The monitoring model started flagging normal transactions as suspicious (false positives). What will you do?

If the monitoring model starts frequently flagging normal transactions as suspicious (false positives), the following steps should be taken:

1. Data analysis: check if the data the model is working on has changed (e.g., new user behavior patterns).
2. Model retraining: the model may be outdated and require updating with new data.
3. Threshold adjustment: modify the model's trigger threshold to reduce false alarms.
4. Adding new features: improve the model's quality by adding relevant features that help better distinguish normal transactions from suspicious ones.
5. Ensemble methods: combine multiple models to improve accuracy.
6. Monitoring and feedback: implement a feedback collection system from analysts or users to refine the model.

Ultimately, it is important not only to reduce false positives but also to maintain the model's sensitivity to real threats.