FOR3705 Assignment 1
(COMPLETE ANSWERS)
Semester 1 2026 - DUE
March 2026
,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] Here’s a detailed answer to your question, structured clearly for
marks allocation:
1.1 Analyse the relationship between data mining and data
analysis in financial crime investigation (5 marks)
Data mining is the process of extracting hidden patterns, trends, and relationships
from large datasets using algorithms and statistical methods. In financial crime
investigations, it can uncover suspicious transactions, unusual patterns, or links
between entities that may indicate fraud, money laundering, or insider trading.
Data analysis is the systematic examination and interpretation of data to draw
meaningful insights. It involves cleaning, organizing, and summarizing data to
support decision-making.
Relationship:
Data mining is often the first step, identifying patterns or anomalies in
financial data.
Data analysis then interprets and validates these patterns to assess the
likelihood of financial crime.
They are interdependent: data mining generates raw findings; data analysis
contextualizes them to produce actionable insights.
Example: Data mining may flag a series of unusually large transactions across
accounts, and data analysis evaluates whether these transactions are consistent with
normal business activity or indicative of money laundering.
, 1.2 Assess how these two processes complement each other
in detecting financial crimes (5 marks)
Enhanced Detection: Data mining uncovers hidden patterns, while data
analysis evaluates their significance, together increasing the chance of
identifying fraudulent activity.
Efficiency: Data mining automates pattern recognition in massive datasets;
data analysis adds human insight and judgment, ensuring accuracy.
Decision Support: Data mining highlights suspicious activity; data analysis
helps investigators prioritize cases and take appropriate action.
Predictive Insights: Combined, they allow predictive modeling to anticipate
potential financial crimes before they occur.
Fraud Prevention: Continuous mining and analysis help financial institutions
detect anomalies early and implement controls, reducing losses.
Example: A bank uses data mining to detect unusual clusters of transactions across
multiple accounts. Data analysis then examines transaction histories, customer
profiles, and external risk factors to confirm if these clusters indicate organized fraud.
Marking breakdown suggestion:
1.1 Analysis: 5 marks (relationship explained, examples, interdependence)
(COMPLETE ANSWERS)
Semester 1 2026 - DUE
March 2026
,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] Here’s a detailed answer to your question, structured clearly for
marks allocation:
1.1 Analyse the relationship between data mining and data
analysis in financial crime investigation (5 marks)
Data mining is the process of extracting hidden patterns, trends, and relationships
from large datasets using algorithms and statistical methods. In financial crime
investigations, it can uncover suspicious transactions, unusual patterns, or links
between entities that may indicate fraud, money laundering, or insider trading.
Data analysis is the systematic examination and interpretation of data to draw
meaningful insights. It involves cleaning, organizing, and summarizing data to
support decision-making.
Relationship:
Data mining is often the first step, identifying patterns or anomalies in
financial data.
Data analysis then interprets and validates these patterns to assess the
likelihood of financial crime.
They are interdependent: data mining generates raw findings; data analysis
contextualizes them to produce actionable insights.
Example: Data mining may flag a series of unusually large transactions across
accounts, and data analysis evaluates whether these transactions are consistent with
normal business activity or indicative of money laundering.
, 1.2 Assess how these two processes complement each other
in detecting financial crimes (5 marks)
Enhanced Detection: Data mining uncovers hidden patterns, while data
analysis evaluates their significance, together increasing the chance of
identifying fraudulent activity.
Efficiency: Data mining automates pattern recognition in massive datasets;
data analysis adds human insight and judgment, ensuring accuracy.
Decision Support: Data mining highlights suspicious activity; data analysis
helps investigators prioritize cases and take appropriate action.
Predictive Insights: Combined, they allow predictive modeling to anticipate
potential financial crimes before they occur.
Fraud Prevention: Continuous mining and analysis help financial institutions
detect anomalies early and implement controls, reducing losses.
Example: A bank uses data mining to detect unusual clusters of transactions across
multiple accounts. Data analysis then examines transaction histories, customer
profiles, and external risk factors to confirm if these clusters indicate organized fraud.
Marking breakdown suggestion:
1.1 Analysis: 5 marks (relationship explained, examples, interdependence)
