ASSIGNMENT 1
Academically sound.
Ethically crafted.
Distinction driven.”
The material provided is custom-written and
intended solely for study support and
academic guidance. It must not be
submitted as-is to any institution. All
documents are plagiarism-checked to
ensure originality. By purchasing, you agree
to use this material responsibly and in
accordance with your university’s code of
conduct.
, FOR3705 ASSIGNMENT 1 SEMESTER 1 2026
DUE DATE: MARCH 2026
ADVANCED FORENSIC CRIME INTELLIGENCE
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. (6 marks)
Meaning of Data Mining
Data mining refers to the systematic process of analysing large volumes of data to
identify hidden patterns, relationships, trends, and anomalies that are not easily
detectable through traditional data analysis methods. It combines statistical
techniques, machine learning algorithms, and database technology to transform raw
data into meaningful information that supports decision-making, risk management,
and control activities (Han, Kamber & Pei, 2012).
Use of Data Mining Tools to Identify Fraud
Data mining tools are used in fraud detection by examining large datasets to identify
unusual patterns and indicators that may suggest fraudulent behaviour.
Classification techniques compare current transactions with known fraud cases to
predict the likelihood of fraud. Clustering groups similar transactions and highlights
outliers that deviate from normal behaviour. Association rule mining identifies
suspicious relationships, such as repeated links between certain employees and
suppliers, while anomaly detection techniques flag transactions that fall outside
expected norms, including duplicate payments or abnormal transaction amounts.
These techniques enable organisations and auditors to proactively detect potential
fraud risks efficiently within large datasets (Singleton & Singleton, 2010; Wells,
2017).
Question 2
