In a banking context, how can predictive analytics be used to detect potential fraudulent transactions?
- Anomaly Detection
- Clustering
- Decision Trees
- Linear Regression
Anomaly Detection is an effective method for detecting potential fraudulent transactions in a banking context. This approach identifies deviations from normal patterns, helping to flag transactions that exhibit unusual behavior. Clustering, Linear Regression, and Decision Trees are valuable for other types of predictions but may not be as effective in capturing the anomalous patterns associated with fraud.
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