In this series, we explore the possibility to use classical supervised learning techniques to classify alarms generated by a system that implements the Market Abuse Regulation MAR. If successful, this machine learning application could help Compliance Officers to reduce the effort spent in the analysis and classification of the alarms generated.

In this second post of the series, we introduce: the dataset used for the classification problem, the ML approach that better manages the high-imbalanced dataset and finally the statistical metrics used to measure the goodness of the results.

In this last post we finally present if the Random Forest trained on the past activity of the compliance officer is able to classify an alarm as false positive or not.