,FOR3705 Assignment 1 Semester 1 | Due 29 March
2025. All questions answered.
Question 1
1.1. Analyse the relationship between data mining and data
analysis in financial crime
investigation. 1.2. Assess how these two processes complement
each other in detecting financial crimes. Question 2
2.1. Compare public and non-public sources of information in
financial crime investigation. 2.2. Explore three relevant
examples of each type of source and argue their significance in
financial crime investigation.
Question 3
3.1. Evaluate five key advantages of using data analysis software
in financial crime investigation.
3.2. Support your answer by generating one practical example
for each key advantage.
Question 4
4. Distinguish the four main phases of the data analysis process
and interpret their importance
in ensuring accurate financial crime investigation results.
Question 5
5. Demonstrate how undercover operations and surveillance
operations can be effectively
utilised in financial crime investigation.
Question 6
, 6.1. Compare the net-worth method and the expenditures
method as indirect approaches to
proving a subject’s sources of income.
6.2. Explain when each method would be most appropriate to
use.
Question 1
1.1. Relationship between Data Mining and Data Analysis in
Financial Crime Investigation Data mining and data analysis are
interrelated processes used to detect financial crimes. Data
mining involves extracting large sets of structured and
unstructured data to uncover patterns, anomalies, and
relationships that may indicate fraudulent activities. It employs
machine learning, statistical algorithms, and artificial
intelligence to detect suspicious transactions. On the other
hand, data analysis interprets and evaluates the mined data to
derive meaningful insights. It involves applying statistical
techniques, visualization tools, and forensic accounting to
validate and corroborate findings from data mining.
1.2. How Data Mining and Data Analysis Complement Each
Other Data mining identifies potential fraudulent activities by
detecting hidden correlations, trends, and outliers, while data
analysis validates and refines these findings. Together, they
help:
Improve the accuracy of fraud detection models.
2025. All questions answered.
Question 1
1.1. Analyse the relationship between data mining and data
analysis in financial crime
investigation. 1.2. Assess how these two processes complement
each other in detecting financial crimes. Question 2
2.1. Compare public and non-public sources of information in
financial crime investigation. 2.2. Explore three relevant
examples of each type of source and argue their significance in
financial crime investigation.
Question 3
3.1. Evaluate five key advantages of using data analysis software
in financial crime investigation.
3.2. Support your answer by generating one practical example
for each key advantage.
Question 4
4. Distinguish the four main phases of the data analysis process
and interpret their importance
in ensuring accurate financial crime investigation results.
Question 5
5. Demonstrate how undercover operations and surveillance
operations can be effectively
utilised in financial crime investigation.
Question 6
, 6.1. Compare the net-worth method and the expenditures
method as indirect approaches to
proving a subject’s sources of income.
6.2. Explain when each method would be most appropriate to
use.
Question 1
1.1. Relationship between Data Mining and Data Analysis in
Financial Crime Investigation Data mining and data analysis are
interrelated processes used to detect financial crimes. Data
mining involves extracting large sets of structured and
unstructured data to uncover patterns, anomalies, and
relationships that may indicate fraudulent activities. It employs
machine learning, statistical algorithms, and artificial
intelligence to detect suspicious transactions. On the other
hand, data analysis interprets and evaluates the mined data to
derive meaningful insights. It involves applying statistical
techniques, visualization tools, and forensic accounting to
validate and corroborate findings from data mining.
1.2. How Data Mining and Data Analysis Complement Each
Other Data mining identifies potential fraudulent activities by
detecting hidden correlations, trends, and outliers, while data
analysis validates and refines these findings. Together, they
help:
Improve the accuracy of fraud detection models.
