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Fraud-detection model started passing a class of fraudulent transactions after retraining.
The nightly fraud model retrains on feedback-labeled data. Over two weeks, chargebacks rose 22% while the model's reported precision stayed flat. Investigation suggests mislabeled "legitimate" samples were injected into the feedback loop.
index=ml_metrics model=fraud_v* | timechart span=1d avg(precision) avg(recall) | join chargeback_rate | where chargeback_rate > 1.2 * baselinedetection queryindex=ml_metrics model=fraud_v* | timechart span=1d avg(precision) avg(recall) | join chargeback_rate | where chargeback_rate > 1.2 * baseline