FOR3705
Assignment 1
Semester 1
Due March 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: Data mining is the process of systematically examining
large datasets to discover patterns, correlations, anomalies, or trends that are not
immediately apparent. It involves extracting meaningful information from raw data to
support decision-making, prediction, or investigative purposes (Han, Kamber & Pei,
2012). In the context of forensic and financial investigations, data mining focuses on
uncovering unusual patterns or irregularities that may indicate fraudulent activity.
Use of Data Mining Tools in Fraud Detection: Data mining tools help investigators
analyse vast amounts of transactional and financial data efficiently. These tools apply
statistical, computational, and algorithmic techniques to identify irregularities that might
suggest fraud. Key ways data mining tools assist include:
1. Pattern Recognition:
o Tools can detect recurring patterns that align with known fraudulent
schemes.
o Example: If multiple employees submit similar expense claims with
identical minor errors, a pattern emerges indicating potential collusion or
falsification.
2. Anomaly Detection:
o Data mining can flag transactions that deviate significantly from normal
behaviour.
o Example: A sudden, unexplained transfer of large sums from a low-risk
account can be automatically highlighted for further review.
, 3. Predictive Analytics:
o Historical fraud data is used to train models that predict the likelihood of
fraudulent behaviour in new transactions.
o Example: Machine learning algorithms can score each transaction based
on fraud risk, prioritising high-risk cases for investigators.
4. Clustering and Segmentation:
o Data mining groups similar records together, making it easier to spot
outliers.
o Example: A cluster of vendors receiving unusually frequent payments
compared to others in the same category may indicate a shell company or
kickback scheme.
Conclusion: In summary, data mining transforms large, complex datasets into
actionable insights. By identifying patterns, anomalies, and suspicious relationships, it
provides investigators with a systematic approach to detect and investigate fraud
effectively. Without such tools, manually detecting subtle indicators in millions of records
would be nearly impossible.
References:
• Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques.
3rd Edition. Morgan Kaufmann.
• Boland, B., & Tanyel, F. (2014). Fraud Analytics Using Descriptive, Predictive,
and Social Network Techniques: A Guide to Data Science for Fraud Detection.
Wiley.
Assignment 1
Semester 1
Due March 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: Data mining is the process of systematically examining
large datasets to discover patterns, correlations, anomalies, or trends that are not
immediately apparent. It involves extracting meaningful information from raw data to
support decision-making, prediction, or investigative purposes (Han, Kamber & Pei,
2012). In the context of forensic and financial investigations, data mining focuses on
uncovering unusual patterns or irregularities that may indicate fraudulent activity.
Use of Data Mining Tools in Fraud Detection: Data mining tools help investigators
analyse vast amounts of transactional and financial data efficiently. These tools apply
statistical, computational, and algorithmic techniques to identify irregularities that might
suggest fraud. Key ways data mining tools assist include:
1. Pattern Recognition:
o Tools can detect recurring patterns that align with known fraudulent
schemes.
o Example: If multiple employees submit similar expense claims with
identical minor errors, a pattern emerges indicating potential collusion or
falsification.
2. Anomaly Detection:
o Data mining can flag transactions that deviate significantly from normal
behaviour.
o Example: A sudden, unexplained transfer of large sums from a low-risk
account can be automatically highlighted for further review.
, 3. Predictive Analytics:
o Historical fraud data is used to train models that predict the likelihood of
fraudulent behaviour in new transactions.
o Example: Machine learning algorithms can score each transaction based
on fraud risk, prioritising high-risk cases for investigators.
4. Clustering and Segmentation:
o Data mining groups similar records together, making it easier to spot
outliers.
o Example: A cluster of vendors receiving unusually frequent payments
compared to others in the same category may indicate a shell company or
kickback scheme.
Conclusion: In summary, data mining transforms large, complex datasets into
actionable insights. By identifying patterns, anomalies, and suspicious relationships, it
provides investigators with a systematic approach to detect and investigate fraud
effectively. Without such tools, manually detecting subtle indicators in millions of records
would be nearly impossible.
References:
• Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques.
3rd Edition. Morgan Kaufmann.
• Boland, B., & Tanyel, F. (2014). Fraud Analytics Using Descriptive, Predictive,
and Social Network Techniques: A Guide to Data Science for Fraud Detection.
Wiley.
