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FOR3705 Assignment 1 Semester 1 2026 (Answer Guide) - DUE March
2026
VERIFIED AND CERTIFIED ANSWERS. WRITTEN IN REQUIRED FORMAT AND WITHIN
GIVEN GUIDELINES. IT IS GOOD TO USE AS A GUIDE AND FOR REFERENCE, NEVER
PLAGARIZE. Thank you and success in your academics.
UNISA, 2025
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]
Meaning of Data Mining
Data mining refers to the systematic process of discovering hidden patterns,
relationships, anomalies, and trends within large volumes of data using statistical,
mathematical, and computational techniques. It involves extracting meaningful and
actionable information from raw data that would otherwise be too complex or
voluminous to analyse manually. In the context of forensic and fraud investigations, data
mining is used to transform vast transactional datasets into intelligence that supports
the detection, prevention, and investigation of fraudulent activities (Han, Kamber & Pei,
2012). Data mining forms a core component of knowledge discovery in databases
(KDD), where data is cleaned, integrated, analysed, and interpreted to support decision-
making.
Use of Data Mining Tools in Identifying Fraud Patterns and Indicators
Data mining tools play a critical role in fraud detection by enabling investigators to
analyse large datasets efficiently and systematically. One key application is pattern
recognition, where tools identify unusual transaction behaviours, such as repeated
payments just below approval thresholds, abnormal transaction frequencies, or irregular
, 2
timing of transactions. These patterns often indicate attempts to circumvent internal
controls (Wells, 2017).
Another important use is anomaly detection, where data mining tools flag outliers that
deviate significantly from normal behaviour. For example, a sudden increase in expense
claims, unexplained changes in supplier payment trends, or transactions occurring
outside normal business hours may indicate potential fraud. These anomalies help
investigators focus on high-risk transactions rather than reviewing all data manually
(Bolton & Hand, 2002).
Data mining tools also support link and association analysis, which identifies
relationships between entities such as employees, vendors, bank accounts, and
customers. This is particularly useful in uncovering collusion, shell companies, or
conflicts of interest by revealing hidden connections that are not immediately visible
through traditional analysis methods.
Additionally, predictive modelling and classification techniques are used to
compare current transactions against known fraud profiles. By learning from historical
fraud cases, data mining tools can assign risk scores to transactions, enabling proactive
identification of potentially fraudulent activities. This enhances investigative efficiency
and strengthens the evidential basis of fraud investigations (Chen, Chiang & Storey,
2012).
Overall, data mining tools enable investigators to detect fraud indicators more
accurately, reduce investigative time, and improve the reliability of findings when
dealing with large and complex datasets.
QUESTION 2
2.1 Identify four significant advantages of using data analysis software in fraud
investigations.
[4]
FOR3705 Assignment 1 Semester 1 2026 (Answer Guide) - DUE March
2026
VERIFIED AND CERTIFIED ANSWERS. WRITTEN IN REQUIRED FORMAT AND WITHIN
GIVEN GUIDELINES. IT IS GOOD TO USE AS A GUIDE AND FOR REFERENCE, NEVER
PLAGARIZE. Thank you and success in your academics.
UNISA, 2025
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]
Meaning of Data Mining
Data mining refers to the systematic process of discovering hidden patterns,
relationships, anomalies, and trends within large volumes of data using statistical,
mathematical, and computational techniques. It involves extracting meaningful and
actionable information from raw data that would otherwise be too complex or
voluminous to analyse manually. In the context of forensic and fraud investigations, data
mining is used to transform vast transactional datasets into intelligence that supports
the detection, prevention, and investigation of fraudulent activities (Han, Kamber & Pei,
2012). Data mining forms a core component of knowledge discovery in databases
(KDD), where data is cleaned, integrated, analysed, and interpreted to support decision-
making.
Use of Data Mining Tools in Identifying Fraud Patterns and Indicators
Data mining tools play a critical role in fraud detection by enabling investigators to
analyse large datasets efficiently and systematically. One key application is pattern
recognition, where tools identify unusual transaction behaviours, such as repeated
payments just below approval thresholds, abnormal transaction frequencies, or irregular
, 2
timing of transactions. These patterns often indicate attempts to circumvent internal
controls (Wells, 2017).
Another important use is anomaly detection, where data mining tools flag outliers that
deviate significantly from normal behaviour. For example, a sudden increase in expense
claims, unexplained changes in supplier payment trends, or transactions occurring
outside normal business hours may indicate potential fraud. These anomalies help
investigators focus on high-risk transactions rather than reviewing all data manually
(Bolton & Hand, 2002).
Data mining tools also support link and association analysis, which identifies
relationships between entities such as employees, vendors, bank accounts, and
customers. This is particularly useful in uncovering collusion, shell companies, or
conflicts of interest by revealing hidden connections that are not immediately visible
through traditional analysis methods.
Additionally, predictive modelling and classification techniques are used to
compare current transactions against known fraud profiles. By learning from historical
fraud cases, data mining tools can assign risk scores to transactions, enabling proactive
identification of potentially fraudulent activities. This enhances investigative efficiency
and strengthens the evidential basis of fraud investigations (Chen, Chiang & Storey,
2012).
Overall, data mining tools enable investigators to detect fraud indicators more
accurately, reduce investigative time, and improve the reliability of findings when
dealing with large and complex datasets.
QUESTION 2
2.1 Identify four significant advantages of using data analysis software in fraud
investigations.
[4]
