Published: Jul 2026
Fraud detection is usually an imbalanced classification problem. Accuracy can look strong, but business impact depends more on precision and recall.
Precision answers: among alerts raised, how many are truly fraud?
$$ \text{Precision} = \frac{TP}{TP + FP} $$Recall answers: among all real fraud events, how many were detected?
$$ \text{Recall} = \frac{TP}{TP + FN} $$To balance both, teams often track the harmonic mean:
$$ F_1 = 2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}} $$If recall is too low, fraud escapes. If precision is too low, analysts get overloaded. A good threshold is the point where risk capture and investigation capacity are both sustainable.