Below-the-line thresholds tuning with machine learning

Abstract

Systems, methods, and other embodiments for ML-Based automated below-the-line threshold tuning include, in one embodiment, training an ML model to predict probabilities that an event is fraudulent on a set of events (i) sampled from a set of historic events labeled by an alerting engine as either above-the-line events or below-the-line events on either side of a threshold line indicating that an event is suspicious, and (ii) confirmed to be either fraudulent or not fraudulent; determining that the alerting engine should be tuned based on differences between probability values predicted for the events by the trained machine learning model and the labels applied to the events; generating a tuned threshold value for the threshold line based at least in part on the probability values predicted by the machine learning model; and tuning the alerting engine by replacing a threshold value with the tuned threshold value to adjust the threshold line.