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FOR3705 Assignment 1 (100% COMPLETE ANSWERS) Semester 1 2025 - DUE 29 March 2025

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Advanced Forensic Crime Intelligence - FOR3705 Assignment 1 Semester 1 2025 - DUE 29 March 2025 ;100 % TRUSTED workings, Expert Solved, Explanations and Solutions. For assistance call or W.h.a.t.s.a.p.p us on ...(.+.2.5.4.7.7.9.5.4.0.1.3.2)........... QUESTION 1 1.1 Analyse the relationship between data mining and data analysis in financial crime investigation. (5) 1.2 Assess how these two processes complement each other in detecting financial crimes. (5) [10] QUESTION 2 2.1 Compare public and non-public sources of information in financial crime investigation. (4) 2.2 Explore three relevant examples of each type of source and argue their significance in financial crime investigation. (6) [10] QUESTION 3 3.1 Evaluate five key advantages of using data analysis software in financial crime investigation. (5) 3.2 Support your answer by generating one practical example for each key advantage. (5) [10] QUESTION 4 Distinguish the four main phases of the data analysis process and interpret their importance in ensuring accurate financial crime investigation results. [8] QUESTION 5 Demonstrate how undercover operations and surveillance operations can be effectively utilised in financial crime investigation. [6] ADVANCED FORENSIC CRIME INTELLIGENCE FOR3705 ASSESSMENT 01 SEMESTER 1 2025 QUESTION 6 6.1 Compare the net-worth method and the expenditures method as indirect approaches to proving a subject's sources of income. (3) 6.2 Explain when each method would be most appropriate to use. (3) [6]

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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

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