FOR3705
Assignment 1
Semester 1
Due March 2026
, FOR3705 ASSIGNMENT 1 2026 SEMESTER 1 2026
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.
Answer
Definition of Data Mining (2 marks) Data mining is the process of systematically
examining large datasets to identify patterns, relationships, trends, or anomalies that
are not immediately obvious. It applies statistical, mathematical, and algorithmic
techniques to extract meaningful information from complex data (NetSuite, 2025).
Use of Data Mining Tools in Fraud Detection (4 marks)
1. Pattern Recognition Data mining tools can group similar transactions or
behaviours to detect irregularities. For example, clustering methods can identify
unusual spending patterns in credit card transactions, highlighting potential
fraudulent activity (EuroMaTech, 2025).
2. Anomaly Detection Software can flag data points that deviate from expected
norms. For instance, an unusual volume of late-night withdrawals from a
corporate account may indicate fraud (EuroMaTech, 2025).
3. Classification and Predictive Modelling By learning from historical fraud data,
algorithms can classify new transactions as high or low risk. Decision trees or
machine learning models can predict potential fraudulent activity before it
escalates (EuroMaTech, 2025).
4. Relationship Mapping Tools can link multiple data points, such as account
numbers or IP addresses, to uncover hidden connections that may indicate
collusion or structured fraud schemes (Scribd, 2025).
Assignment 1
Semester 1
Due March 2026
, FOR3705 ASSIGNMENT 1 2026 SEMESTER 1 2026
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.
Answer
Definition of Data Mining (2 marks) Data mining is the process of systematically
examining large datasets to identify patterns, relationships, trends, or anomalies that
are not immediately obvious. It applies statistical, mathematical, and algorithmic
techniques to extract meaningful information from complex data (NetSuite, 2025).
Use of Data Mining Tools in Fraud Detection (4 marks)
1. Pattern Recognition Data mining tools can group similar transactions or
behaviours to detect irregularities. For example, clustering methods can identify
unusual spending patterns in credit card transactions, highlighting potential
fraudulent activity (EuroMaTech, 2025).
2. Anomaly Detection Software can flag data points that deviate from expected
norms. For instance, an unusual volume of late-night withdrawals from a
corporate account may indicate fraud (EuroMaTech, 2025).
3. Classification and Predictive Modelling By learning from historical fraud data,
algorithms can classify new transactions as high or low risk. Decision trees or
machine learning models can predict potential fraudulent activity before it
escalates (EuroMaTech, 2025).
4. Relationship Mapping Tools can link multiple data points, such as account
numbers or IP addresses, to uncover hidden connections that may indicate
collusion or structured fraud schemes (Scribd, 2025).
