Implementing only anomaly detection algorithms to identify transactions that deviate from normal patterns, focusing solely on unusual transaction amounts.

Applying supervised learning algorithms to classify transactions as fraudulent or legitimate based on historical data, without considering the relationships between different transaction features.

Utilizing association rule mining to uncover relationships between transaction features and fraudulent activity, ignoring the classification of transactions based on historical data.

Combining anomaly detection algorithms, supervised learning algorithms, and association rule mining to provide a holistic view of potential fraudulent transactions by identifying deviations, classifying based on history, and understanding feature relationships.