, ADVANCED FORENSIC CRIME INTELLIGENCE (FOR3705)
ASSESSMENT 01 – SEMESTER 1 (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.
[6 marks]
Data mining refers to the process of extracting meaningful
patterns, relationships, trends, and anomalies from large datasets
using statistical techniques, algorithms, and specialised software.
It involves analysing vast amounts of structured or unstructured
data to uncover hidden information that may not be visible
through manual review.
In fraud investigations, data mining tools are used to:
1. Identify unusual patterns – For example, duplicate
payments, irregular transaction amounts, or abnormal timing
of transactions.
2. Detect anomalies – Transactions that fall outside normal
behavioural patterns (e.g., sudden large withdrawals).
3. Link analysis – Identify relationships between individuals,
accounts, companies, or transactions that suggest collusion.
4. Trend analysis – Monitor changes over time, such as
escalating expense claims.
5. Benford’s Law analysis – Detect fabricated numbers in
financial records.
ASSESSMENT 01 – SEMESTER 1 (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.
[6 marks]
Data mining refers to the process of extracting meaningful
patterns, relationships, trends, and anomalies from large datasets
using statistical techniques, algorithms, and specialised software.
It involves analysing vast amounts of structured or unstructured
data to uncover hidden information that may not be visible
through manual review.
In fraud investigations, data mining tools are used to:
1. Identify unusual patterns – For example, duplicate
payments, irregular transaction amounts, or abnormal timing
of transactions.
2. Detect anomalies – Transactions that fall outside normal
behavioural patterns (e.g., sudden large withdrawals).
3. Link analysis – Identify relationships between individuals,
accounts, companies, or transactions that suggest collusion.
4. Trend analysis – Monitor changes over time, such as
escalating expense claims.
5. Benford’s Law analysis – Detect fabricated numbers in
financial records.
