, QUESTION 1
Data mining refers to the process of extracting useful, previously unknown, and meaningful
patterns from large volumes of data using statistical, mathematical, and computational techniques.
It involves analysing large datasets to discover relationships, trends, anomalies, or patterns that
can support decision-making (Han, Kamber & Pei, 2012). In the context of fraud detection, data
mining tools are used to analyse transactional and behavioural data to identify unusual patterns
that may indicate fraudulent activity.
Techniques such as classification, clustering, association rule mining, and anomaly detection help
organisations compare normal behaviour with suspicious deviations, such as irregular transaction
amounts, unusual frequencies, or inconsistent user behaviour (Witten et al., 2016). By
automatically scanning large datasets that would be impossible to analyse manually, data mining
tools enable early detection of potential fraud, reduce financial losses, and support proactive risk
management through continuous monitoring and predictive analysis (Ngai et al., 2011).
Data mining refers to the process of extracting useful, previously unknown, and meaningful
patterns from large volumes of data using statistical, mathematical, and computational techniques.
It involves analysing large datasets to discover relationships, trends, anomalies, or patterns that
can support decision-making (Han, Kamber & Pei, 2012). In the context of fraud detection, data
mining tools are used to analyse transactional and behavioural data to identify unusual patterns
that may indicate fraudulent activity.
Techniques such as classification, clustering, association rule mining, and anomaly detection help
organisations compare normal behaviour with suspicious deviations, such as irregular transaction
amounts, unusual frequencies, or inconsistent user behaviour (Witten et al., 2016). By
automatically scanning large datasets that would be impossible to analyse manually, data mining
tools enable early detection of potential fraud, reduce financial losses, and support proactive risk
management through continuous monitoring and predictive analysis (Ngai et al., 2011).
