FOR3705
ASSIGNMENT 1 SEMESTER 1 2025
UNIQUE NO.
DUE DATE: 29 MARCH 2025
, FOR3705
Assignment 1 Semester 1 2025
Unique Number:
Due Date: 29 March 2025
Advanced Forensic Crime Intelligence
QUESTION 1
1.1 Relationship Between Data Mining and Data Analysis in Financial Crime
Investigation (5 Marks)
Data mining and data analysis are closely related but distinct processes used in
financial crime investigations. Data mining involves extracting hidden patterns and
relationships from large datasets using machine learning, statistical techniques, and
algorithms (Han, Pei, & Kamber, 2011). It helps in identifying anomalies and potential
fraud patterns. On the other hand, data analysis is the process of inspecting, cleaning,
transforming, and modeling data to draw conclusions and support decision-making
(Provost & Fawcett, 2013). While data mining focuses on discovering new insights, data
analysis interprets these findings to support investigative actions.
1.2 Complementary Role of Data Mining and Data Analysis in Detecting Financial
Crimes (5 Marks)
Data mining and data analysis complement each other in financial crime investigations
by enhancing pattern recognition and decision-making. Data mining identifies
suspicious transactions and irregularities, while data analysis validates these findings
and assesses their implications. Together, they improve fraud detection accuracy,
reduce false positives, and provide actionable intelligence for investigators (Westphal,
2016). For example, in money laundering cases, data mining can highlight unusual
ASSIGNMENT 1 SEMESTER 1 2025
UNIQUE NO.
DUE DATE: 29 MARCH 2025
, FOR3705
Assignment 1 Semester 1 2025
Unique Number:
Due Date: 29 March 2025
Advanced Forensic Crime Intelligence
QUESTION 1
1.1 Relationship Between Data Mining and Data Analysis in Financial Crime
Investigation (5 Marks)
Data mining and data analysis are closely related but distinct processes used in
financial crime investigations. Data mining involves extracting hidden patterns and
relationships from large datasets using machine learning, statistical techniques, and
algorithms (Han, Pei, & Kamber, 2011). It helps in identifying anomalies and potential
fraud patterns. On the other hand, data analysis is the process of inspecting, cleaning,
transforming, and modeling data to draw conclusions and support decision-making
(Provost & Fawcett, 2013). While data mining focuses on discovering new insights, data
analysis interprets these findings to support investigative actions.
1.2 Complementary Role of Data Mining and Data Analysis in Detecting Financial
Crimes (5 Marks)
Data mining and data analysis complement each other in financial crime investigations
by enhancing pattern recognition and decision-making. Data mining identifies
suspicious transactions and irregularities, while data analysis validates these findings
and assesses their implications. Together, they improve fraud detection accuracy,
reduce false positives, and provide actionable intelligence for investigators (Westphal,
2016). For example, in money laundering cases, data mining can highlight unusual
