FOR3705
ASSIGNMENT 1 SEMESTER 1 2026
UNIQUE NO.
DUE DATE: MARCH 2026
, Advanced Forensic Crime Intelligence - FOR3705
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]
Data mining refers to the process of examining large volumes of data to discover hidden
patterns, relationships, trends, and anomalies that are not immediately apparent
through traditional analysis methods. In the context of forensic crime intelligence, data
mining involves using advanced analytical techniques and software tools to extract
meaningful information from structured and unstructured datasets for investigative
purposes.
Data mining tools are used in fraud detection to identify unusual or suspicious patterns
that may indicate fraudulent activity. These tools analyse transactional data to detect
anomalies such as duplicate payments, irregular transaction timing, unusually high
transaction values, or deviations from normal behaviour. They can also apply pattern
recognition, clustering, and predictive modelling to identify relationships between
individuals, accounts, or entities that may suggest collusion or organised fraud. By
automating the analysis of vast datasets, data mining tools enable investigators to
efficiently detect red flags, prioritise high-risk cases, and uncover fraud schemes that
might otherwise remain concealed.
ASSIGNMENT 1 SEMESTER 1 2026
UNIQUE NO.
DUE DATE: MARCH 2026
, Advanced Forensic Crime Intelligence - FOR3705
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]
Data mining refers to the process of examining large volumes of data to discover hidden
patterns, relationships, trends, and anomalies that are not immediately apparent
through traditional analysis methods. In the context of forensic crime intelligence, data
mining involves using advanced analytical techniques and software tools to extract
meaningful information from structured and unstructured datasets for investigative
purposes.
Data mining tools are used in fraud detection to identify unusual or suspicious patterns
that may indicate fraudulent activity. These tools analyse transactional data to detect
anomalies such as duplicate payments, irregular transaction timing, unusually high
transaction values, or deviations from normal behaviour. They can also apply pattern
recognition, clustering, and predictive modelling to identify relationships between
individuals, accounts, or entities that may suggest collusion or organised fraud. By
automating the analysis of vast datasets, data mining tools enable investigators to
efficiently detect red flags, prioritise high-risk cases, and uncover fraud schemes that
might otherwise remain concealed.
