,FOR3705 Assignment 1 (COMPLETE ANSWERS) Semester 1 2026
- DUE March 2026;100% trusted ,comprehensive and complete
reliable solution with clear explanation
Questions and correct answers
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]
Answer
Data mining is the process of extracting useful information, patterns,
and knowledge from large volumes of data using statistical,
mathematical, and computational techniques. It involves analysing
datasets to discover relationships, trends, or anomalies that may not be
immediately apparent. Data mining is widely used in forensic crime
intelligence to detect unusual activity, predict risks, and support
decision-making.
Data mining tools are essential in forensic investigations because they
allow investigators to process and analyse extremely large datasets
quickly and efficiently, uncovering patterns and irregularities that may
not be visible through manual examination. These tools can identify
anomalies, suspicious transactions, or inconsistencies across
financial, operational, or transactional data, which may indicate
fraudulent activity. Common techniques used in data mining include
clustering, which groups similar data points to detect unusual clusters of
, activity; classification, which categorises data based on predefined
criteria; regression analysis, which examines relationships between
variables to predict potential fraud; and association rule mining, which
identifies correlations between different transactions or behaviours that
may suggest coordinated fraudulent schemes.
For instance, data mining tools can flag duplicate payments, abnormal
expenditure spikes, irregular account activity, or deviations from
established patterns of behaviour. By identifying these red flags,
investigators can prioritise their efforts, focusing on high-risk
transactions or accounts that warrant further examination. Additionally,
data visualisation features in these tools allow complex datasets to be
presented in charts, graphs, or dashboards, making it easier to detect
trends, correlations, and anomalies at a glance.
Ultimately, data mining not only helps investigators detect fraud after
it has occurred, but also enables organisations to implement preventive
measures. By recognising patterns of fraudulent behaviour early,
forensic teams can strengthen internal controls, improve monitoring
systems, and reduce the overall risk of financial loss. This proactive
approach enhances both the efficiency and effectiveness of forensic
investigations, making data mining a critical component of modern fraud
detection and crime intelligence.
Question 2.1
Identify four significant advantages of using data analysis software in
fraud investigations that may assist investigators in obtaining evidence
that fraud has been committed. [4marks]
- DUE March 2026;100% trusted ,comprehensive and complete
reliable solution with clear explanation
Questions and correct answers
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]
Answer
Data mining is the process of extracting useful information, patterns,
and knowledge from large volumes of data using statistical,
mathematical, and computational techniques. It involves analysing
datasets to discover relationships, trends, or anomalies that may not be
immediately apparent. Data mining is widely used in forensic crime
intelligence to detect unusual activity, predict risks, and support
decision-making.
Data mining tools are essential in forensic investigations because they
allow investigators to process and analyse extremely large datasets
quickly and efficiently, uncovering patterns and irregularities that may
not be visible through manual examination. These tools can identify
anomalies, suspicious transactions, or inconsistencies across
financial, operational, or transactional data, which may indicate
fraudulent activity. Common techniques used in data mining include
clustering, which groups similar data points to detect unusual clusters of
, activity; classification, which categorises data based on predefined
criteria; regression analysis, which examines relationships between
variables to predict potential fraud; and association rule mining, which
identifies correlations between different transactions or behaviours that
may suggest coordinated fraudulent schemes.
For instance, data mining tools can flag duplicate payments, abnormal
expenditure spikes, irregular account activity, or deviations from
established patterns of behaviour. By identifying these red flags,
investigators can prioritise their efforts, focusing on high-risk
transactions or accounts that warrant further examination. Additionally,
data visualisation features in these tools allow complex datasets to be
presented in charts, graphs, or dashboards, making it easier to detect
trends, correlations, and anomalies at a glance.
Ultimately, data mining not only helps investigators detect fraud after
it has occurred, but also enables organisations to implement preventive
measures. By recognising patterns of fraudulent behaviour early,
forensic teams can strengthen internal controls, improve monitoring
systems, and reduce the overall risk of financial loss. This proactive
approach enhances both the efficiency and effectiveness of forensic
investigations, making data mining a critical component of modern fraud
detection and crime intelligence.
Question 2.1
Identify four significant advantages of using data analysis software in
fraud investigations that may assist investigators in obtaining evidence
that fraud has been committed. [4marks]
