FOR3705
Assignment 1
DUE: March 2026
,FOR3705 ASSESSMENT 01
ADVANCED FORENSIC CRIME INTELLIGENCE
SEMESTER 1, 2026
Student Name: [Your Name]
Student Number: [Your Student Number]
, 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]
Answer to Question 1
Data mining refers to the systematic process of discovering meaningful patterns,
relationships, and anomalies within large volumes of data through the application of
sophisticated computational techniques and statistical algorithms (Nigrini, 2020). In the
context of forensic crime intelligence, data mining represents a critical investigative
methodology that enables forensic accountants and investigators to analyse vast
datasets that would be impossible to examine manually, thereby uncovering hidden
evidence of fraudulent activities, financial crimes, and other illicit transactions (Golden,
Skalak & Clayton, 2017). The fundamental objective of data mining in forensic
investigations is to transform raw, unstructured data into actionable intelligence that can
support the detection, investigation, and prosecution of financial crimes.
Data mining tools employ various advanced techniques to identify patterns and
indicators of fraud within large datasets. One primary technique is anomaly detection,
which utilises statistical algorithms to identify transactions or data points that deviate
significantly from established norms or expected patterns (Wells, 2017). For instance, in
accounts payable systems, data mining tools can detect duplicate payments, unusual
vendor relationships, or payment amounts that fall just below approval thresholds, all of
which may indicate procurement fraud or kickback schemes (Singleton & Singleton,
2021). Another critical technique is clustering analysis, which groups similar
transactions or entities together, enabling investigators to identify suspicious networks
of related parties, shell companies, or coordinated fraudulent activities that might
otherwise remain concealed within vast transactional datasets (Kranacher, Riley &
Wells, 2019).
Data mining tools employ predictive modelling and pattern recognition algorithms to
identify indicators of fraud by comparing current data against historical fraud cases and
known fraud schemes (Nigrini, 2020). These tools can apply Benford's Law analysis to
Assignment 1
DUE: March 2026
,FOR3705 ASSESSMENT 01
ADVANCED FORENSIC CRIME INTELLIGENCE
SEMESTER 1, 2026
Student Name: [Your Name]
Student Number: [Your Student Number]
, 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]
Answer to Question 1
Data mining refers to the systematic process of discovering meaningful patterns,
relationships, and anomalies within large volumes of data through the application of
sophisticated computational techniques and statistical algorithms (Nigrini, 2020). In the
context of forensic crime intelligence, data mining represents a critical investigative
methodology that enables forensic accountants and investigators to analyse vast
datasets that would be impossible to examine manually, thereby uncovering hidden
evidence of fraudulent activities, financial crimes, and other illicit transactions (Golden,
Skalak & Clayton, 2017). The fundamental objective of data mining in forensic
investigations is to transform raw, unstructured data into actionable intelligence that can
support the detection, investigation, and prosecution of financial crimes.
Data mining tools employ various advanced techniques to identify patterns and
indicators of fraud within large datasets. One primary technique is anomaly detection,
which utilises statistical algorithms to identify transactions or data points that deviate
significantly from established norms or expected patterns (Wells, 2017). For instance, in
accounts payable systems, data mining tools can detect duplicate payments, unusual
vendor relationships, or payment amounts that fall just below approval thresholds, all of
which may indicate procurement fraud or kickback schemes (Singleton & Singleton,
2021). Another critical technique is clustering analysis, which groups similar
transactions or entities together, enabling investigators to identify suspicious networks
of related parties, shell companies, or coordinated fraudulent activities that might
otherwise remain concealed within vast transactional datasets (Kranacher, Riley &
Wells, 2019).
Data mining tools employ predictive modelling and pattern recognition algorithms to
identify indicators of fraud by comparing current data against historical fraud cases and
known fraud schemes (Nigrini, 2020). These tools can apply Benford's Law analysis to
